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San Jose State University
1.
Desai, Khushali Yashodhar.
Big Data Quality Modeling And Validation.
Degree: MS, Computer Engineering, 2018, San Jose State University
URL: https://scholarworks.sjsu.edu/etd_theses/4898 
► The chief purpose of this study is to characterize various big data quality models and to validate each with an example. As the volume…
(more)
▼ The chief purpose of this study is to characterize various big data quality models and to validate each with an example. As the volume of data is increasing at an exponential speed in the era of the broadband Internet, the success of a product or decision largely depends upon selecting the highest quality raw materials, or data, to be used in production. However, working with data in high volumes, fast velocities, and various formats can be fraught with problems. Therefore, software industries need a quality check, especially for data being generated by either software or a sensor. This study explores various big data quality parameters and their definitions and proposes a quality model for each parameter. By using data from the Water Quality U. S. Geological Survey (USGS), San Francisco Bay, an example for each of the proposed big data quality models is given. To calculate composite data quality, prevalent methods such as Monte Carlo and neural networks were used. This thesis proposes eight big data quality parameters in total. Six out of eight of those models were coded and made into a final year project by a group of Master’s degree students at SJSU. A case study is carried out using linear regression analysis, and all the big data quality parameters are validated with positive results.
Subjects/Keywords: Big Data; Big Data Quality
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APA (6th Edition):
Desai, K. Y. (2018). Big Data Quality Modeling And Validation. (Masters Thesis). San Jose State University. Retrieved from https://scholarworks.sjsu.edu/etd_theses/4898
Chicago Manual of Style (16th Edition):
Desai, Khushali Yashodhar. “Big Data Quality Modeling And Validation.” 2018. Masters Thesis, San Jose State University. Accessed June 25, 2019.
https://scholarworks.sjsu.edu/etd_theses/4898.
MLA Handbook (7th Edition):
Desai, Khushali Yashodhar. “Big Data Quality Modeling And Validation.” 2018. Web. 25 Jun 2019.
Vancouver:
Desai KY. Big Data Quality Modeling And Validation. [Internet] [Masters thesis]. San Jose State University; 2018. [cited 2019 Jun 25].
Available from: https://scholarworks.sjsu.edu/etd_theses/4898.
Council of Science Editors:
Desai KY. Big Data Quality Modeling And Validation. [Masters Thesis]. San Jose State University; 2018. Available from: https://scholarworks.sjsu.edu/etd_theses/4898
University of Georgia
2.
Cheng, Xiao.
The rise of the Big Data: why should statisticians embrace collaborations with computer scientists.
Degree: MS, Statistics, 2013, University of Georgia
URL: http://purl.galileo.usg.edu/uga_etd/cheng_xiao_201312_ms
► Big Data has been the new trend in businesses. As technology advances, the ability to collect large amount of data and learn insights from them…
(more)
▼ Big Data has been the new trend in businesses. As technology advances, the ability to collect large amount of
data and learn insights from them became more important. We will analyze a dataset provided by the Heritage Provider Network. The goal is to predict the number of days a patient stays in the hospital based on previous year’s insurance claim records. The tools used include logistic regression, Classification and Regression Trees, and Gradient Boosting Machine (GBM). We will compare two techniques of analyzing the
data. The first method will use GBM to predict the outcome variable. While in the second method, we will use a sequential modeling method, where we partition the dataset into risky and non-risky groups, and then use GBM to predict the outcome for the risky patients and assign no-stay for the non-risky group. We also discuss
Big Data topics such as More
Data Beats Better Algorithm.
Advisors/Committee Members: Jeongyoun Ahn.
Subjects/Keywords: Big Data
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APA (6th Edition):
Cheng, X. (2013). The rise of the Big Data: why should statisticians embrace collaborations with computer scientists. (Masters Thesis). University of Georgia. Retrieved from http://purl.galileo.usg.edu/uga_etd/cheng_xiao_201312_ms
Chicago Manual of Style (16th Edition):
Cheng, Xiao. “The rise of the Big Data: why should statisticians embrace collaborations with computer scientists.” 2013. Masters Thesis, University of Georgia. Accessed June 25, 2019.
http://purl.galileo.usg.edu/uga_etd/cheng_xiao_201312_ms.
MLA Handbook (7th Edition):
Cheng, Xiao. “The rise of the Big Data: why should statisticians embrace collaborations with computer scientists.” 2013. Web. 25 Jun 2019.
Vancouver:
Cheng X. The rise of the Big Data: why should statisticians embrace collaborations with computer scientists. [Internet] [Masters thesis]. University of Georgia; 2013. [cited 2019 Jun 25].
Available from: http://purl.galileo.usg.edu/uga_etd/cheng_xiao_201312_ms.
Council of Science Editors:
Cheng X. The rise of the Big Data: why should statisticians embrace collaborations with computer scientists. [Masters Thesis]. University of Georgia; 2013. Available from: http://purl.galileo.usg.edu/uga_etd/cheng_xiao_201312_ms
Wake Forest University
3.
Bowling, Roy Nathaniel.
Big Data and the Rhetorical Narrative.
Degree: 2014, Wake Forest University
URL: http://hdl.handle.net/10339/47448
► This project effectively illustrates a tactic by which the constructs of narrative inquiry from a humanist perspective, in particular the rhetorical narrative tradition, can migrate…
(more)
▼ This project effectively illustrates a tactic by which the constructs of narrative inquiry from a humanist perspective, in particular the rhetorical narrative tradition, can migrate into a larger methodology while simultaneously recognizing and training agents in narrative visualization via up-to-date computational tools. The Reddit platform in particular, served as a suitable illustration for a multifaceted approach to novel methods in narrative inquiry due to the free accessibility to online storytelling in addition to a substantial collection of unstructured data it offers. This permitted an effective exploration through means of analyzing mediated narratives while concurrently using computational methods to assemble, filter, and interpret "Reddit narratives." The project progresses in two parts. First I offer a model for contemporary rhetorical narrative analysis that embraces social media as a viable source of user-generated narrative data. The second half of the project illustrates a data analysis template that employs a rhetorical lens for the creation of narrative maps. Collectively this project proposes a model for continued rhetorical narrative inquiry that intersects at traditional qualitative analysis and the contemporary deployment of textual analytic software.
Subjects/Keywords: Big Data
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APA (6th Edition):
Bowling, R. N. (2014). Big Data and the Rhetorical Narrative. (Thesis). Wake Forest University. Retrieved from http://hdl.handle.net/10339/47448
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Bowling, Roy Nathaniel. “Big Data and the Rhetorical Narrative.” 2014. Thesis, Wake Forest University. Accessed June 25, 2019.
http://hdl.handle.net/10339/47448.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Bowling, Roy Nathaniel. “Big Data and the Rhetorical Narrative.” 2014. Web. 25 Jun 2019.
Vancouver:
Bowling RN. Big Data and the Rhetorical Narrative. [Internet] [Thesis]. Wake Forest University; 2014. [cited 2019 Jun 25].
Available from: http://hdl.handle.net/10339/47448.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Bowling RN. Big Data and the Rhetorical Narrative. [Thesis]. Wake Forest University; 2014. Available from: http://hdl.handle.net/10339/47448
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
4.
Zgraggen, Emanuel Albert Errol.
Towards Accessible Data Analysis.
Degree: Department of Computer Science, 2018, Brown University
URL: https://repository.library.brown.edu/studio/item/bdr:792684/
► In today's world data is ubiquitous. Increasingly large and complex datasets are gathered across many domains. Data analysis - making sense of all this data…
(more)
▼ In today's world
data is ubiquitous. Increasingly
large and complex datasets are gathered across many domains.
Data
analysis - making sense of all this
data - is exploratory by
nature, demanding rapid iterations, and all but the simplest
analysis tasks require humans in the loop to effectively steer the
process. Current tools that support this process are built for an
elite set of individuals: highly trained analysts or
data
scientists who have strong mathematics and computer science skills.
This however presents a bottleneck. Qualified
data scientists are
scarce and expensive which makes it often unfeasible to inform
decisions with
data. How do we empower
data enthusiasts,
stakeholders or
subject matter experts, who are not statisticians
or programmers, to directly tease out insights from
data? This
thesis presents work towards making
data analysis more accessible.
We invent a set of user experiences with approachable visual
metaphors where building blocks are directly manipulatable and
incrementally composable to support common
data analysis tasks at
the pace that matches the thought process of a humans. First, we
develop a system for back-of-the-envelope calculations that
revolves around handwriting recognition - all
data is represented
as digital ink - and gestural commands. Second, we introduce a
novel pen & touch system for
data exploration and analysis
which is based on four core interaction concepts. The combination
and interplay between those concepts supports a wide range of
common analytical tasks. The interface allows for incremental and
piecewise query specification where intermediate visualizations
serve as feedback as well as interactive handles to adjust query
parameters. Third, we present a visual query interface for event
sequence
data. This touch-based interface exposes the full
expressive power of regular expressions in an approachable way and
interleaves query specification with result visualizations. Fourth,
we present the results of an experiment where we analyze how
progressive visualizations affect exploratory analysis. Based on
these results, which suggest that progressive visualizations are a
viable solution to achieve scalability in
data exploration systems,
we develop a system entirely based on progressive computation that
allows users to interactively build complex analytics workflows.
And finally, we discuss and experimentally show that using visual
analysis tools might inflate false discovery rates among
user-extracted insights and suggest ways of ameliorating this
problem.
Advisors/Committee Members: van Dam, Andries (Advisor), Kraska, Tim (Reader), Drucker, Steven M. (Reader).
Subjects/Keywords: Big data
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APA ·
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Manager
APA (6th Edition):
Zgraggen, E. A. E. (2018). Towards Accessible Data Analysis. (Thesis). Brown University. Retrieved from https://repository.library.brown.edu/studio/item/bdr:792684/
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Zgraggen, Emanuel Albert Errol. “Towards Accessible Data Analysis.” 2018. Thesis, Brown University. Accessed June 25, 2019.
https://repository.library.brown.edu/studio/item/bdr:792684/.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Zgraggen, Emanuel Albert Errol. “Towards Accessible Data Analysis.” 2018. Web. 25 Jun 2019.
Vancouver:
Zgraggen EAE. Towards Accessible Data Analysis. [Internet] [Thesis]. Brown University; 2018. [cited 2019 Jun 25].
Available from: https://repository.library.brown.edu/studio/item/bdr:792684/.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Zgraggen EAE. Towards Accessible Data Analysis. [Thesis]. Brown University; 2018. Available from: https://repository.library.brown.edu/studio/item/bdr:792684/
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
University of KwaZulu-Natal
5.
Vela Vela, Junior.
The employees’ perception on the adoption of big data analytics by selected medical aid organisations in Durban.
Degree: 2017, University of KwaZulu-Natal
URL: http://hdl.handle.net/10413/15171
► The increase of number of data available in today’s world has prompted different industries to find a way to get the value out of the…
(more)
▼ The increase of number of
data available in today’s world has prompted different industries to find a way to get the value out of the
data available.
Big data analytics is a term used to describe the analysis of the enormous amount of
data. Therefore, practitioners and researchers are trying to understand the adoption of this new technology by companies, government, universities.
Big data analytics has been used by some medical aid companies to improve the quality of schemes and products provided to clients by collecting, analysing accurate
data. However, the rate of acceptance and use of
big data analytics by medical aids organisations in South Africa is still unknown. In this dissertation, we discuss the employees’ perceptions on the adoption of
big data analytics by medical aid organizations in Durban. The benefits and challenges of
big data analytics in medical aid organizations was also discussed.
A conceptual framework was developed to structure the problem being investigated in this dissertation. To this end, five perceived factors that might influence the employees’ perception on the adoption of
big data analytics were examined: - perceived performance expectancy, - perceive price value, - perceived social influence, - perceived facilitating conditions, - perceived characteristic of Innovation.
A survey research was used as a research strategy. An exploratory nature of the study was chosen. Thus, there is no conclusive outcomes in this dissertation. Results show that generally employees have a positive perception on the adoption of
big data analytics. Constructs such as perceived performance expectancy, perceived price value, and the perceived characteristics of innovation proved to be influencing the employees’ attitudes towards the adoption of
big data analytics.
Advisors/Committee Members: Subramaniam, Prabhakar Rontala. (advisor).
Subjects/Keywords: Big data analysis.; Technology.; Big data.; Adoption.
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APA (6th Edition):
Vela Vela, J. (2017). The employees’ perception on the adoption of big data analytics by selected medical aid organisations in Durban. (Thesis). University of KwaZulu-Natal. Retrieved from http://hdl.handle.net/10413/15171
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Vela Vela, Junior. “The employees’ perception on the adoption of big data analytics by selected medical aid organisations in Durban.” 2017. Thesis, University of KwaZulu-Natal. Accessed June 25, 2019.
http://hdl.handle.net/10413/15171.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Vela Vela, Junior. “The employees’ perception on the adoption of big data analytics by selected medical aid organisations in Durban.” 2017. Web. 25 Jun 2019.
Vancouver:
Vela Vela J. The employees’ perception on the adoption of big data analytics by selected medical aid organisations in Durban. [Internet] [Thesis]. University of KwaZulu-Natal; 2017. [cited 2019 Jun 25].
Available from: http://hdl.handle.net/10413/15171.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Vela Vela J. The employees’ perception on the adoption of big data analytics by selected medical aid organisations in Durban. [Thesis]. University of KwaZulu-Natal; 2017. Available from: http://hdl.handle.net/10413/15171
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Universidade do Minho
6.
Torres, Hugo Miguel Oliveira.
Benchmarking de tecnologias de Big Data aplicadas à saúde-medicina
.
Degree: 2017, Universidade do Minho
URL: http://hdl.handle.net/1822/54930
► Os avanços tecnológicos observados nas últimas décadas levaram a um aumento no volume e variedade dos dados gerados. Esses dados, quando armazenados, processados e analisados,…
(more)
▼ Os avanços tecnológicos observados nas últimas décadas levaram a um aumento no volume e variedade
dos dados gerados. Esses dados, quando armazenados, processados e analisados, podem fornecer novo
conhecimento e uma maior perceção do negócio, o que pode ajudar as organizações a obter vantagem
sobre os seus concorrentes. Está provado que
Big Data está relacionado com um aumento na eficiência
e eficácia em várias áreas. Embora muitos estudos tenham sido realizados com o intuito de provar o
valor do
Big Data na saúde/medicina, não existem muitos avanços efetuados na prática.
Pretende-se facilitar a adoção de tecnologias
Big Data na medicina e em organizações ligadas à saúde.
Vai ser discutido o potencial e os desafios na adoção de
Big Data, comparando várias tecnologias
Big
Data (Benchmarking) que foram utilizadas ou projetadas para ser aplicadas na área da saúde.
Nesta dissertação foi realizada uma análise às tecnologias
Big Data já existentes e aplicadas na área da
saúde. Foi analisada uma solução
Big Data desenvolvida para o projeto INTCare, uma solução baseada
em Hadoop proposta para o hospital Maharaja Yeshwatrao situado na Índia e também uma solução que
utiliza o Apache Spark. As três soluções mencionadas assentam em tecnologias open source. A solução
IBM PureData Solution For Healthcare Analytics utilizada no Seattle’s Children’s Hospital e a solução
Cisco Connected Health Solutions and Services fazem parte das soluções proprietárias analisadas.
O inicio deste documento apresenta uma descrição sucinta do contexto do projeto, qual a motivação e o
objetivo deste trabalho. Posteriormente é apresentado o Estado da Arte onde são explicados os vários
tópicos relacionados com o que foi feito e estudado. De seguida são apresentados os objetivos e as
abordagens metodológicas utlizadas e de que forma foram aplicadas no desenvolvimento desta
dissertação. Posteriormente são apresentadas várias ferramentas que integram as soluções encontradas.
Na secção seguinte são apresentadas as várias experiências que comparam as soluções escolhidas para
o benchmarking. Por fim, é apresentada a discussão dos resultados obtidos e sugestões que podem ser
adotadas para reduzir o risco de adoção de tecnologias
Big Data na área da saúde.
Advisors/Committee Members: Santos, Manuel (advisor), Portela, Filipe (advisor).
Subjects/Keywords: Big data;
Big data technologies;
Big data in healthcare;
Benchmarking
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APA (6th Edition):
Torres, H. M. O. (2017). Benchmarking de tecnologias de Big Data aplicadas à saúde-medicina
. (Masters Thesis). Universidade do Minho. Retrieved from http://hdl.handle.net/1822/54930
Chicago Manual of Style (16th Edition):
Torres, Hugo Miguel Oliveira. “Benchmarking de tecnologias de Big Data aplicadas à saúde-medicina
.” 2017. Masters Thesis, Universidade do Minho. Accessed June 25, 2019.
http://hdl.handle.net/1822/54930.
MLA Handbook (7th Edition):
Torres, Hugo Miguel Oliveira. “Benchmarking de tecnologias de Big Data aplicadas à saúde-medicina
.” 2017. Web. 25 Jun 2019.
Vancouver:
Torres HMO. Benchmarking de tecnologias de Big Data aplicadas à saúde-medicina
. [Internet] [Masters thesis]. Universidade do Minho; 2017. [cited 2019 Jun 25].
Available from: http://hdl.handle.net/1822/54930.
Council of Science Editors:
Torres HMO. Benchmarking de tecnologias de Big Data aplicadas à saúde-medicina
. [Masters Thesis]. Universidade do Minho; 2017. Available from: http://hdl.handle.net/1822/54930
Delft University of Technology
7.
Verheij, B.A.
The process of big data solution adoption:.
Degree: 2013, Delft University of Technology
URL: http://resolver.tudelft.nl/uuid:369986f0-dd24-4855-aa85-8f69a3325191
► This research concerned the process of big data solution adoption and the main issues that firms experience in this process. Using eight cases within the…
(more)
▼ This research concerned the process of
big data solution adoption and the main issues that firms experience in this process. Using eight cases within the Dutch telecommunication and energy utility sectors, a conceptual model was constructed of issues in the process of
big data solution adoption. Twelve main issues were identified in the process of
big data solution adoption which were discussed and led to a number of research implications.
Advisors/Committee Members: Rook, L., Van den Berg, J., Van Beers, C..
Subjects/Keywords: big data; big data solution; NoSQL; big data solution adoption
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APA ·
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to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Verheij, B. A. (2013). The process of big data solution adoption:. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:369986f0-dd24-4855-aa85-8f69a3325191
Chicago Manual of Style (16th Edition):
Verheij, B A. “The process of big data solution adoption:.” 2013. Masters Thesis, Delft University of Technology. Accessed June 25, 2019.
http://resolver.tudelft.nl/uuid:369986f0-dd24-4855-aa85-8f69a3325191.
MLA Handbook (7th Edition):
Verheij, B A. “The process of big data solution adoption:.” 2013. Web. 25 Jun 2019.
Vancouver:
Verheij BA. The process of big data solution adoption:. [Internet] [Masters thesis]. Delft University of Technology; 2013. [cited 2019 Jun 25].
Available from: http://resolver.tudelft.nl/uuid:369986f0-dd24-4855-aa85-8f69a3325191.
Council of Science Editors:
Verheij BA. The process of big data solution adoption:. [Masters Thesis]. Delft University of Technology; 2013. Available from: http://resolver.tudelft.nl/uuid:369986f0-dd24-4855-aa85-8f69a3325191
University of Hawaii – Manoa
8.
Kang, Qiuling.
Sentiment analysis of big social data with Apache Hadoop.
Degree: 2015, University of Hawaii – Manoa
URL: http://hdl.handle.net/10125/101227
► M.S. University of Hawaii at Manoa 2014.
Twitter is a microblog service and is a very popular communication mechanism. Users of Twitter express their interests,…
(more)
▼ M.S. University of Hawaii at Manoa 2014.
Twitter is a microblog service and is a very popular communication mechanism. Users of Twitter express their interests, favorites, and sentiments towards various topics and issues they encountered in daily life, therefore, Twitter is an important online platform for people to express their opinions which is a key fact to influence their behaviors. Thus, sentiment analysis for Twitter data is meaningful for both individuals and organizations to make decisions. Due to the huge amount of data generated by Twitter every day, a system which can store and process big data is becoming a problem. In this study, we present a method to collect Twitter data sets, and store and analyze the data sets on Hadoop platform. The experiment results prove that the present method performs efficient.
Subjects/Keywords: Twitter data sets; big data
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Manager
APA (6th Edition):
Kang, Q. (2015). Sentiment analysis of big social data with Apache Hadoop. (Thesis). University of Hawaii – Manoa. Retrieved from http://hdl.handle.net/10125/101227
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Kang, Qiuling. “Sentiment analysis of big social data with Apache Hadoop.” 2015. Thesis, University of Hawaii – Manoa. Accessed June 25, 2019.
http://hdl.handle.net/10125/101227.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Kang, Qiuling. “Sentiment analysis of big social data with Apache Hadoop.” 2015. Web. 25 Jun 2019.
Vancouver:
Kang Q. Sentiment analysis of big social data with Apache Hadoop. [Internet] [Thesis]. University of Hawaii – Manoa; 2015. [cited 2019 Jun 25].
Available from: http://hdl.handle.net/10125/101227.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Kang Q. Sentiment analysis of big social data with Apache Hadoop. [Thesis]. University of Hawaii – Manoa; 2015. Available from: http://hdl.handle.net/10125/101227
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Montana State University
9.
Ganesan Pillai, Karthik.
Mining spatiotemporal co-occurrence patterns from massive data sets with evolving regions.
Degree: College of Engineering, 2014, Montana State University
URL: https://scholarworks.montana.edu/xmlui/handle/1/9422
► Due to the current rates of data acquisition, the growth of data volumes in nearly all domains of our lives is reaching historic proportions [5],…
(more)
▼ Due to the current rates of
data acquisition, the growth of
data volumes in nearly all domains of our lives is reaching historic proportions [5], [6], [7]. Spatiotemporal
data mining has emerged in recent decades with the main goal focused on developing
data-driven mechanisms for the understanding of the spatiotemporal characteristics and patterns occurring in the massive repositories of
data. This work focuses on discovering spatiotemporal co-occurrence patterns (STCOPs) from large
data sets with evolving regions. Spatiotemporal co-occurrence patterns represent the subset of event types that occur together in both space and time. Major limitations of existing spatiotemporal
data mining models and techniques include the following. First, they do not take into account continuously evolving spatiotemporal events that have polygon-like representations. Second, they do not investigate and provide sufficient interest measures for the STCOPs discovery purposes. Third, computationally and storage efficient algorithms to discover STCOPs are missing. These limitations of existing approaches represent important hurdles while analyzing massive spatiotemporal
data sets in several application domains that generate
big data, including solar physics, which is an application of our interdisciplinary research. In this work, we address these limitations by i) introducing the problem of mining STCOPs from
data sets with extended (region-based) spatial representations that evolve over time, ii) developing a set of novel interest measures, and iii) providing a novel framework to model STCOPs. We also present and investigate three novel approaches to STCOPs mining. We follow this investigation by applying our algorithm to perform a novel
data-driven discovery of STCOPs from solar physics
data.
Advisors/Committee Members: Chairperson, Graduate Committee: John Paxton (advisor).
Subjects/Keywords: Data mining.; Big data.
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APA (6th Edition):
Ganesan Pillai, K. (2014). Mining spatiotemporal co-occurrence patterns from massive data sets with evolving regions. (Thesis). Montana State University. Retrieved from https://scholarworks.montana.edu/xmlui/handle/1/9422
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Ganesan Pillai, Karthik. “Mining spatiotemporal co-occurrence patterns from massive data sets with evolving regions.” 2014. Thesis, Montana State University. Accessed June 25, 2019.
https://scholarworks.montana.edu/xmlui/handle/1/9422.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Ganesan Pillai, Karthik. “Mining spatiotemporal co-occurrence patterns from massive data sets with evolving regions.” 2014. Web. 25 Jun 2019.
Vancouver:
Ganesan Pillai K. Mining spatiotemporal co-occurrence patterns from massive data sets with evolving regions. [Internet] [Thesis]. Montana State University; 2014. [cited 2019 Jun 25].
Available from: https://scholarworks.montana.edu/xmlui/handle/1/9422.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Ganesan Pillai K. Mining spatiotemporal co-occurrence patterns from massive data sets with evolving regions. [Thesis]. Montana State University; 2014. Available from: https://scholarworks.montana.edu/xmlui/handle/1/9422
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
University of Saskatchewan
10.
Turland, Madeline G 1993-.
Farmers’ willingness to participate in a big data sharing program: A study of Saskatchewan grain farmers.
Degree: 2018, University of Saskatchewan
URL: http://hdl.handle.net/10388/11053
► Big data in crop agriculture is information collected by sophisticated machinery at the farm level, as well as externally generated data, such as field satellite…
(more)
▼ Big data in crop agriculture is information collected by sophisticated machinery at the farm level, as well as externally generated
data, such as field satellite imagery. Although some of this
data is useful to individual farmers, much of it has little value to the farmer that collects it. Capturing the true value of
big data comes when it is aggregated over many farms, allowing researchers to find underlying bio-physical and economical relationships.
We conduct a hypothetical choice experiment to analyze farmers’ willingness to share
data by asking farmers in Saskatchewan whether they would participate in a
big data sharing program. The choice tasks varied the type of organization that operated the
big data program and included financial and non-financial incentives.
Heteroscedastic and random effects probit models are presented using the
data from the survey. The results are consistent across models and find that farmers are most willing to share their
data with university researchers, followed by crop input suppliers or grower associations, and financial institutions or equipment manufacturers. Farmers are least willing to share their
data with government. Farmers are more willing to share
data in the presence of a financial incentive or non-financial incentive such as comparative benchmark statistics or prescription maps generated from the
data submitted.
Checks for robustness and heterogeneity indicate there is no self-selection bias into the survey, and no heterogeneity in the results for financial incentive and farm revenue. A latent class logit model determines the farmer population may be heterogenous in their willingness to participate in a
big data sharing program, but homogenous in their ordering of preferences for organization, financial incentive, and non-financial incentive. In addition, demographic variables are not related to class membership.
Advisors/Committee Members: Slade, Peter, Micheels, Eric, Smyth, Stuart, Gray, Richard, Skolrud, Tristan, McDonald, Jill.
Subjects/Keywords: big data; data sharing
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APA (6th Edition):
Turland, M. G. 1. (2018). Farmers’ willingness to participate in a big data sharing program: A study of Saskatchewan grain farmers. (Thesis). University of Saskatchewan. Retrieved from http://hdl.handle.net/10388/11053
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Turland, Madeline G 1993-. “Farmers’ willingness to participate in a big data sharing program: A study of Saskatchewan grain farmers.” 2018. Thesis, University of Saskatchewan. Accessed June 25, 2019.
http://hdl.handle.net/10388/11053.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Turland, Madeline G 1993-. “Farmers’ willingness to participate in a big data sharing program: A study of Saskatchewan grain farmers.” 2018. Web. 25 Jun 2019.
Vancouver:
Turland MG1. Farmers’ willingness to participate in a big data sharing program: A study of Saskatchewan grain farmers. [Internet] [Thesis]. University of Saskatchewan; 2018. [cited 2019 Jun 25].
Available from: http://hdl.handle.net/10388/11053.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Turland MG1. Farmers’ willingness to participate in a big data sharing program: A study of Saskatchewan grain farmers. [Thesis]. University of Saskatchewan; 2018. Available from: http://hdl.handle.net/10388/11053
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Tampere University
11.
Syrjärinne, Paula.
Urban Traffic Analysis with Bus Location Data
.
Degree: 2016, Tampere University
URL: http://tampub.uta.fi/handle/10024/98613
► Tässä työssä esitellään Tampereen alueen julkisen liikenteen linja-autoista kerätyn datan käyttöä ja analysointia. Aineistoa on analysoitu useilla eri algoritmeilla ja monesta eri näkökulmasta. Osa analyyseista…
(more)
▼ Tässä työssä esitellään Tampereen alueen julkisen liikenteen linja-autoista kerätyn datan käyttöä ja analysointia. Aineistoa on analysoitu useilla eri algoritmeilla ja monesta eri näkökulmasta. Osa analyyseista mittaa julkisen liikenteen palvelutasoa, osa tarjoaa matkustajille hyödyllistä lisäinformaatiota ja osa keskittyy havainnoimaan liikenteen yleistä sujuvuutta.
Työn alussa esitellään aiheeseen liittyviä taustatietoja ja aiemmin samasta aiheesta tehtyjä tutkimuksia. Erilaiset liikenteeseen liittyvät sensoriverkostot käydään läpi, keskittyen erityisesti sensoriautoverkostoihin. Kauttaaltaan työssä käsitellään Tampereen linja-autodataa liikkuvasta autosensoriverkosta kerättynä datana. Sensoriautoverkostojen analyysiin liittyvää kirjallisuutta esitellään työssä siten että tutkimukset on jaoteltu lähdedatan perusteella taksidataa, linja-autodataa ja mobiililaitedataa käsitteleviin artikkeleihin. Kuhunkin näistä liittyy erilaisia tutkimusongelmia.
Taksidataa käytettäessä puuttuvat havaintopisteet ovat yleisin ongelma, kun taas henkilöautoliikenteen mallintaminen linja-autoista kerätyn datan perusteella on tyypillinen kysymys bussidataa käyttävissä tutkimuksissa. Mobiililaitteista kerättyä dataa käytettäessä pitää sen sijaan yleensä ensin selvittää onko laite ylipäätään liikkuvassa ajoneuvossa.
Tampereen linja-autodata esitellään yksityiskohtaisesti. Tämä data on verrattain hyvälaatuista, koska sen päivitysnopeus on korkea, jokaiseen havaintoon on aina liittetty yksilölliset tunnisteet ja koko julkisen liikenteen verkoston alueelta on runsaasti havaintoja saatavilla. Kuten missä tahansa oikeasta lähteestä kerätyssä datassa, tässäkin aineistossa on kuitenkin ongelmia, kuten epäjohdonmukaisuuksia, virheitä ja kohinaa. Näiden virheiden odotettavissa olevat suuruusluokat on käyty datan esittelyssä läpi. Samoin esittelellään esikäsittelyprosessi, jossa dataa sekä puhdistetaan virheistä että sen kokoa ja muotoa muutetaan helpommin käytettäväksi tilastollisessa analyysissä.
Työn kokeellisessa osassa tarkastellaan aluksi datan käyttöä julkisen liikenteen toimivuuden mittaamisessa. Datasta on etsitty usein esiintyviä aika-paikka-linja –joukkoja, jotka paljastavat missä, milloin ja millä linjoilla bussit ovat säännöllisesti myöhässä. Sen lisäksi reittiajoja on jaoteltu paikan ja tapahtumien (kuten pysäkillä käynnit tai liikennevaloissa odottaminen) mukaan, jotta on löydetty syitä myöhästymisille.
Matkustajien kannalta tehdyissä kokeiluissa on toteutettu mm. dataan perustuvat pysäkkiaikataulut, jotka mukautuvat ajan mittaan todellisten saapumisaikojen mukaan. Saapumisajan lisäksi matkustajille annetaan arvio saapumisajan epävarmuudesta.
Yleisen liikenteen sujuvuuden analysoimiseksi esitellään katuosuusprofiilien käsite. Profiili kertoo kullekin pysäkinvälille normaalin ajoajan rajat kunakin vuorokaudenaikana. Profiileja voidaan käyttää pysäkinvälien luokitteluun esimerkiksi aamu- ja iltapäiväruuhkan vaikutusten mukaan, ja ne ovat perusta reaaliaikaisen poikkeustilamonitoroinnin tarpeisiin.…
Subjects/Keywords: data-analyysi;
big data;
älyliikenne;
data analysis;
big data;
ITS
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APA ·
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Export
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Manager
APA (6th Edition):
Syrjärinne, P. (2016). Urban Traffic Analysis with Bus Location Data
. (Doctoral Dissertation). Tampere University. Retrieved from http://tampub.uta.fi/handle/10024/98613
Chicago Manual of Style (16th Edition):
Syrjärinne, Paula. “Urban Traffic Analysis with Bus Location Data
.” 2016. Doctoral Dissertation, Tampere University. Accessed June 25, 2019.
http://tampub.uta.fi/handle/10024/98613.
MLA Handbook (7th Edition):
Syrjärinne, Paula. “Urban Traffic Analysis with Bus Location Data
.” 2016. Web. 25 Jun 2019.
Vancouver:
Syrjärinne P. Urban Traffic Analysis with Bus Location Data
. [Internet] [Doctoral dissertation]. Tampere University; 2016. [cited 2019 Jun 25].
Available from: http://tampub.uta.fi/handle/10024/98613.
Council of Science Editors:
Syrjärinne P. Urban Traffic Analysis with Bus Location Data
. [Doctoral Dissertation]. Tampere University; 2016. Available from: http://tampub.uta.fi/handle/10024/98613
Indiana University
12.
Suriarachchi, Isuru.
Big provenance stream processing for data-intensive computations
.
Degree: 2018, Indiana University
URL: http://hdl.handle.net/2022/22579
► Industry, academia, and research alike are grappling with the opportunities that Big Data brings in the ability to analyze data from numerous sources for insight,…
(more)
▼ Industry, academia, and research alike are grappling with the opportunities that
Big Data brings in the ability to analyze
data from numerous sources for insight, decision making, and predictive forecasts. The analysis workflows for dealing with such volumes of
data are said to be large scale
data-intensive computations (DICs).
Data-intensive computation frameworks, also known as
Big Data processing frameworks, carry out both online and offline processing.
Big Data analysis workflows frequently consist of multiple steps:
data cleaning, joining
data from different sources and applying processing algorithms. Critically today the steps of a given workflow may be performed with different processing frameworks simultaneously, complicating the lifecycle of the
data products that go through the workflow. This is particularly the case in emerging
Big Data management solutions like
Data Lakes in which
data from multiple sources are stored in a shared storage solution and analyzed for different purposes at different points of time. In such an environment, accessibility and traceability of
data products are known to be hard to achieve.
Data provenance, or
data lineage, leads to a good solution for this problem as it provides the derivation history of a
data product and helps in monitoring, debugging and reproducing computations. Our initial research produced a provenance-based reference architecture and a prototype implementation to achieve better traceability and management. Experiments show that the size of fine-grained provenance collected from
data-intensive computations can be several times larger than the original
data itself, creating a
Big Data problem referred to in the literature “
Big Provenance”. Storing and managing
Big Provenance for later analysis is not be feasible for some
data-intensive applications due to high resource consumption. In addition to that, not all provenance is equally valuable and can be summarized without loss of critical information. In this thesis, I apply stream processing techniques to analyze streams of provenance captured from
data-intensive computations. The specific contributions are several. First, a provenance model which includes formal definitions for provenance stream, forward provenance and backward provenance in the context of
data-intensive computations. Second, a stateful, one-pass, parallel stream processing algorithm to summarize a full provenance stream on-the-fly by preserving backward provenance and forward provenance. The algorithm is resilient to provenance events arriving out-of-order. Multiple provenance stream partitioning strategies: horizontal, vertical, and random for provenance emerging from
data-intensive computations are also presented. A provenance stream processing architecture is developed to apply the proposed parallel streaming algorithm on a stream of provenance arriving through a distributed log store. The solution is evaluated using Apache Kafka log store, Apache Flink stream processing system, and the Komadu provenance capture service. Provenance…
Advisors/Committee Members: Plale, Beth (advisor).
Subjects/Keywords: Big Data;
Big Provenance;
Stream Processing
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APA ·
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Export
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APA (6th Edition):
Suriarachchi, I. (2018). Big provenance stream processing for data-intensive computations
. (Thesis). Indiana University. Retrieved from http://hdl.handle.net/2022/22579
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Suriarachchi, Isuru. “Big provenance stream processing for data-intensive computations
.” 2018. Thesis, Indiana University. Accessed June 25, 2019.
http://hdl.handle.net/2022/22579.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Suriarachchi, Isuru. “Big provenance stream processing for data-intensive computations
.” 2018. Web. 25 Jun 2019.
Vancouver:
Suriarachchi I. Big provenance stream processing for data-intensive computations
. [Internet] [Thesis]. Indiana University; 2018. [cited 2019 Jun 25].
Available from: http://hdl.handle.net/2022/22579.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Suriarachchi I. Big provenance stream processing for data-intensive computations
. [Thesis]. Indiana University; 2018. Available from: http://hdl.handle.net/2022/22579
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
University of Victoria
13.
Chrimes, Dillon.
Towards a big data analytics platform with Hadoop/MapReduce framework using simulated patient data of a hospital system.
Degree: School of Health Information Science, 2016, University of Victoria
URL: http://hdl.handle.net/1828/7645
► Background: Big data analytics (BDA) is important to reduce healthcare costs. However, there are many challenges. The study objective was high performance establishment of interactive…
(more)
▼ Background:
Big data analytics (BDA) is important to reduce healthcare costs. However, there are many challenges. The study objective was high performance establishment of interactive BDA platform of hospital system.
Methods: A Hadoop/MapReduce framework formed the BDA platform with HBase (NoSQL database) using hospital-specific metadata and file ingestion. Query performance tested with Apache tools in Hadoop’s ecosystem.
Results: At optimized iteration, Hadoop distributed file system (HDFS) ingestion required three seconds but HBase required four to twelve hours to complete the Reducer of MapReduce. HBase bulkloads took a week for one billion (10TB) and over two months for three billion (30TB). Simple and complex query results showed about two seconds for one and three billion, respectively.
Interpretations: BDA platform of HBase distributed by Hadoop successfully under high performance at large volumes representing the Province’s entire
data. Inconsistencies of MapReduce limited operational efficiencies. Importance of the Hadoop/MapReduce on representation of health informatics is further discussed.
Advisors/Committee Members: Kuo, Alex (Mu - Hsing) (supervisor).
Subjects/Keywords: Big Data; Big Data Analytics; Big Data Tools; Big Data Visualizations; Hadoop Ecosystem; Health Big Data; Hospital Systems; Interactive Big Data; Patient Data; Simulations
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APA (6th Edition):
Chrimes, D. (2016). Towards a big data analytics platform with Hadoop/MapReduce framework using simulated patient data of a hospital system. (Masters Thesis). University of Victoria. Retrieved from http://hdl.handle.net/1828/7645
Chicago Manual of Style (16th Edition):
Chrimes, Dillon. “Towards a big data analytics platform with Hadoop/MapReduce framework using simulated patient data of a hospital system.” 2016. Masters Thesis, University of Victoria. Accessed June 25, 2019.
http://hdl.handle.net/1828/7645.
MLA Handbook (7th Edition):
Chrimes, Dillon. “Towards a big data analytics platform with Hadoop/MapReduce framework using simulated patient data of a hospital system.” 2016. Web. 25 Jun 2019.
Vancouver:
Chrimes D. Towards a big data analytics platform with Hadoop/MapReduce framework using simulated patient data of a hospital system. [Internet] [Masters thesis]. University of Victoria; 2016. [cited 2019 Jun 25].
Available from: http://hdl.handle.net/1828/7645.
Council of Science Editors:
Chrimes D. Towards a big data analytics platform with Hadoop/MapReduce framework using simulated patient data of a hospital system. [Masters Thesis]. University of Victoria; 2016. Available from: http://hdl.handle.net/1828/7645
Georgia Tech
14.
Lee, Kisung.
Scalable big data systems: Architectures and optimizations.
Degree: PhD, Computer Science, 2015, Georgia Tech
URL: http://hdl.handle.net/1853/55520
► Big data analytics has become not just a popular buzzword but also a strategic direction in information technology for many enterprises and government organizations. Even…
(more)
▼ Big data analytics has become not just a popular buzzword but also a strategic direction in information technology for many enterprises and government organizations. Even though many new computing and storage systems have been developed for
big data analytics, scalable
big data processing has become more and more challenging as a result of the huge and rapidly growing size of real-world
data. Dedicated to the development of architectures and optimization techniques for scaling
big data processing systems, especially in the era of cloud computing, this dissertation makes three unique contributions. First, it introduces a suite of graph partitioning algorithms that can run much faster than existing
data distribution methods and inherently scale to the growth of
big data. The main idea of these approaches is to partition a
big graph by preserving the core computational
data structure as much as possible to maximize intra-server computation and minimize inter-server communication. In addition, it proposes a distributed iterative graph computation framework that effectively utilizes secondary storage to maximize access locality and speed up distributed iterative graph computations. The framework not only considerably reduces memory requirements for iterative graph algorithms but also significantly improves the performance of iterative graph computations. Last but not the least, it establishes a suite of optimization techniques for scalable spatial
data processing along with three orthogonal dimensions: (i) scalable processing of spatial alarms for mobile users traveling on road networks, (ii) scalable location tagging for improving the quality of Twitter
data analytics and prediction accuracy, and (iii) lightweight spatial indexing for enhancing the performance of
big spatial
data queries.
Advisors/Committee Members: Liu, Ling (advisor), Omiecinski, Ed (committee member), Pu, Calton (committee member), Schwan, Karsten (committee member), Ramaswamy, Lakshmish (committee member).
Subjects/Keywords: Big data; Graph data; Spatial data
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APA ·
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APA (6th Edition):
Lee, K. (2015). Scalable big data systems: Architectures and optimizations. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/55520
Chicago Manual of Style (16th Edition):
Lee, Kisung. “Scalable big data systems: Architectures and optimizations.” 2015. Doctoral Dissertation, Georgia Tech. Accessed June 25, 2019.
http://hdl.handle.net/1853/55520.
MLA Handbook (7th Edition):
Lee, Kisung. “Scalable big data systems: Architectures and optimizations.” 2015. Web. 25 Jun 2019.
Vancouver:
Lee K. Scalable big data systems: Architectures and optimizations. [Internet] [Doctoral dissertation]. Georgia Tech; 2015. [cited 2019 Jun 25].
Available from: http://hdl.handle.net/1853/55520.
Council of Science Editors:
Lee K. Scalable big data systems: Architectures and optimizations. [Doctoral Dissertation]. Georgia Tech; 2015. Available from: http://hdl.handle.net/1853/55520
15.
Cao, Xiang.
Efficient Data Management and Processing in Big Data Applications.
Degree: PhD, Computer Science, 2017, University of Minnesota
URL: http://hdl.handle.net/11299/188863
► In today's Big Data applications, huge amount of data are being generated. With the rapid growth of data amount, data management and processing become essential.…
(more)
▼ In today's Big Data applications, huge amount of data are being generated. With the rapid growth of data amount, data management and processing become essential. It is important to design efficient approaches to manage and process data. In this thesis, data management and processing are investigated for Big Data applications. Key-value store (KVS) is widely used in many Big Data applications by providing flexible and efficient performance. Recently, a new Ethernet accessed disk drive for key-value pairs called "Kinetic Drive" was developed by Seagate. It can reduce the management complexity, especially in large-scale deployment. It is important to manage the key-value pairs and store them in Kinetic Drives in an organized way. In this thesis, we present data allocation schemes on a large-scale key-value store system using Kinetic Drives. We investigate key indexing schemes and allocate data on drives accordingly. We propose efficient approaches to migrate data among drives. Also, it is necessary to manage huge amount of key-value pairs to provide attributes search for users. In this thesis, we design a large-scale searchable key-value store system based on Kinetic Drives. We investigate an indexing scheme to map data to the drives. We propose a key generation approach to reflect metadata information of the actual data and support users' attributes search requests. Nowadays, MapReduce has become a very popular framework to process data in many applications. Data shuffling usually accounts for a large portion of the entire running time of MapReduce jobs. In recent years, scale-up computing architecture for MapReduce jobs has been developed. With multi-processor, multi-core design connected via NUMAlink and large shared memories, NUMA architecture provides a powerful scale-up computing capability. In this thesis, we focus on the optimization of data shuffling phase in MapReduce framework in NUMA machine. We concentrate on the various bandwidth capacities of NUMAlink(s) among different memory locations to fully utilize the network. We investigate the NUMAlink topology and propose a topology-aware reducer placement algorithm to speed up the data shuffling phase. We extend our approach to a larger computing environment with multiple NUMA machines.
Subjects/Keywords: Big Data; Data Management; Data Processing
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APA ·
Chicago ·
MLA ·
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Export
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Manager
APA (6th Edition):
Cao, X. (2017). Efficient Data Management and Processing in Big Data Applications. (Doctoral Dissertation). University of Minnesota. Retrieved from http://hdl.handle.net/11299/188863
Chicago Manual of Style (16th Edition):
Cao, Xiang. “Efficient Data Management and Processing in Big Data Applications.” 2017. Doctoral Dissertation, University of Minnesota. Accessed June 25, 2019.
http://hdl.handle.net/11299/188863.
MLA Handbook (7th Edition):
Cao, Xiang. “Efficient Data Management and Processing in Big Data Applications.” 2017. Web. 25 Jun 2019.
Vancouver:
Cao X. Efficient Data Management and Processing in Big Data Applications. [Internet] [Doctoral dissertation]. University of Minnesota; 2017. [cited 2019 Jun 25].
Available from: http://hdl.handle.net/11299/188863.
Council of Science Editors:
Cao X. Efficient Data Management and Processing in Big Data Applications. [Doctoral Dissertation]. University of Minnesota; 2017. Available from: http://hdl.handle.net/11299/188863
University of California – Riverside
16.
Jacobs, Steven.
A BAD Thesis: The Vision, Creation, and Evaluation of a Big Active Data Platform.
Degree: Computer Science, 2018, University of California – Riverside
URL: http://www.escholarship.org/uc/item/47g680h1
► Virtually all of today's Big Data systems are passive in nature, responding to queries posted by their users. Instead, this thesis aims to shift Big…
(more)
▼ Virtually all of today's Big Data systems are passive in nature, responding to queries posted by their users. Instead, this thesis aims to shift Big Data platforms from passive to active. A Big Active Data (BAD) system should continuously and reliably capture Big Data while enabling timely and automatic delivery of relevant information to a large pool of interested users, as well as supporting retrospective analyses of historical information. While various scalable streaming query engines have been created, their active behavior is limited to a (relatively) small window of the incoming data.To this end this thesis presents a BAD platform, that combines ideas and capabilities from both Big Data and Active Data (e.g., Publish/Subscribe, Streaming Engines). It supports complex subscriptions that consider not only newly arrived items but also their relationships to past, stored data. Further, it can provide actionable notifications by enriching the subscription results with other useful data. The platform extends an existing open-source Big Data Management System, Apache AsterixDB, with an it{active toolkit}. The toolkit contains features to rapidly ingest semistructured data, share execution pipelines among users, manage scaled user data subscriptions, and actively monitor the state of the data to produce individualized information for each user.This thesis describes the features and designs of the current BAD system and demonstrates its ability to scale without sacrificing query capability or individualization. One part of the BAD platform, the \emph{Data Feed}, relies on storage mechanisms that allow for fast ingestion, namely the Log-Structured Merge-Tree (LSM-Tree). As such, this thesis also presents work on a formal evaluation and performance comparison of theoretical and existing LSM Merge policies for fast ingestion.
Subjects/Keywords: Computer science; Active Data; Big Active Data; Big Data
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APA ·
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Export
to Zotero / EndNote / Reference
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APA (6th Edition):
Jacobs, S. (2018). A BAD Thesis: The Vision, Creation, and Evaluation of a Big Active Data Platform. (Thesis). University of California – Riverside. Retrieved from http://www.escholarship.org/uc/item/47g680h1
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Jacobs, Steven. “A BAD Thesis: The Vision, Creation, and Evaluation of a Big Active Data Platform.” 2018. Thesis, University of California – Riverside. Accessed June 25, 2019.
http://www.escholarship.org/uc/item/47g680h1.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Jacobs, Steven. “A BAD Thesis: The Vision, Creation, and Evaluation of a Big Active Data Platform.” 2018. Web. 25 Jun 2019.
Vancouver:
Jacobs S. A BAD Thesis: The Vision, Creation, and Evaluation of a Big Active Data Platform. [Internet] [Thesis]. University of California – Riverside; 2018. [cited 2019 Jun 25].
Available from: http://www.escholarship.org/uc/item/47g680h1.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Jacobs S. A BAD Thesis: The Vision, Creation, and Evaluation of a Big Active Data Platform. [Thesis]. University of California – Riverside; 2018. Available from: http://www.escholarship.org/uc/item/47g680h1
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Brunel University
17.
Lakoju, Mike.
A strategic approach of value identification for a big data project.
Degree: PhD, 2017, Brunel University
URL: http://bura.brunel.ac.uk/handle/2438/15837
;
https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.764867
► The disruptive nature of innovations and technological advancements present potentially huge benefits, however, it is critical to take caution because they also come with challenges.…
(more)
▼ The disruptive nature of innovations and technological advancements present potentially huge benefits, however, it is critical to take caution because they also come with challenges. This author holds fast to the school of thought which suggests that every organisation or society should properly evaluate innovations and their attendant challenges from a strategic perspective, before adopting them, or else could get blindsided by the after effects. Big Data is one of such innovations, currently trending within industry and academia. The instinctive nature of Organizations compels them to constantly find new ways to stay ahead of the competition. It is for this reason, that some incoherencies exist in the field of big data. While on the one hand, we have some Organizations rushing into implementing Big Data Projects, we also have in possibly equal measure, many other organisations that remain sceptical and uncertain of the benefits of "Big Data" in general and are also concerned with the implementation costs. What this has done is, create a huge focus on the area of Big Data Implementation. Literature reveals a good number of challenges around Big Data project implementations. For example, most Big Data projects are either abandoned or do not hit their expected target. Unfortunately, most IS literature has focused on implementation methodologies that are primarily focused on the data, resources, Big Data infrastructures, algorithms etc. Rather than leaving the incoherent space that exists to remain, this research seeks to collapse the space and open opportunities to harness and expand knowledge. Consequently, the research takes a slightly different standpoint by approaching Big Data implementation from a Strategic Perspective. The author emphasises the fact that focus should be shifted from going straight into implementing Big Data projects to first implementing a Big Data Strategy for the Organization. Before implementation, this strategy step will create the value proposition and identify deliverables to justify the project. To this end, the researcher combines an Alignment theory, with Digital Business Strategy theory to create a Big Data Strategy Framework that Organisations could use to align their business strategy with the Big Data project. The Framework was tested in two case studies, and the study resulted in the generation of the strategic Big Data Goals for both case studies. This Big Data Strategy framework aided the organisation in identifying the potential value that could be obtained from their Big Data project. These Strategic Big Data Goals can now be implemented in Big data Projects.
Subjects/Keywords: Big data strategy; Savi-bigd; Big data; Digital business strategy; Big data framework
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APA (6th Edition):
Lakoju, M. (2017). A strategic approach of value identification for a big data project. (Doctoral Dissertation). Brunel University. Retrieved from http://bura.brunel.ac.uk/handle/2438/15837 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.764867
Chicago Manual of Style (16th Edition):
Lakoju, Mike. “A strategic approach of value identification for a big data project.” 2017. Doctoral Dissertation, Brunel University. Accessed June 25, 2019.
http://bura.brunel.ac.uk/handle/2438/15837 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.764867.
MLA Handbook (7th Edition):
Lakoju, Mike. “A strategic approach of value identification for a big data project.” 2017. Web. 25 Jun 2019.
Vancouver:
Lakoju M. A strategic approach of value identification for a big data project. [Internet] [Doctoral dissertation]. Brunel University; 2017. [cited 2019 Jun 25].
Available from: http://bura.brunel.ac.uk/handle/2438/15837 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.764867.
Council of Science Editors:
Lakoju M. A strategic approach of value identification for a big data project. [Doctoral Dissertation]. Brunel University; 2017. Available from: http://bura.brunel.ac.uk/handle/2438/15837 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.764867
University of Technology, Sydney
18.
Zhang, X.
Toward scalable and cost-effective privacy-preserving big data publishing in cloud computing.
Degree: 2014, University of Technology, Sydney
URL: http://hdl.handle.net/10453/30324
► Big data and cloud computing are two disruptive trends nowadays, provisioning numerous opportunities to current IT industry and research communities while posing significant challenges on…
(more)
▼ Big data and cloud computing are two disruptive trends nowadays, provisioning numerous opportunities to current IT industry and research communities while posing significant challenges on them as well. The massive increase in computing power and data storage capacity provisioned by the cloud and the advances in big data mining and analytics have expanded the scope of information available to businesses, government, and individuals by orders of magnitude. A major obstacle to the adoption of cloud computing in sectors such as health and business for big data analysis is the privacy risk associated with releasing data sets to third-parties in the cloud. The data sets in the sectors mentioned above often contain personal privacy-sensitive data, e.g., electronic health records and financial transaction records, while these data sets can offer significant economic and social benefits if analysed or mined by organizations such as disease research centres. Although some privacy issues are not new, the situation is aggravated due to the features of cloud computing like ubiquitous access and multi-tenancy, and the three V properties of big data, i.e., Volume, Velocity and Variety. Therefore, it is still a significant challenge to achieve privacy-preserving big data publishing in cloud computing. A widely-adopted technique for privacy-preserving data publishing with semantic correctness guarantees is to anonymise data via generalisation, and a bundle of anonymisation approaches have been proposed. However, most existing approaches are either inherently sequential or distributed without directly optimising scalability, thus rendering them unsuitable for data intensive applications and inapplicable to the state-of-the-art parallel and distributed paradigms like MapReduce.
In this thesis, we mainly investigate the problem of big data anonymisation for privacy preservation from the perspectives of scalability and cost-effectiveness. The cloud computing advantages including on-demand resource provisioning, rapid elasticity and pay-as-you-go fashion are exploited to address the problem, aiming at gaining high scalability and cost-effectiveness. Specifically, we examine three major phases in the lifecycle of privacy-preserving data publishing or sharing in cloud environments, including data anonymisation, anonymous data update and anonymous data management. Accordingly, a scalable and cost-effective privacy-preserving framework is proposed to provide a holistic conceptual foundation for privacy preservation over big data and enable users to accomplish the full potential of the high scalability, elasticity, and cost-effectiveness of the cloud. We develop a corresponding prototype system consisting of a series of solutions to the scalability issues that lie in the three phases based on MapReduce, the de facto standard for big data processing paradigm at present, for the sake of high scalability, cost-effectiveness and compatibility with other big data mining and analytical tools. In terms of extensive experiments on real-world data…
Subjects/Keywords: Cloud computing.; Big data.; Privacy.
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APA (6th Edition):
Zhang, X. (2014). Toward scalable and cost-effective privacy-preserving big data publishing in cloud computing. (Thesis). University of Technology, Sydney. Retrieved from http://hdl.handle.net/10453/30324
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Zhang, X. “Toward scalable and cost-effective privacy-preserving big data publishing in cloud computing.” 2014. Thesis, University of Technology, Sydney. Accessed June 25, 2019.
http://hdl.handle.net/10453/30324.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Zhang, X. “Toward scalable and cost-effective privacy-preserving big data publishing in cloud computing.” 2014. Web. 25 Jun 2019.
Vancouver:
Zhang X. Toward scalable and cost-effective privacy-preserving big data publishing in cloud computing. [Internet] [Thesis]. University of Technology, Sydney; 2014. [cited 2019 Jun 25].
Available from: http://hdl.handle.net/10453/30324.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Zhang X. Toward scalable and cost-effective privacy-preserving big data publishing in cloud computing. [Thesis]. University of Technology, Sydney; 2014. Available from: http://hdl.handle.net/10453/30324
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Halmstad University
19.
Eriksson, Marcus.
Utmaningar i upphandlingsprocessen av Big datasystem : En kvalitativ studie om vilka utmaningar organisationer upplever vid upphandlingar av Big data-system.
Degree: Information Technology, 2017, Halmstad University
URL: http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-35802
► Organisationer har idag tillgång till stora mängder data som inte kan hanteras av traditionella Business Intelligence‐verktyg. Big data karakteriseras av stor volym, snabb hastighet…
(more)
▼ Organisationer har idag tillgång till stora mängder data som inte kan hanteras av traditionella Business Intelligence‐verktyg. Big data karakteriseras av stor volym, snabb hastighet och stor variation av data. För att hantera dessa karaktärer av data behöver organisationer upphandla ett Big data‐system för att ha möjlighet att utvinna värde. Många organisationer är medvetna om att investeringar i Big data kan bli lönsamma men vägen dit är otydlig. Studiens syfte är att undersöka vilka utmaningar organisationer står inför i samband med upphandling av ett Big data‐system och var i upphandlingsprocessen dessa utmaningar uppstår. Det empiriska materialet har samlats in från tre stora svenska företag och myndigheter som har upphandlat ett Big data‐ system. Analys av materialet har genomförts med Critical Incident Technique att identifiera utmaningar organisationer upplever i samband med upphandling av ett Big data‐system. I studiens resultat framgår det att organisationer upplever utmaningar med att förstå behovet av ett Big data‐system, skapa projektgruppen, välja projektmetod, skapa kravspecifikationen och hantera känslig och personlig data.
Organizations today have access to massive amounts of data that cannot be handled by traditional Business Intelligence tools. Big data is characterized by big volume, high velocity and variation. Organizations need to acquire a Big Data system, in order to handle the characteristics of the data and be able to generate business value. Today’s organizations are aware that investing in Big Data can be profitable but getting there is a challenge. The purpose of this study is to investigate the challenges the organizations may experience in the process of acquiring a Big Data system and when these challenges arise. The empirical data has been collected by interviewing three large Swedish companies and authorities which have acquired a Big Data system. The Critical Incident Technique has been used in order to identify the challenges which the organizations had experienced in the process of acquiring a Big Data system. The findings of the study shows that organizations experience challenges when they are understanding the need of the Big data‐system, creating the project team, choosing the project method, defining the requirements of the system and managing sensitive and personal data.
Subjects/Keywords: Big Data; Big Data strategy; Procurement; Acquisition; Challenges; Big data; Big data-strategi; Upphandlingsprocess; Utmaningar; Information Systems; Systemvetenskap, informationssystem och informatik
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APA ·
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MLA ·
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Manager
APA (6th Edition):
Eriksson, M. (2017). Utmaningar i upphandlingsprocessen av Big datasystem : En kvalitativ studie om vilka utmaningar organisationer upplever vid upphandlingar av Big data-system. (Thesis). Halmstad University. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-35802
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Eriksson, Marcus. “Utmaningar i upphandlingsprocessen av Big datasystem : En kvalitativ studie om vilka utmaningar organisationer upplever vid upphandlingar av Big data-system.” 2017. Thesis, Halmstad University. Accessed June 25, 2019.
http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-35802.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Eriksson, Marcus. “Utmaningar i upphandlingsprocessen av Big datasystem : En kvalitativ studie om vilka utmaningar organisationer upplever vid upphandlingar av Big data-system.” 2017. Web. 25 Jun 2019.
Vancouver:
Eriksson M. Utmaningar i upphandlingsprocessen av Big datasystem : En kvalitativ studie om vilka utmaningar organisationer upplever vid upphandlingar av Big data-system. [Internet] [Thesis]. Halmstad University; 2017. [cited 2019 Jun 25].
Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-35802.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Eriksson M. Utmaningar i upphandlingsprocessen av Big datasystem : En kvalitativ studie om vilka utmaningar organisationer upplever vid upphandlingar av Big data-system. [Thesis]. Halmstad University; 2017. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-35802
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Queens University
20.
Eftekhari, Azadeh.
BINARY: A FRAMEWORK FOR BIG DATA INTEGRATION FOR AD-HOC QUERYING
.
Degree: Computing, 2016, Queens University
URL: http://hdl.handle.net/1974/14234
► Traditional relational database systems are not practical for big data workloads that require scalable architectures for efficient data storage, manipulation, and analysis. Apache Hadoop, one…
(more)
▼ Traditional relational database systems are not practical for big data workloads that require scalable architectures for efficient data storage, manipulation, and analysis. Apache Hadoop, one of these big data frameworks, provides distributed storage and processing as well as a central repository for different types of data from different sources. Data integration from various sources is often required before performing analytics. Apache Hive on Hadoop is widely used for this purpose, as well as for data summarization and analysis. It has features such as a SQL-like query language, a Metastore to hold metadata and file formats to support access to various frameworks on Hadoop and beyond. For comprehensive analysis and decision-making, however, a hybrid system is required to integrate Hadoop with traditional relational database management systems in order to access the valuable data stored in relational databases. Current hybrid systems are either expensive proprietary products or require a system to be developed by the user, which requires programming knowledge. In addition these approaches are not sufficiently flexible to be applied to other frameworks.
In this thesis, we propose a framework called BINARY (A framework for Big data INtegration for Ad-hoc queRYing). BINARY is a hybrid Software as a Service that provides a web interface supported by a back-end infrastructure for ad-hoc querying, accessing, visualizing and joining data from different data sources, including Relational Database Management Systems and Apache Hive. Our framework uses scalable Hive and HDFS big data storage systems and supports different data sources via back-end resource adapters. There is also a front-end web interface that enables the use of HiveQL to query the data sources.
The framework is extendable and allows adding other storage engines (e.g. HBase) and analytics engines (e.g. R) as needed. We used REST software architecture to enable loose connections between the engines and the User Interface programs to facilitate independent updates without affecting the data infrastructure. Our approach is validated with a proof-of-concept prototype implemented on the OpenStack cloud system.
Subjects/Keywords: ad-hoc query;
big data
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APA ·
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APA (6th Edition):
Eftekhari, A. (2016). BINARY: A FRAMEWORK FOR BIG DATA INTEGRATION FOR AD-HOC QUERYING
. (Thesis). Queens University. Retrieved from http://hdl.handle.net/1974/14234
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Eftekhari, Azadeh. “BINARY: A FRAMEWORK FOR BIG DATA INTEGRATION FOR AD-HOC QUERYING
.” 2016. Thesis, Queens University. Accessed June 25, 2019.
http://hdl.handle.net/1974/14234.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Eftekhari, Azadeh. “BINARY: A FRAMEWORK FOR BIG DATA INTEGRATION FOR AD-HOC QUERYING
.” 2016. Web. 25 Jun 2019.
Vancouver:
Eftekhari A. BINARY: A FRAMEWORK FOR BIG DATA INTEGRATION FOR AD-HOC QUERYING
. [Internet] [Thesis]. Queens University; 2016. [cited 2019 Jun 25].
Available from: http://hdl.handle.net/1974/14234.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Eftekhari A. BINARY: A FRAMEWORK FOR BIG DATA INTEGRATION FOR AD-HOC QUERYING
. [Thesis]. Queens University; 2016. Available from: http://hdl.handle.net/1974/14234
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Universiteit Utrecht
21.
Franzke, A.S.
Big Data Ethicist - What will the role of the ethicist be in advising governments in the field of big data?.
Degree: 2016, Universiteit Utrecht
URL: http://dspace.library.uu.nl:8080/handle/1874/336098
► This paper elaborates on the question how the ethicist can address the demands for ethical expertise in governments occurring through big data practices. In the…
(more)
▼ This paper elaborates on the question how the ethicist can address the demands for ethical expertise in governments occurring through
big data practices. In the field of
big data, information about known ethical risks is needed. This role of providing information is more related to the role of the ethical consultant, but also discussion and open reflection is needed. Thus, a hybrid between ethical consultant and ethical facilitator will be the most beneficial way to increase a culture of engagement in those working with
big data. Such an approach is needed to keep reflection of how to use this technology on going. To avoid, fruitless discussion, three steps are presented. First the ethicist should gain some insight into the context, then distil the most relevant issues, address them and end with a clear recommendation how to proceed. The ethicist should be both open for discussion and solutions oriented.
Advisors/Committee Members: Anderson, Joel.
Subjects/Keywords: big data; ethic; e-government
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APA ·
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APA (6th Edition):
Franzke, A. S. (2016). Big Data Ethicist - What will the role of the ethicist be in advising governments in the field of big data?. (Masters Thesis). Universiteit Utrecht. Retrieved from http://dspace.library.uu.nl:8080/handle/1874/336098
Chicago Manual of Style (16th Edition):
Franzke, A S. “Big Data Ethicist - What will the role of the ethicist be in advising governments in the field of big data?.” 2016. Masters Thesis, Universiteit Utrecht. Accessed June 25, 2019.
http://dspace.library.uu.nl:8080/handle/1874/336098.
MLA Handbook (7th Edition):
Franzke, A S. “Big Data Ethicist - What will the role of the ethicist be in advising governments in the field of big data?.” 2016. Web. 25 Jun 2019.
Vancouver:
Franzke AS. Big Data Ethicist - What will the role of the ethicist be in advising governments in the field of big data?. [Internet] [Masters thesis]. Universiteit Utrecht; 2016. [cited 2019 Jun 25].
Available from: http://dspace.library.uu.nl:8080/handle/1874/336098.
Council of Science Editors:
Franzke AS. Big Data Ethicist - What will the role of the ethicist be in advising governments in the field of big data?. [Masters Thesis]. Universiteit Utrecht; 2016. Available from: http://dspace.library.uu.nl:8080/handle/1874/336098
Hong Kong University of Science and Technology
22.
Wang, Lu.
Techniques and applications of random sampling on massive data.
Degree: 2015, Hong Kong University of Science and Technology
URL: https://doi.org/10.14711/thesis-b1514598
;
http://repository.ust.hk/ir/bitstream/1783.1-78835/1/th_redirect.html
► Living in the era of big data, we often need to process and analyze data sets that have never been so large and fast-growing. Random…
(more)
▼ Living in the era of big data, we often need to process and analyze data sets that have never been so large and fast-growing. Random sampling has thus received much attention as an effective tool for turning big data “small”. It allows us to significantly reduce the size of input while maintaining the main features of the original data set we need. It is also easy to trade off between the computation complexity and the accuracy of the result, by tweaking the sample size. Although random sampling is a classical problem with a long history, it has received revived attention lately motivated by new applications as well as new constraints in the big data era. This thesis presents several new techniques and applications of random sampling: (1) a new randomized streaming algorithm for finding approximate quantiles in a data stream, which achieves the smallest space complexity of all such algorithms; (2) an augmented B-tree index that, for any given range query, returns a sampling-based summary containing the quantiles and heavy hitters of all tuples in the query range; (3) a sample-augmented R-tree that, given any range query, returns random samples from the query range in an online fashion. Apart from the description and analysis of each algorithm we propose, experimental results are also provided, confirming the advantages of the new algorithms. Finally, we showcase a system for large-scale spatio-temporal data analysis using the developed techniques.
Subjects/Keywords: Big data; Sampling (Statistics)
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APA ·
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Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Wang, L. (2015). Techniques and applications of random sampling on massive data. (Thesis). Hong Kong University of Science and Technology. Retrieved from https://doi.org/10.14711/thesis-b1514598 ; http://repository.ust.hk/ir/bitstream/1783.1-78835/1/th_redirect.html
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Wang, Lu. “Techniques and applications of random sampling on massive data.” 2015. Thesis, Hong Kong University of Science and Technology. Accessed June 25, 2019.
https://doi.org/10.14711/thesis-b1514598 ; http://repository.ust.hk/ir/bitstream/1783.1-78835/1/th_redirect.html.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Wang, Lu. “Techniques and applications of random sampling on massive data.” 2015. Web. 25 Jun 2019.
Vancouver:
Wang L. Techniques and applications of random sampling on massive data. [Internet] [Thesis]. Hong Kong University of Science and Technology; 2015. [cited 2019 Jun 25].
Available from: https://doi.org/10.14711/thesis-b1514598 ; http://repository.ust.hk/ir/bitstream/1783.1-78835/1/th_redirect.html.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Wang L. Techniques and applications of random sampling on massive data. [Thesis]. Hong Kong University of Science and Technology; 2015. Available from: https://doi.org/10.14711/thesis-b1514598 ; http://repository.ust.hk/ir/bitstream/1783.1-78835/1/th_redirect.html
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
23.
Loza Corera, María.
De los microdatos a los datos masivos. Cuestiones legales.
Degree: 2018, TDX
URL: http://hdl.handle.net/10803/571763
► Internet ha supuesto una revolución en todas las áreas de nuestra vida, la manera que tenemos de trabajar, de comunicarnos y relacionarnos con los demás,…
(more)
▼ Internet ha supuesto una revolución en todas las áreas de nuestra vida, la manera que tenemos de trabajar, de comunicarnos y relacionarnos con los demás, de comprar y vender, creando nuevos hábitos y costumbres, una nueva forma de vivir en definitiva. Conforme vamos avanzando en la Sociedad de la Información, más se pone de relieve el debate entre seguridad y privacidad, lo privado (intimidad) y lo público, lo que debe permanecer o “borrarse” de internet y es ahora cuando mayor importancia adquiere la normativa en materia de protección de datos de carácter personal, que se ve obligada a trazar unos límites en un mundo no físico, que no conoce de distancias, barreras ni tiempos. La evolución en el ámbito de la informática con las distintas generaciones de ordenadores junto con la irrupción de internet, ha desembocado en un proceso de datificación de nuestras vidas. Todo es medible, analizable, y por tanto, rastreable. Comenzamos a hablar de
big data cuando podemos manejar ingentes bases de datos y mediante los conocimientos necesarios, sabemos interpretar lo que dichos los datos nos pueden decir. Pero lógicamente, la tecnología no puede hacer sino mejorar, por lo que del mismo modo que ahora somos capaces de procesar bases de datos no relacionales, en un futuro probablemente podamos obtener información de la que ahora quizá no somos ni conscientes que podamos tener, es decir, tendremos mejores conocimientos y mejor tecnología para obtener nuevas informaciones de los datos, tal y como está ocurriendo ahora, en los inicios del
Big Data.
El tratamiento de datos masivos puede aportarnos grandes beneficios a todas las personas, a nivel global, pero también puede acarrear importantes consecuencias que repercutan negativamente en el individuo, tanto desde un punto de vista sociológico, pudiendo conllevar discriminación, despersonalización, y problemas éticos (en relación a un uso responsable del
big data), como a nivel de protección de datos de carácter personal.
Se analiza el concepto de tratamientos masivos de datos (
big data), y su diferente encuadre jurídico como un bien económico dentro de la denominada Economía de los datos, o desde la perspectiva del derecho fundamental de protección de datos. Se estudia también el nacimiento y evolución del derecho fundamental a la protección de datos, tanto a nivel europeo como en EEUU, con el fin de observar las diferencias entre ambos regímenes jurídicos, así como el marco jurídico normativo de protección de datos tanto a nivel español, europeo e internacional y en EEUU.
Se analizan los peligros que plantea y su incidencia respecto a la normativa de protección de datos de carácter personal, realizando un análisis crítico de la evolución de la normativa y el nivel de protección que ofrece con respecto a los nuevos retos que plantea la evolución de la Sociedad de la Información.
A nivel de protección de datos, los riesgos que se plantean son los derivados de una falta de transparencia y control, pues los datos son recogidos de las más diversas fuentes, por diferentes responsables,…
Advisors/Committee Members: Plaza Penadés, Javier, Departament de Dret Civil.
Subjects/Keywords: big data; protección de datos
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APA ·
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MLA ·
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Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Loza Corera, M. (2018). De los microdatos a los datos masivos. Cuestiones legales. (Thesis). TDX. Retrieved from http://hdl.handle.net/10803/571763
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Loza Corera, María. “De los microdatos a los datos masivos. Cuestiones legales.” 2018. Thesis, TDX. Accessed June 25, 2019.
http://hdl.handle.net/10803/571763.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Loza Corera, María. “De los microdatos a los datos masivos. Cuestiones legales.” 2018. Web. 25 Jun 2019.
Vancouver:
Loza Corera M. De los microdatos a los datos masivos. Cuestiones legales. [Internet] [Thesis]. TDX; 2018. [cited 2019 Jun 25].
Available from: http://hdl.handle.net/10803/571763.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Loza Corera M. De los microdatos a los datos masivos. Cuestiones legales. [Thesis]. TDX; 2018. Available from: http://hdl.handle.net/10803/571763
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Penn State University
24.
Iyer, Karthik Thyagarajan.
Computational complexity of data mining algorithms used in
fraud detection.
Degree: MS, Industrial Engineering, 2015, Penn State University
URL: https://etda.libraries.psu.edu/catalog/26437
► According to estimates by certain government agencies, 10% of the total medical expenditure is lost to healthcare fraud. Similarly the credit card industry loses billions…
(more)
▼ According to estimates by certain government agencies,
10% of the total medical expenditure is lost to healthcare fraud.
Similarly the credit card industry loses billions of dollars every
year due to fraudulent transactions. The datasets used to identify
these fraudulent transactions have millions of rows and each
transaction is defined by 15-40 attributes. It is not possible for
a human to sift through these massive datasets and find the
fraudulent transactions. Hence credit card companies and insurance
companies use data mining algorithms to identify fraudulent
transactions. These data mining algorithms need to identify the
fraudulent transactions efficiently and at the same time they need
to process the dataset as quickly as possible. The time taken by
the algorithm to execute is a function of the computational
complexity of the algorithm. In this thesis, the theoretical
running time complexities of the various data mining algorithms
used in fraud detection are compared, i.e. the complexity is
expressed as a function of the number of instances in the database.
These algorithms were then run on statistical tools like Weka and R
and on comparing the performance of these algorithms to the
theoretical computational efficiency it was found that, all
algorithms agree with the big O complexity. Support vector machines
and decision trees performed better than the big O
complexity.
Subjects/Keywords: Data mining; Complexity; Big O
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APA (6th Edition):
Iyer, K. T. (2015). Computational complexity of data mining algorithms used in
fraud detection. (Masters Thesis). Penn State University. Retrieved from https://etda.libraries.psu.edu/catalog/26437
Chicago Manual of Style (16th Edition):
Iyer, Karthik Thyagarajan. “Computational complexity of data mining algorithms used in
fraud detection.” 2015. Masters Thesis, Penn State University. Accessed June 25, 2019.
https://etda.libraries.psu.edu/catalog/26437.
MLA Handbook (7th Edition):
Iyer, Karthik Thyagarajan. “Computational complexity of data mining algorithms used in
fraud detection.” 2015. Web. 25 Jun 2019.
Vancouver:
Iyer KT. Computational complexity of data mining algorithms used in
fraud detection. [Internet] [Masters thesis]. Penn State University; 2015. [cited 2019 Jun 25].
Available from: https://etda.libraries.psu.edu/catalog/26437.
Council of Science Editors:
Iyer KT. Computational complexity of data mining algorithms used in
fraud detection. [Masters Thesis]. Penn State University; 2015. Available from: https://etda.libraries.psu.edu/catalog/26437
Princeton University
25.
Zhao, Tianqi.
Statistical Inference for Big Data
.
Degree: PhD, 2017, Princeton University
URL: http://arks.princeton.edu/ark:/88435/dsp017d278w62w
► This dissertation develops novel inferential methods and theory for assessing uncertainty of modern statistical procedures unique to big data analysis. In particular, we mainly focus…
(more)
▼ This dissertation develops novel inferential methods and theory for assessing uncertainty
of modern statistical procedures unique to
big data analysis. In particular,
we mainly focus on four challenging aspects of
big data: massive sample size, high
dimensionality, heterogeneity and complexity. To begin with, we consider a partially
linear framework for modeling massive heterogeneous
data. The major goal is to
extract common features across all sub-populations while exploring heterogeneity of
each sub-population. In particular, we propose an aggregation type estimator for the
commonality parameter that possesses the (non-asymptotic) minimax optimal bound
and asymptotic distribution as if there were no heterogeneity. This oracle result holds
when the number of sub-populations does not grow too fast.
The next problem focuses on the challenge of the high dimensionality. We propose
a robust inferential procedure for assessing uncertainties of parameter estimation in
high dimensional linear models, where the dimension p can grow exponentially fast
with the sample size n. We develop a new de-biasing framework tailored for nonsmooth
loss functions. Our framework enables us to exploit the composite quantile
function to construct a de-biased CQR estimator. This estimator is robust, and
preserves efficiency in the sense that the worst case efficiency loss is less than 30%
compared to square-loss-based procedures. In many cases our estimator is close to or
better than the latter.
Next, we consider the problem of high dimensional semiparametric generalized
linear models. We propose a new inferential framework which addresses a variety of
challenging problems in high dimensional
data analysis, including incomplete
data,
selection bias, and heterogeneity. First, we develop a regularized statistical chromatography
approach to infer the parameter of interest under the proposed semiparametric
generalized linear model without the need of estimating the unknown
base measure function. Then we propose a new likelihood ratio based framework
to construct post-regularization confidence regions and tests for the low dimensional
components of high dimensional parameters. We demonstrate the consequences of
the general theory by using examples of missing
data and multiple datasets inference.
Lastly, we study the rank likelihood as a powerful inferential tool in multivariate
analysis. The computation of the full rank likelihood function is often intractable
in large-scale datasets. Motivated by this, we resort to lower order rank approximations
and propose a new family of local rank likelihood functions. In particular, we
show that the maximizer of the second-order local rank likelihood coincides with the
Kendall's tau correlation matrix for the…
Advisors/Committee Members: Liu, Han (advisor).
Subjects/Keywords: Big Data;
Statistical Inference
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APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Zhao, T. (2017). Statistical Inference for Big Data
. (Doctoral Dissertation). Princeton University. Retrieved from http://arks.princeton.edu/ark:/88435/dsp017d278w62w
Chicago Manual of Style (16th Edition):
Zhao, Tianqi. “Statistical Inference for Big Data
.” 2017. Doctoral Dissertation, Princeton University. Accessed June 25, 2019.
http://arks.princeton.edu/ark:/88435/dsp017d278w62w.
MLA Handbook (7th Edition):
Zhao, Tianqi. “Statistical Inference for Big Data
.” 2017. Web. 25 Jun 2019.
Vancouver:
Zhao T. Statistical Inference for Big Data
. [Internet] [Doctoral dissertation]. Princeton University; 2017. [cited 2019 Jun 25].
Available from: http://arks.princeton.edu/ark:/88435/dsp017d278w62w.
Council of Science Editors:
Zhao T. Statistical Inference for Big Data
. [Doctoral Dissertation]. Princeton University; 2017. Available from: http://arks.princeton.edu/ark:/88435/dsp017d278w62w
26.
Lindström, Frej.
Impact analysis of characteristics in product development : Change in product property with respect to component generations.
Degree: Mathematics and Mathematical Statistics, 2017, Umeå University
URL: http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-136911
► Scania has developed a unique modular product system which is an important successfactor, creating exibility and lies at the heart of their business model.…
(more)
▼ Scania has developed a unique modular product system which is an important successfactor, creating exibility and lies at the heart of their business model. R&Duse product and vehicle product properties to describe the product key factors. Theseproduct properties are both used during the development of new features and products,and also utilized by the project oce to estimate the total contribution of a project.Scania want to develop a new method to understand and be able to track and comparethe projects eect over time and also predict future vehicle improvements. In this thesis, we investigate how to quantify the impact on vehicle product propertiesand predict component improvements, based on data sources that have not beenutilized for these purposes before. The impact objective is ultimately to increase the understandingof the development process of heavy vehicles and the aim for this projectwas to provide statistical methods that can be used for investigative and predictivepurposes. First, with analysis of variance we statistically veried and quantied differencesin a product property between comparable vehicle populations with respectto component generations. Then, Random Forest and Articial Neural Networks wereimplemented to predict future eect on product property with respect to componentimprovements. We could see a dierence of approximately 10 % between the comparablecomponents of interest, which was more than the expected dierence. Theexpectations are based on performance measurements from a test environment. Theimplemented Random Forest model was not able to predict future eect based on theseperformance measures. Articial Neural Networks was able to capture structures fromthe test environment and its predictive performance and reliability was, under the givencircumstances, relatively good.
Subjects/Keywords: Statistics; big data; Mathematics; Matematik
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APA ·
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CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Lindström, F. (2017). Impact analysis of characteristics in product development : Change in product property with respect to component generations. (Thesis). Umeå University. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-136911
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Lindström, Frej. “Impact analysis of characteristics in product development : Change in product property with respect to component generations.” 2017. Thesis, Umeå University. Accessed June 25, 2019.
http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-136911.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Lindström, Frej. “Impact analysis of characteristics in product development : Change in product property with respect to component generations.” 2017. Web. 25 Jun 2019.
Vancouver:
Lindström F. Impact analysis of characteristics in product development : Change in product property with respect to component generations. [Internet] [Thesis]. Umeå University; 2017. [cited 2019 Jun 25].
Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-136911.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Lindström F. Impact analysis of characteristics in product development : Change in product property with respect to component generations. [Thesis]. Umeå University; 2017. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-136911
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Universitat de Valencia
27.
Loza Corera, María.
De los microdatos a los datos masivos. Cuestiones legales
.
Degree: 2017, Universitat de Valencia
URL: http://hdl.handle.net/10550/61015
► Internet ha supuesto una revolución en todas las áreas de nuestra vida, la manera que tenemos de trabajar, de comunicarnos y relacionarnos con los demás,…
(more)
▼ Internet ha supuesto una revolución en todas las áreas de nuestra vida, la manera que tenemos de trabajar, de comunicarnos y relacionarnos con los demás, de comprar y vender, creando nuevos hábitos y costumbres, una nueva forma de vivir en definitiva. Conforme vamos avanzando en la Sociedad de la Información, más se pone de relieve el debate entre seguridad y privacidad, lo privado (intimidad) y lo público, lo que debe permanecer o “borrarse” de internet y es ahora cuando mayor importancia adquiere la normativa en materia de protección de datos de carácter personal, que se ve obligada a trazar unos límites en un mundo no físico, que no conoce de distancias, barreras ni tiempos. La evolución en el ámbito de la informática con las distintas generaciones de ordenadores junto con la irrupción de internet, ha desembocado en un proceso de datificación de nuestras vidas. Todo es medible, analizable, y por tanto, rastreable. Comenzamos a hablar de
big data cuando podemos manejar ingentes bases de datos y mediante los conocimientos necesarios, sabemos interpretar lo que dichos los datos nos pueden decir. Pero lógicamente, la tecnología no puede hacer sino mejorar, por lo que del mismo modo que ahora somos capaces de procesar bases de datos no relacionales, en un futuro probablemente podamos obtener información de la que ahora quizá no somos ni conscientes que podamos tener, es decir, tendremos mejores conocimientos y mejor tecnología para obtener nuevas informaciones de los datos, tal y como está ocurriendo ahora, en los inicios del
Big Data.
El tratamiento de datos masivos puede aportarnos grandes beneficios a todas las personas, a nivel global, pero también puede acarrear importantes consecuencias que repercutan negativamente en el individuo, tanto desde un punto de vista sociológico, pudiendo conllevar discriminación, despersonalización, y problemas éticos (en relación a un uso responsable del
big data), como a nivel de protección de datos de carácter personal.
Se analiza el concepto de tratamientos masivos de datos (
big data), y su diferente encuadre jurídico como un bien económico dentro de la denominada Economía de los datos, o desde la perspectiva del derecho fundamental de protección de datos. Se estudia también el nacimiento y evolución del derecho fundamental a la protección de datos, tanto a nivel europeo como en EEUU, con el fin de observar las diferencias entre ambos regímenes jurídicos, así como el marco jurídico normativo de protección de datos tanto a nivel español, europeo e internacional y en EEUU.
Se analizan los peligros que plantea y su incidencia respecto a la normativa de protección de datos de carácter personal, realizando un análisis crítico de la evolución de la normativa y el nivel de protección que ofrece con respecto a los nuevos retos que plantea la evolución de la Sociedad de la Información.
A nivel de protección de datos, los riesgos que se plantean son los derivados de una falta de transparencia y control, pues los datos son recogidos de las más diversas fuentes, por diferentes…
Advisors/Committee Members: Plaza Penadés, Javier (advisor).
Subjects/Keywords: big data;
protección de datos
Record Details
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Record Details
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Loza Corera, M. (2017). De los microdatos a los datos masivos. Cuestiones legales
. (Doctoral Dissertation). Universitat de Valencia. Retrieved from http://hdl.handle.net/10550/61015
Chicago Manual of Style (16th Edition):
Loza Corera, María. “De los microdatos a los datos masivos. Cuestiones legales
.” 2017. Doctoral Dissertation, Universitat de Valencia. Accessed June 25, 2019.
http://hdl.handle.net/10550/61015.
MLA Handbook (7th Edition):
Loza Corera, María. “De los microdatos a los datos masivos. Cuestiones legales
.” 2017. Web. 25 Jun 2019.
Vancouver:
Loza Corera M. De los microdatos a los datos masivos. Cuestiones legales
. [Internet] [Doctoral dissertation]. Universitat de Valencia; 2017. [cited 2019 Jun 25].
Available from: http://hdl.handle.net/10550/61015.
Council of Science Editors:
Loza Corera M. De los microdatos a los datos masivos. Cuestiones legales
. [Doctoral Dissertation]. Universitat de Valencia; 2017. Available from: http://hdl.handle.net/10550/61015
28.
Mäntysaari, Piia-Pauliina.
Big data : tieto lisää (muutakin kuin) tuskaa
.
Degree: 2018, Theseus
URL: http://www.theseus.fi/handle/10024/159988
Subjects/Keywords: big data
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APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Mäntysaari, P. (2018). Big data : tieto lisää (muutakin kuin) tuskaa
. (Thesis). Theseus. Retrieved from http://www.theseus.fi/handle/10024/159988
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Mäntysaari, Piia-Pauliina. “Big data : tieto lisää (muutakin kuin) tuskaa
.” 2018. Thesis, Theseus. Accessed June 25, 2019.
http://www.theseus.fi/handle/10024/159988.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Mäntysaari, Piia-Pauliina. “Big data : tieto lisää (muutakin kuin) tuskaa
.” 2018. Web. 25 Jun 2019.
Vancouver:
Mäntysaari P. Big data : tieto lisää (muutakin kuin) tuskaa
. [Internet] [Thesis]. Theseus; 2018. [cited 2019 Jun 25].
Available from: http://www.theseus.fi/handle/10024/159988.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Mäntysaari P. Big data : tieto lisää (muutakin kuin) tuskaa
. [Thesis]. Theseus; 2018. Available from: http://www.theseus.fi/handle/10024/159988
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Uppsala University
29.
Lind, Simon.
Distributed Ensemble Learning With Apache Spark.
Degree: Biology Education Centre, 2016, Uppsala University
URL: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-274323
Subjects/Keywords: Big Data
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APA ·
Chicago ·
MLA ·
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CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Lind, S. (2016). Distributed Ensemble Learning With Apache Spark. (Thesis). Uppsala University. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-274323
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Lind, Simon. “Distributed Ensemble Learning With Apache Spark.” 2016. Thesis, Uppsala University. Accessed June 25, 2019.
http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-274323.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Lind, Simon. “Distributed Ensemble Learning With Apache Spark.” 2016. Web. 25 Jun 2019.
Vancouver:
Lind S. Distributed Ensemble Learning With Apache Spark. [Internet] [Thesis]. Uppsala University; 2016. [cited 2019 Jun 25].
Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-274323.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Lind S. Distributed Ensemble Learning With Apache Spark. [Thesis]. Uppsala University; 2016. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-274323
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
University of Oklahoma
30.
Yetgin, Emre.
Cognitive Fit in Visualizing Big Data.
Degree: PhD, 2015, University of Oklahoma
URL: http://hdl.handle.net/11244/14568
► This dissertation examines the consequences of cognitive fit in visualizing big data. Specifically, it focuses on the interplay between different types of business data analysis…
(more)
▼ This dissertation examines the consequences of cognitive fit in visualizing
big data. Specifically, it focuses on the interplay between different types of business
data analysis tasks and visualization methods, and how the defining characteristics of
big data (i.e., volume and variety) moderate the outcomes concerning
data analysis performance (i.e., solution time and solution accuracy). A 12-cell repeated-measures laboratory experiment (n=145) using eye trackers is conducted to test the hypotheses.
Data analysis performance is observed to improve when the information emphasized by a visualization method matches the specific information requirements for a
data analysis task. Such improvements in
data analysis performance are further amplified when the visualized information has high volume and variety.
This dissertation contributes to the literature in at least three ways. First, it improves our understanding of cognitive fit and how it manifests in analysts’ problem solving behaviors when using visualization tools. This is done by analyzing participants’ eye movement and gaze fixation patterns while they work with different types of
data analysis tasks and visualization methods. Based on this analysis, this study proposes an objective method for assessing and measuring cognitive fit. Second, this study maps visualization characteristics to business
data analysis task types, and informs the choice of visualization tools among an ever-increasing number of alternatives for supporting the complex problems faced by
big data analysts. Third, this dissertation extends the cognitive fit theory to the
big data context and highlights the relative importance of cognitive fit in this setting by demonstrating that increases in volume and variety amplify the task performance consequences of cognitive fit. The limitations of the experiment conducted for this dissertation and the future research opportunities they present are discussed. The findings of this dissertation also can inform the development of new visualization tools and techniques based on task and
data characteristics.
Advisors/Committee Members: Shaft, Teresa (advisor), Chidambaram, Laku (committee member), Jensen, Matthew (committee member), Santhanam, Radhika (committee member), Weaver, Chris (committee member).
Subjects/Keywords: Visualization; Cognitive Fit; Big Data
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APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Yetgin, E. (2015). Cognitive Fit in Visualizing Big Data. (Doctoral Dissertation). University of Oklahoma. Retrieved from http://hdl.handle.net/11244/14568
Chicago Manual of Style (16th Edition):
Yetgin, Emre. “Cognitive Fit in Visualizing Big Data.” 2015. Doctoral Dissertation, University of Oklahoma. Accessed June 25, 2019.
http://hdl.handle.net/11244/14568.
MLA Handbook (7th Edition):
Yetgin, Emre. “Cognitive Fit in Visualizing Big Data.” 2015. Web. 25 Jun 2019.
Vancouver:
Yetgin E. Cognitive Fit in Visualizing Big Data. [Internet] [Doctoral dissertation]. University of Oklahoma; 2015. [cited 2019 Jun 25].
Available from: http://hdl.handle.net/11244/14568.
Council of Science Editors:
Yetgin E. Cognitive Fit in Visualizing Big Data. [Doctoral Dissertation]. University of Oklahoma; 2015. Available from: http://hdl.handle.net/11244/14568
◁ [1] [2] [3] [4] [5] … [40] ▶
. 
Open Access Theses and Dissertations
