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You searched for subject:(Big Data). Showing records 1 – 30 of 1527 total matches.

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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

  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)

Subjects/Keywords: Big Data; Big Data Quality

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Desai, K. Y. (2018). Big Data Quality Modeling And Validation. (Masters Thesis). San Jose State University. Retrieved from https://doi.org/10.31979/etd.c68w-98uf ; 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 August 07, 2020. https://doi.org/10.31979/etd.c68w-98uf ; https://scholarworks.sjsu.edu/etd_theses/4898.

MLA Handbook (7th Edition):

Desai, Khushali Yashodhar. “Big Data Quality Modeling And Validation.” 2018. Web. 07 Aug 2020.

Vancouver:

Desai KY. Big Data Quality Modeling And Validation. [Internet] [Masters thesis]. San Jose State University; 2018. [cited 2020 Aug 07]. Available from: https://doi.org/10.31979/etd.c68w-98uf ; 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://doi.org/10.31979/etd.c68w-98uf ; 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

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)

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 August 07, 2020. 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. 07 Aug 2020.

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 2020 Aug 07]. 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

3. Zgraggen, Emanuel Albert Errol. Towards Accessible Data Analysis.

Degree: Department of Computer Science, 2018, Brown University

 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)

Subjects/Keywords: Big data

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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 August 07, 2020. 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. 07 Aug 2020.

Vancouver:

Zgraggen EAE. Towards Accessible Data Analysis. [Internet] [Thesis]. Brown University; 2018. [cited 2020 Aug 07]. 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


Wake Forest University

4. Bowling, Roy Nathaniel. Big Data and the Rhetorical Narrative.

Degree: 2014, Wake Forest University

 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)

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 August 07, 2020. 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. 07 Aug 2020.

Vancouver:

Bowling RN. Big Data and the Rhetorical Narrative. [Internet] [Thesis]. Wake Forest University; 2014. [cited 2020 Aug 07]. 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


California State Polytechnic University – Pomona

5. Singh, Darvesh Pari. Big Data - Performance Analysis Of Hadoop.

Degree: MS, Computer Science, 2017, California State Polytechnic University – Pomona

 Clustering data stream is an important branch of mining data stream. Due to the dynamic nature of the data stream and large, traditional data mining… (more)

Subjects/Keywords: big data

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Singh, D. P. (2017). Big Data - Performance Analysis Of Hadoop. (Masters Thesis). California State Polytechnic University – Pomona. Retrieved from http://hdl.handle.net/10211.3/189329

Chicago Manual of Style (16th Edition):

Singh, Darvesh Pari. “Big Data - Performance Analysis Of Hadoop.” 2017. Masters Thesis, California State Polytechnic University – Pomona. Accessed August 07, 2020. http://hdl.handle.net/10211.3/189329.

MLA Handbook (7th Edition):

Singh, Darvesh Pari. “Big Data - Performance Analysis Of Hadoop.” 2017. Web. 07 Aug 2020.

Vancouver:

Singh DP. Big Data - Performance Analysis Of Hadoop. [Internet] [Masters thesis]. California State Polytechnic University – Pomona; 2017. [cited 2020 Aug 07]. Available from: http://hdl.handle.net/10211.3/189329.

Council of Science Editors:

Singh DP. Big Data - Performance Analysis Of Hadoop. [Masters Thesis]. California State Polytechnic University – Pomona; 2017. Available from: http://hdl.handle.net/10211.3/189329


University of KwaZulu-Natal

6. 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

 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)

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 August 07, 2020. 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. 07 Aug 2020.

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 2020 Aug 07]. 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


Delft University of Technology

7. Gloudemans, T.W. (author). Aircraft Performance Parameter Estimation using Global ADS-B and Open Data.

Degree: 2016, Delft University of Technology

To enable low cost open source ATM simulations the University of Technology Delft is developing an open source ATM simulator Bluesky. A method was developed… (more)

Subjects/Keywords: ADSB; Big Data

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APA (6th Edition):

Gloudemans, T. W. (. (2016). Aircraft Performance Parameter Estimation using Global ADS-B and Open Data. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:6b210945-7004-430e-884e-72b27a7f5acc

Chicago Manual of Style (16th Edition):

Gloudemans, T W (author). “Aircraft Performance Parameter Estimation using Global ADS-B and Open Data.” 2016. Masters Thesis, Delft University of Technology. Accessed August 07, 2020. http://resolver.tudelft.nl/uuid:6b210945-7004-430e-884e-72b27a7f5acc.

MLA Handbook (7th Edition):

Gloudemans, T W (author). “Aircraft Performance Parameter Estimation using Global ADS-B and Open Data.” 2016. Web. 07 Aug 2020.

Vancouver:

Gloudemans TW(. Aircraft Performance Parameter Estimation using Global ADS-B and Open Data. [Internet] [Masters thesis]. Delft University of Technology; 2016. [cited 2020 Aug 07]. Available from: http://resolver.tudelft.nl/uuid:6b210945-7004-430e-884e-72b27a7f5acc.

Council of Science Editors:

Gloudemans TW(. Aircraft Performance Parameter Estimation using Global ADS-B and Open Data. [Masters Thesis]. Delft University of Technology; 2016. Available from: http://resolver.tudelft.nl/uuid:6b210945-7004-430e-884e-72b27a7f5acc


Universidade do Minho

8. Torres, Hugo Miguel Oliveira. Benchmarking de tecnologias de Big Data aplicadas à saúde-medicina .

Degree: 2017, Universidade do Minho

 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)

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 August 07, 2020. 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. 07 Aug 2020.

Vancouver:

Torres HMO. Benchmarking de tecnologias de Big Data aplicadas à saúde-medicina . [Internet] [Masters thesis]. Universidade do Minho; 2017. [cited 2020 Aug 07]. 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

9. Verheij, B.A. (author). The process of big data solution adoption.

Degree: 2013, Delft University of Technology

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)

Subjects/Keywords: big data; big data solution; NoSQL; big data solution adoption

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APA · Chicago · MLA · Vancouver · CSE | Export 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 (author). “The process of big data solution adoption.” 2013. Masters Thesis, Delft University of Technology. Accessed August 07, 2020. http://resolver.tudelft.nl/uuid:369986f0-dd24-4855-aa85-8f69a3325191.

MLA Handbook (7th Edition):

Verheij, B A (author). “The process of big data solution adoption.” 2013. Web. 07 Aug 2020.

Vancouver:

Verheij BA(. The process of big data solution adoption. [Internet] [Masters thesis]. Delft University of Technology; 2013. [cited 2020 Aug 07]. 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


Montana State University

10. Ganesan Pillai, Karthik. Mining spatiotemporal co-occurrence patterns from massive data sets with evolving regions.

Degree: College of Engineering, 2014, Montana State University

 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)

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 August 07, 2020. 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. 07 Aug 2020.

Vancouver:

Ganesan Pillai K. Mining spatiotemporal co-occurrence patterns from massive data sets with evolving regions. [Internet] [Thesis]. Montana State University; 2014. [cited 2020 Aug 07]. 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


Rutgers University

11. Patel, Jimit. Real time big data mining.

Degree: MS, Computer Science, 2015, Rutgers University

 This thesis presents a parallel implementation of data streaming algorithms for multiple streams. Thousands of data streams are generated in different industries like finance, health,… (more)

Subjects/Keywords: Big data; Data mining

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APA (6th Edition):

Patel, J. (2015). Real time big data mining. (Masters Thesis). Rutgers University. Retrieved from https://rucore.libraries.rutgers.edu/rutgers-lib/49077/

Chicago Manual of Style (16th Edition):

Patel, Jimit. “Real time big data mining.” 2015. Masters Thesis, Rutgers University. Accessed August 07, 2020. https://rucore.libraries.rutgers.edu/rutgers-lib/49077/.

MLA Handbook (7th Edition):

Patel, Jimit. “Real time big data mining.” 2015. Web. 07 Aug 2020.

Vancouver:

Patel J. Real time big data mining. [Internet] [Masters thesis]. Rutgers University; 2015. [cited 2020 Aug 07]. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/49077/.

Council of Science Editors:

Patel J. Real time big data mining. [Masters Thesis]. Rutgers University; 2015. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/49077/


University of Hawaii – Manoa

12. Kang, Qiuling. Sentiment analysis of big social data with Apache Hadoop.

Degree: 2015, University of Hawaii – Manoa

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)

Subjects/Keywords: Twitter data sets; big data

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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 August 07, 2020. 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. 07 Aug 2020.

Vancouver:

Kang Q. Sentiment analysis of big social data with Apache Hadoop. [Internet] [Thesis]. University of Hawaii – Manoa; 2015. [cited 2020 Aug 07]. 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


University of Saskatchewan

13. 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

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)

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 August 07, 2020. 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. 07 Aug 2020.

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 2020 Aug 07]. 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


Indiana University

14. Suriarachchi, Isuru. Big provenance stream processing for data-intensive computations .

Degree: 2018, Indiana University

 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)

Subjects/Keywords: Big Data; Big Provenance; Stream Processing

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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 August 07, 2020. 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. 07 Aug 2020.

Vancouver:

Suriarachchi I. Big provenance stream processing for data-intensive computations . [Internet] [Thesis]. Indiana University; 2018. [cited 2020 Aug 07]. 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

15. 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

 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)

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 August 07, 2020. 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. 07 Aug 2020.

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 2020 Aug 07]. 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

16. Cao, Xiang. Efficient Data Management and Processing in Big Data Applications.

Degree: PhD, Computer Science, 2017, University of Minnesota

 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)

Subjects/Keywords: Big Data; Data Management; Data Processing

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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 August 07, 2020. http://hdl.handle.net/11299/188863.

MLA Handbook (7th Edition):

Cao, Xiang. “Efficient Data Management and Processing in Big Data Applications.” 2017. Web. 07 Aug 2020.

Vancouver:

Cao X. Efficient Data Management and Processing in Big Data Applications. [Internet] [Doctoral dissertation]. University of Minnesota; 2017. [cited 2020 Aug 07]. 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


Georgia Tech

17. Lee, Kisung. Scalable big data systems: Architectures and optimizations.

Degree: PhD, Computer Science, 2015, Georgia Tech

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)

Subjects/Keywords: Big data; Graph data; Spatial data

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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 August 07, 2020. http://hdl.handle.net/1853/55520.

MLA Handbook (7th Edition):

Lee, Kisung. “Scalable big data systems: Architectures and optimizations.” 2015. Web. 07 Aug 2020.

Vancouver:

Lee K. Scalable big data systems: Architectures and optimizations. [Internet] [Doctoral dissertation]. Georgia Tech; 2015. [cited 2020 Aug 07]. 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


Jönköping University

18. Rystadius, Gustaf; Monell, David. The dynamic management revolution of Big Data : A case study of Åhlen’s Big Data Analytics operation.

Degree: Jönköping International Business School, 2020, Jönköping University

  Background: The implementation of Big Data Analytics (BDA) has drastically increased within several sectors such as retailing. Due to its rapidly altering environment, companies have… (more)

Subjects/Keywords: Big Data Analytics; Big Data Analytics Capabilities; Ambidexterity; Agility; Big Data; Business Administration; Företagsekonomi

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APA (6th Edition):

Rystadius, Gustaf; Monell, D. (2020). The dynamic management revolution of Big Data : A case study of Åhlen’s Big Data Analytics operation. (Thesis). Jönköping University. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-48959

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):

Rystadius, Gustaf; Monell, David. “The dynamic management revolution of Big Data : A case study of Åhlen’s Big Data Analytics operation.” 2020. Thesis, Jönköping University. Accessed August 07, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-48959.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Rystadius, Gustaf; Monell, David. “The dynamic management revolution of Big Data : A case study of Åhlen’s Big Data Analytics operation.” 2020. Web. 07 Aug 2020.

Vancouver:

Rystadius, Gustaf; Monell D. The dynamic management revolution of Big Data : A case study of Åhlen’s Big Data Analytics operation. [Internet] [Thesis]. Jönköping University; 2020. [cited 2020 Aug 07]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-48959.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Rystadius, Gustaf; Monell D. The dynamic management revolution of Big Data : A case study of Åhlen’s Big Data Analytics operation. [Thesis]. Jönköping University; 2020. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-48959

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Brunel University

19. Lakoju, Mike. A strategic approach of value identification for a big data project.

Degree: PhD, 2017, Brunel University

 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)

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 August 07, 2020. 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. 07 Aug 2020.

Vancouver:

Lakoju M. A strategic approach of value identification for a big data project. [Internet] [Doctoral dissertation]. Brunel University; 2017. [cited 2020 Aug 07]. 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 California – Riverside

20. Jacobs, Steven. A BAD Thesis: The Vision, Creation, and Evaluation of a Big Active Data Platform.

Degree: Computer Science, 2018, University of California – Riverside

 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)

Subjects/Keywords: Computer science; Active Data; Big Active Data; Big Data

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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 August 07, 2020. 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. 07 Aug 2020.

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 2020 Aug 07]. 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

21. Fize, Jacques. Mise en correspondance de données textuelles hétérogènes fondée sur la dimension spatiale : Matching between heterogeneous textual data based on spatial dimension.

Degree: Docteur es, Informatique, 2019, Montpellier

Avec l’essor du Big Data, le traitement du Volume, de la Vélocité (croissance et évolution) et de la Variété de la donnée concentre les efforts… (more)

Subjects/Keywords: Science des données; Big Data; Biodiversité; Data Science; Big Data; Biodiversity

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APA (6th Edition):

Fize, J. (2019). Mise en correspondance de données textuelles hétérogènes fondée sur la dimension spatiale : Matching between heterogeneous textual data based on spatial dimension. (Doctoral Dissertation). Montpellier. Retrieved from http://www.theses.fr/2019MONTS099

Chicago Manual of Style (16th Edition):

Fize, Jacques. “Mise en correspondance de données textuelles hétérogènes fondée sur la dimension spatiale : Matching between heterogeneous textual data based on spatial dimension.” 2019. Doctoral Dissertation, Montpellier. Accessed August 07, 2020. http://www.theses.fr/2019MONTS099.

MLA Handbook (7th Edition):

Fize, Jacques. “Mise en correspondance de données textuelles hétérogènes fondée sur la dimension spatiale : Matching between heterogeneous textual data based on spatial dimension.” 2019. Web. 07 Aug 2020.

Vancouver:

Fize J. Mise en correspondance de données textuelles hétérogènes fondée sur la dimension spatiale : Matching between heterogeneous textual data based on spatial dimension. [Internet] [Doctoral dissertation]. Montpellier; 2019. [cited 2020 Aug 07]. Available from: http://www.theses.fr/2019MONTS099.

Council of Science Editors:

Fize J. Mise en correspondance de données textuelles hétérogènes fondée sur la dimension spatiale : Matching between heterogeneous textual data based on spatial dimension. [Doctoral Dissertation]. Montpellier; 2019. Available from: http://www.theses.fr/2019MONTS099


Penn State University

22. Iyer, Karthik Thyagarajan. Computational complexity of data mining algorithms used in fraud detection.

Degree: MS, Industrial Engineering, 2015, Penn State University

 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)

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 August 07, 2020. 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. 07 Aug 2020.

Vancouver:

Iyer KT. Computational complexity of data mining algorithms used in fraud detection. [Internet] [Masters thesis]. Penn State University; 2015. [cited 2020 Aug 07]. 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


Universiteit Utrecht

23. 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

 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)

Subjects/Keywords: big data; ethic; e-government

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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 August 07, 2020. 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. 07 Aug 2020.

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 2020 Aug 07]. 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


University of Manitoba

24. Chen, Yixuan. Approaching “Big Data” in Biological Research Imaging Spectroscopy with Novel Compression.

Degree: Biosystems Engineering, 2014, University of Manitoba

 This research focuses on providing a fast and space efficient compression method to answer information queries on spectroscopic data. Our primary hypothesis was whether a… (more)

Subjects/Keywords: Image Compression; Big Data

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APA (6th Edition):

Chen, Y. (2014). Approaching “Big Data” in Biological Research Imaging Spectroscopy with Novel Compression. (Masters Thesis). University of Manitoba. Retrieved from http://hdl.handle.net/1993/23434

Chicago Manual of Style (16th Edition):

Chen, Yixuan. “Approaching “Big Data” in Biological Research Imaging Spectroscopy with Novel Compression.” 2014. Masters Thesis, University of Manitoba. Accessed August 07, 2020. http://hdl.handle.net/1993/23434.

MLA Handbook (7th Edition):

Chen, Yixuan. “Approaching “Big Data” in Biological Research Imaging Spectroscopy with Novel Compression.” 2014. Web. 07 Aug 2020.

Vancouver:

Chen Y. Approaching “Big Data” in Biological Research Imaging Spectroscopy with Novel Compression. [Internet] [Masters thesis]. University of Manitoba; 2014. [cited 2020 Aug 07]. Available from: http://hdl.handle.net/1993/23434.

Council of Science Editors:

Chen Y. Approaching “Big Data” in Biological Research Imaging Spectroscopy with Novel Compression. [Masters Thesis]. University of Manitoba; 2014. Available from: http://hdl.handle.net/1993/23434


University of New South Wales

25. Chen, Chen. Process big data using approximation methods.

Degree: Computer Science & Engineering, 2016, University of New South Wales

Data proliferation makes big data analysis a challenging task. One way to address the issue is to utilize the parallel systems but it is cost… (more)

Subjects/Keywords: Big data; Approximation methods

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APA (6th Edition):

Chen, C. (2016). Process big data using approximation methods. (Doctoral Dissertation). University of New South Wales. Retrieved from http://handle.unsw.edu.au/1959.4/57020 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:42283/SOURCE02?view=true

Chicago Manual of Style (16th Edition):

Chen, Chen. “Process big data using approximation methods.” 2016. Doctoral Dissertation, University of New South Wales. Accessed August 07, 2020. http://handle.unsw.edu.au/1959.4/57020 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:42283/SOURCE02?view=true.

MLA Handbook (7th Edition):

Chen, Chen. “Process big data using approximation methods.” 2016. Web. 07 Aug 2020.

Vancouver:

Chen C. Process big data using approximation methods. [Internet] [Doctoral dissertation]. University of New South Wales; 2016. [cited 2020 Aug 07]. Available from: http://handle.unsw.edu.au/1959.4/57020 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:42283/SOURCE02?view=true.

Council of Science Editors:

Chen C. Process big data using approximation methods. [Doctoral Dissertation]. University of New South Wales; 2016. Available from: http://handle.unsw.edu.au/1959.4/57020 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:42283/SOURCE02?view=true


Princeton University

26. Zhao, Tianqi. Statistical Inference for Big Data .

Degree: PhD, 2017, Princeton University

 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)

Subjects/Keywords: Big Data; Statistical Inference

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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 August 07, 2020. http://arks.princeton.edu/ark:/88435/dsp017d278w62w.

MLA Handbook (7th Edition):

Zhao, Tianqi. “Statistical Inference for Big Data .” 2017. Web. 07 Aug 2020.

Vancouver:

Zhao T. Statistical Inference for Big Data . [Internet] [Doctoral dissertation]. Princeton University; 2017. [cited 2020 Aug 07]. 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


Tampere University

27. Hassi, Sakari. Big Datan visualisoinnin kokemus virtuaalitodellisuudessa .

Degree: 2018, Tampere University

 Tutkielmassa pyrittiin selvittämään vastauksia siihen olisiko virtuaalitodellisuus soveltuva ympäristö Big Datan visualisoimiseen, eli tehostaisiko kokemuksellisempi ympäristö Big Dataksi luokiteltavien datajoukkojen ymmärtämistä. Tutkimuskysymykseen liittyen tutkielmassa haluttiin… (more)

Subjects/Keywords: Big Data; virtuaalitodellisuus; visualisointi; käyttäjäkokemus

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APA (6th Edition):

Hassi, S. (2018). Big Datan visualisoinnin kokemus virtuaalitodellisuudessa . (Masters Thesis). Tampere University. Retrieved from https://trepo.tuni.fi/handle/10024/104000

Chicago Manual of Style (16th Edition):

Hassi, Sakari. “Big Datan visualisoinnin kokemus virtuaalitodellisuudessa .” 2018. Masters Thesis, Tampere University. Accessed August 07, 2020. https://trepo.tuni.fi/handle/10024/104000.

MLA Handbook (7th Edition):

Hassi, Sakari. “Big Datan visualisoinnin kokemus virtuaalitodellisuudessa .” 2018. Web. 07 Aug 2020.

Vancouver:

Hassi S. Big Datan visualisoinnin kokemus virtuaalitodellisuudessa . [Internet] [Masters thesis]. Tampere University; 2018. [cited 2020 Aug 07]. Available from: https://trepo.tuni.fi/handle/10024/104000.

Council of Science Editors:

Hassi S. Big Datan visualisoinnin kokemus virtuaalitodellisuudessa . [Masters Thesis]. Tampere University; 2018. Available from: https://trepo.tuni.fi/handle/10024/104000


Rutgers University

28. Liu, Xialu, 1986-. New models and methods for time series analysis in big data era.

Degree: PhD, Statistics and Biostatistics, 2015, Rutgers University

In big data era, available information becomes massive and complex and is often observed over time. Conventional time series models are limited in capability of… (more)

Subjects/Keywords: Time-series analysis; Big data

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APA (6th Edition):

Liu, Xialu, 1. (2015). New models and methods for time series analysis in big data era. (Doctoral Dissertation). Rutgers University. Retrieved from https://rucore.libraries.rutgers.edu/rutgers-lib/47468/

Chicago Manual of Style (16th Edition):

Liu, Xialu, 1986-. “New models and methods for time series analysis in big data era.” 2015. Doctoral Dissertation, Rutgers University. Accessed August 07, 2020. https://rucore.libraries.rutgers.edu/rutgers-lib/47468/.

MLA Handbook (7th Edition):

Liu, Xialu, 1986-. “New models and methods for time series analysis in big data era.” 2015. Web. 07 Aug 2020.

Vancouver:

Liu, Xialu 1. New models and methods for time series analysis in big data era. [Internet] [Doctoral dissertation]. Rutgers University; 2015. [cited 2020 Aug 07]. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/47468/.

Council of Science Editors:

Liu, Xialu 1. New models and methods for time series analysis in big data era. [Doctoral Dissertation]. Rutgers University; 2015. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/47468/


Rutgers University

29. Ghassemi, Mohsen, 1990-. From coordinate descent to social sampling: coordinate sampling for large scale optimization.

Degree: MS, Electrical and Computer Engineering, 2016, Rutgers University

 The unprecedented rate at which data is being created and stored calls for scalable optimization techniques that allow e cient Big Data" analysis. In this… (more)

Subjects/Keywords: Stochastic processes; Big data

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APA (6th Edition):

Ghassemi, Mohsen, 1. (2016). From coordinate descent to social sampling: coordinate sampling for large scale optimization. (Masters Thesis). Rutgers University. Retrieved from https://rucore.libraries.rutgers.edu/rutgers-lib/49967/

Chicago Manual of Style (16th Edition):

Ghassemi, Mohsen, 1990-. “From coordinate descent to social sampling: coordinate sampling for large scale optimization.” 2016. Masters Thesis, Rutgers University. Accessed August 07, 2020. https://rucore.libraries.rutgers.edu/rutgers-lib/49967/.

MLA Handbook (7th Edition):

Ghassemi, Mohsen, 1990-. “From coordinate descent to social sampling: coordinate sampling for large scale optimization.” 2016. Web. 07 Aug 2020.

Vancouver:

Ghassemi, Mohsen 1. From coordinate descent to social sampling: coordinate sampling for large scale optimization. [Internet] [Masters thesis]. Rutgers University; 2016. [cited 2020 Aug 07]. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/49967/.

Council of Science Editors:

Ghassemi, Mohsen 1. From coordinate descent to social sampling: coordinate sampling for large scale optimization. [Masters Thesis]. Rutgers University; 2016. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/49967/


Rutgers University

30. Turkoz, Mehmet, 1984-. Distribution-free fault identification and anomaly detection in high-dimensional data.

Degree: PhD, Industrial and Systems Engineering, 2018, Rutgers University

 Quality engineering is an essential activity in production processes and its objective is to ensure the quality of the products throughout the production stages. Many… (more)

Subjects/Keywords: Manufacturing processes; Big data

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APA (6th Edition):

Turkoz, Mehmet, 1. (2018). Distribution-free fault identification and anomaly detection in high-dimensional data. (Doctoral Dissertation). Rutgers University. Retrieved from https://rucore.libraries.rutgers.edu/rutgers-lib/57741/

Chicago Manual of Style (16th Edition):

Turkoz, Mehmet, 1984-. “Distribution-free fault identification and anomaly detection in high-dimensional data.” 2018. Doctoral Dissertation, Rutgers University. Accessed August 07, 2020. https://rucore.libraries.rutgers.edu/rutgers-lib/57741/.

MLA Handbook (7th Edition):

Turkoz, Mehmet, 1984-. “Distribution-free fault identification and anomaly detection in high-dimensional data.” 2018. Web. 07 Aug 2020.

Vancouver:

Turkoz, Mehmet 1. Distribution-free fault identification and anomaly detection in high-dimensional data. [Internet] [Doctoral dissertation]. Rutgers University; 2018. [cited 2020 Aug 07]. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/57741/.

Council of Science Editors:

Turkoz, Mehmet 1. Distribution-free fault identification and anomaly detection in high-dimensional data. [Doctoral Dissertation]. Rutgers University; 2018. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/57741/

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