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You searched for subject:(large data). Showing records 1 – 30 of 289 total matches.

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1. Wu, Rui. Environment for Large Data Processing and Visualization Using MongoDB.

Degree: 2015, University of Nevada – Reno

Data means treasures to both scientists and business people. Scientists can discover significant rules and theories beneath data. People involved in business can find their… (more)

Subjects/Keywords: data management; data processing; data visualization; large data

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

APA (6th Edition):

Wu, R. (2015). Environment for Large Data Processing and Visualization Using MongoDB. (Thesis). University of Nevada – Reno. Retrieved from http://hdl.handle.net/11714/2657

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):

Wu, Rui. “Environment for Large Data Processing and Visualization Using MongoDB.” 2015. Thesis, University of Nevada – Reno. Accessed April 23, 2021. http://hdl.handle.net/11714/2657.

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

MLA Handbook (7th Edition):

Wu, Rui. “Environment for Large Data Processing and Visualization Using MongoDB.” 2015. Web. 23 Apr 2021.

Vancouver:

Wu R. Environment for Large Data Processing and Visualization Using MongoDB. [Internet] [Thesis]. University of Nevada – Reno; 2015. [cited 2021 Apr 23]. Available from: http://hdl.handle.net/11714/2657.

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

Council of Science Editors:

Wu R. Environment for Large Data Processing and Visualization Using MongoDB. [Thesis]. University of Nevada – Reno; 2015. Available from: http://hdl.handle.net/11714/2657

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


University of Minnesota

2. Datta, Abhirup. Statistical Methods for Large Complex Datasets.

Degree: PhD, Biostatistics, 2016, University of Minnesota

 Modern technological advancements have enabled massive-scale collection, processing and storage of information triggering the onset of the `big data' era where in every two days… (more)

Subjects/Keywords: Big data; High dimensional data; Large spatial data

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APA (6th Edition):

Datta, A. (2016). Statistical Methods for Large Complex Datasets. (Doctoral Dissertation). University of Minnesota. Retrieved from http://hdl.handle.net/11299/199089

Chicago Manual of Style (16th Edition):

Datta, Abhirup. “Statistical Methods for Large Complex Datasets.” 2016. Doctoral Dissertation, University of Minnesota. Accessed April 23, 2021. http://hdl.handle.net/11299/199089.

MLA Handbook (7th Edition):

Datta, Abhirup. “Statistical Methods for Large Complex Datasets.” 2016. Web. 23 Apr 2021.

Vancouver:

Datta A. Statistical Methods for Large Complex Datasets. [Internet] [Doctoral dissertation]. University of Minnesota; 2016. [cited 2021 Apr 23]. Available from: http://hdl.handle.net/11299/199089.

Council of Science Editors:

Datta A. Statistical Methods for Large Complex Datasets. [Doctoral Dissertation]. University of Minnesota; 2016. Available from: http://hdl.handle.net/11299/199089


University of California – Berkeley

3. Hand, Nicholas. Theoretical and Computational Tools for Analyzing the Large-Scale Structure of the Universe.

Degree: Astrophysics, 2017, University of California – Berkeley

 The analysis of the large-scale structure (LSS) of the Universe can yield insights into some of the most important questions in contemporary cosmology, and in… (more)

Subjects/Keywords: Astrophysics; cosmology; data analysis; large-scale structure

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APA (6th Edition):

Hand, N. (2017). Theoretical and Computational Tools for Analyzing the Large-Scale Structure of the Universe. (Thesis). University of California – Berkeley. Retrieved from http://www.escholarship.org/uc/item/0vk2x0cs

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):

Hand, Nicholas. “Theoretical and Computational Tools for Analyzing the Large-Scale Structure of the Universe.” 2017. Thesis, University of California – Berkeley. Accessed April 23, 2021. http://www.escholarship.org/uc/item/0vk2x0cs.

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

MLA Handbook (7th Edition):

Hand, Nicholas. “Theoretical and Computational Tools for Analyzing the Large-Scale Structure of the Universe.” 2017. Web. 23 Apr 2021.

Vancouver:

Hand N. Theoretical and Computational Tools for Analyzing the Large-Scale Structure of the Universe. [Internet] [Thesis]. University of California – Berkeley; 2017. [cited 2021 Apr 23]. Available from: http://www.escholarship.org/uc/item/0vk2x0cs.

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

Council of Science Editors:

Hand N. Theoretical and Computational Tools for Analyzing the Large-Scale Structure of the Universe. [Thesis]. University of California – Berkeley; 2017. Available from: http://www.escholarship.org/uc/item/0vk2x0cs

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

4. Cheng, Long. A scalable analysis framework for large-scale RDF data.

Degree: 2014, RIAN

 With the growth of the Semantic Web, the availability of RDF datasets from multiple domains as Linked Data has taken the corpora of this web… (more)

Subjects/Keywords: Electronic Engineering; large-scale RDF data

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APA (6th Edition):

Cheng, L. (2014). A scalable analysis framework for large-scale RDF data. (Thesis). RIAN. Retrieved from http://eprints.maynoothuniversity.ie/5442/

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):

Cheng, Long. “A scalable analysis framework for large-scale RDF data.” 2014. Thesis, RIAN. Accessed April 23, 2021. http://eprints.maynoothuniversity.ie/5442/.

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

MLA Handbook (7th Edition):

Cheng, Long. “A scalable analysis framework for large-scale RDF data.” 2014. Web. 23 Apr 2021.

Vancouver:

Cheng L. A scalable analysis framework for large-scale RDF data. [Internet] [Thesis]. RIAN; 2014. [cited 2021 Apr 23]. Available from: http://eprints.maynoothuniversity.ie/5442/.

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

Council of Science Editors:

Cheng L. A scalable analysis framework for large-scale RDF data. [Thesis]. RIAN; 2014. Available from: http://eprints.maynoothuniversity.ie/5442/

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

5. Cheng, Long. A scalable analysis framework for large-scale RDF data.

Degree: 2014, RIAN

 With the growth of the Semantic Web, the availability of RDF datasets from multiple domains as Linked Data has taken the corpora of this web… (more)

Subjects/Keywords: Electronic Engineering; large-scale RDF data

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

APA (6th Edition):

Cheng, L. (2014). A scalable analysis framework for large-scale RDF data. (Thesis). RIAN. Retrieved from http://mural.maynoothuniversity.ie/5442/

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):

Cheng, Long. “A scalable analysis framework for large-scale RDF data.” 2014. Thesis, RIAN. Accessed April 23, 2021. http://mural.maynoothuniversity.ie/5442/.

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

MLA Handbook (7th Edition):

Cheng, Long. “A scalable analysis framework for large-scale RDF data.” 2014. Web. 23 Apr 2021.

Vancouver:

Cheng L. A scalable analysis framework for large-scale RDF data. [Internet] [Thesis]. RIAN; 2014. [cited 2021 Apr 23]. Available from: http://mural.maynoothuniversity.ie/5442/.

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

Council of Science Editors:

Cheng L. A scalable analysis framework for large-scale RDF data. [Thesis]. RIAN; 2014. Available from: http://mural.maynoothuniversity.ie/5442/

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


Vanderbilt University

6. Yan, Wei. A Data-Driven Approach to Optimal Resource Management for Large-Scale Data Processing Platforms.

Degree: PhD, Computer Science, 2015, Vanderbilt University

 In the era of “Big Data”, a variety of data processing and analysis frameworks (such as MapReduce/Hadoop, Dremel/Impala, and Storm) have emerged as a solution… (more)

Subjects/Keywords: Resource management; large-scale; data processing; data profiling; MapReduce/Hadoop

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APA (6th Edition):

Yan, W. (2015). A Data-Driven Approach to Optimal Resource Management for Large-Scale Data Processing Platforms. (Doctoral Dissertation). Vanderbilt University. Retrieved from http://hdl.handle.net/1803/13138

Chicago Manual of Style (16th Edition):

Yan, Wei. “A Data-Driven Approach to Optimal Resource Management for Large-Scale Data Processing Platforms.” 2015. Doctoral Dissertation, Vanderbilt University. Accessed April 23, 2021. http://hdl.handle.net/1803/13138.

MLA Handbook (7th Edition):

Yan, Wei. “A Data-Driven Approach to Optimal Resource Management for Large-Scale Data Processing Platforms.” 2015. Web. 23 Apr 2021.

Vancouver:

Yan W. A Data-Driven Approach to Optimal Resource Management for Large-Scale Data Processing Platforms. [Internet] [Doctoral dissertation]. Vanderbilt University; 2015. [cited 2021 Apr 23]. Available from: http://hdl.handle.net/1803/13138.

Council of Science Editors:

Yan W. A Data-Driven Approach to Optimal Resource Management for Large-Scale Data Processing Platforms. [Doctoral Dissertation]. Vanderbilt University; 2015. Available from: http://hdl.handle.net/1803/13138

7. Quddus, Syed. Accurate and efficient clustering algorithms for very large data sets.

Degree: PhD, 2017, Federation University Australia

The ability to mine and extract useful information from large data sets is a common concern for organizations. Data over the internet is rapidly increasing… (more)

Subjects/Keywords: Clustering algorithms; Very large data sets; Data mining

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

APA (6th Edition):

Quddus, S. (2017). Accurate and efficient clustering algorithms for very large data sets. (Doctoral Dissertation). Federation University Australia. Retrieved from http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/162586 ; https://library.federation.edu.au/record=b2746800

Chicago Manual of Style (16th Edition):

Quddus, Syed. “Accurate and efficient clustering algorithms for very large data sets.” 2017. Doctoral Dissertation, Federation University Australia. Accessed April 23, 2021. http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/162586 ; https://library.federation.edu.au/record=b2746800.

MLA Handbook (7th Edition):

Quddus, Syed. “Accurate and efficient clustering algorithms for very large data sets.” 2017. Web. 23 Apr 2021.

Vancouver:

Quddus S. Accurate and efficient clustering algorithms for very large data sets. [Internet] [Doctoral dissertation]. Federation University Australia; 2017. [cited 2021 Apr 23]. Available from: http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/162586 ; https://library.federation.edu.au/record=b2746800.

Council of Science Editors:

Quddus S. Accurate and efficient clustering algorithms for very large data sets. [Doctoral Dissertation]. Federation University Australia; 2017. Available from: http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/162586 ; https://library.federation.edu.au/record=b2746800


Texas A&M University

8. Khodabakhshi, Morteza. Preservation and Identification of Large Scale Connectivity from Production Data.

Degree: PhD, Petroleum Engineering, 2014, Texas A&M University

 Multipoint statistics (MPS) provides an approach for pattern-based simulation of complex geologic objects from a training image (TI), which contains the general connectivity structures of… (more)

Subjects/Keywords: Facies characterization; Multipoint geostatistics; Probability map; Flow data integration; Large scale connectivity; Large scale continuity

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

APA (6th Edition):

Khodabakhshi, M. (2014). Preservation and Identification of Large Scale Connectivity from Production Data. (Doctoral Dissertation). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/186989

Chicago Manual of Style (16th Edition):

Khodabakhshi, Morteza. “Preservation and Identification of Large Scale Connectivity from Production Data.” 2014. Doctoral Dissertation, Texas A&M University. Accessed April 23, 2021. http://hdl.handle.net/1969.1/186989.

MLA Handbook (7th Edition):

Khodabakhshi, Morteza. “Preservation and Identification of Large Scale Connectivity from Production Data.” 2014. Web. 23 Apr 2021.

Vancouver:

Khodabakhshi M. Preservation and Identification of Large Scale Connectivity from Production Data. [Internet] [Doctoral dissertation]. Texas A&M University; 2014. [cited 2021 Apr 23]. Available from: http://hdl.handle.net/1969.1/186989.

Council of Science Editors:

Khodabakhshi M. Preservation and Identification of Large Scale Connectivity from Production Data. [Doctoral Dissertation]. Texas A&M University; 2014. Available from: http://hdl.handle.net/1969.1/186989


Temple University

9. Djuric, Nemanja. Big Data Algorithms for Visualization and Supervised Learning.

Degree: PhD, 2013, Temple University

Computer and Information Science

Explosive growth in data size, data complexity, and data rates, triggered by emergence of high-throughput technologies such as remote sensing, crowd-sourcing,… (more)

Subjects/Keywords: Computer science;

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

APA (6th Edition):

Djuric, N. (2013). Big Data Algorithms for Visualization and Supervised Learning. (Doctoral Dissertation). Temple University. Retrieved from http://digital.library.temple.edu/u?/p245801coll10,239445

Chicago Manual of Style (16th Edition):

Djuric, Nemanja. “Big Data Algorithms for Visualization and Supervised Learning.” 2013. Doctoral Dissertation, Temple University. Accessed April 23, 2021. http://digital.library.temple.edu/u?/p245801coll10,239445.

MLA Handbook (7th Edition):

Djuric, Nemanja. “Big Data Algorithms for Visualization and Supervised Learning.” 2013. Web. 23 Apr 2021.

Vancouver:

Djuric N. Big Data Algorithms for Visualization and Supervised Learning. [Internet] [Doctoral dissertation]. Temple University; 2013. [cited 2021 Apr 23]. Available from: http://digital.library.temple.edu/u?/p245801coll10,239445.

Council of Science Editors:

Djuric N. Big Data Algorithms for Visualization and Supervised Learning. [Doctoral Dissertation]. Temple University; 2013. Available from: http://digital.library.temple.edu/u?/p245801coll10,239445

10. Ravi, Likhitha. AVISTED: Analysis and Visualization Toolset for Environmental Data.

Degree: 2018, University of Nevada – Reno

 Climate modeled datasets are available on the internet for exploration by researchers in the field of environmental sciences. In order to get insight into these… (more)

Subjects/Keywords: Climate data formats; Data analysis; Data visualization; Environmental sciences; Large datasets; Web based application

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APA (6th Edition):

Ravi, L. (2018). AVISTED: Analysis and Visualization Toolset for Environmental Data. (Thesis). University of Nevada – Reno. Retrieved from http://hdl.handle.net/11714/3446

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):

Ravi, Likhitha. “AVISTED: Analysis and Visualization Toolset for Environmental Data.” 2018. Thesis, University of Nevada – Reno. Accessed April 23, 2021. http://hdl.handle.net/11714/3446.

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

MLA Handbook (7th Edition):

Ravi, Likhitha. “AVISTED: Analysis and Visualization Toolset for Environmental Data.” 2018. Web. 23 Apr 2021.

Vancouver:

Ravi L. AVISTED: Analysis and Visualization Toolset for Environmental Data. [Internet] [Thesis]. University of Nevada – Reno; 2018. [cited 2021 Apr 23]. Available from: http://hdl.handle.net/11714/3446.

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

Council of Science Editors:

Ravi L. AVISTED: Analysis and Visualization Toolset for Environmental Data. [Thesis]. University of Nevada – Reno; 2018. Available from: http://hdl.handle.net/11714/3446

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


UCLA

11. Zhang, Qiang. Modern Models for Learning Large-Scale Highly Skewed Online Advertising Data.

Degree: Statistics, 2015, UCLA

 Click through rate (CTR) and conversation rate estimation are two core prediction tasks in online advertising. However, four major challenges emerged as data scientists trying… (more)

Subjects/Keywords: Statistics; Marketing; Data Mining; High Cardinality; Imbalanced Data; Large-scale Classification; Machine Learning; Online Advertising

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APA (6th Edition):

Zhang, Q. (2015). Modern Models for Learning Large-Scale Highly Skewed Online Advertising Data. (Thesis). UCLA. Retrieved from http://www.escholarship.org/uc/item/7mc0k1v8

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, Qiang. “Modern Models for Learning Large-Scale Highly Skewed Online Advertising Data.” 2015. Thesis, UCLA. Accessed April 23, 2021. http://www.escholarship.org/uc/item/7mc0k1v8.

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

MLA Handbook (7th Edition):

Zhang, Qiang. “Modern Models for Learning Large-Scale Highly Skewed Online Advertising Data.” 2015. Web. 23 Apr 2021.

Vancouver:

Zhang Q. Modern Models for Learning Large-Scale Highly Skewed Online Advertising Data. [Internet] [Thesis]. UCLA; 2015. [cited 2021 Apr 23]. Available from: http://www.escholarship.org/uc/item/7mc0k1v8.

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

Council of Science Editors:

Zhang Q. Modern Models for Learning Large-Scale Highly Skewed Online Advertising Data. [Thesis]. UCLA; 2015. Available from: http://www.escholarship.org/uc/item/7mc0k1v8

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

12. Adjout Rehab, Moufida. Big Data : le nouvel enjeu de l'apprentissage à partir des données massives : Big Data : the new challenge Learning from data Massive.

Degree: Docteur es, Informatique, 2016, Sorbonne Paris Cité

Le croisement du phénomène de mondialisation et du développement continu des technologies de l’information a débouché sur une explosion des volumes de données disponibles. Ainsi,… (more)

Subjects/Keywords: Données massives; Big data; Régression linéaire multiple; Large scale data; Mapreduce; Multiple linear regression; Bagging

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APA (6th Edition):

Adjout Rehab, M. (2016). Big Data : le nouvel enjeu de l'apprentissage à partir des données massives : Big Data : the new challenge Learning from data Massive. (Doctoral Dissertation). Sorbonne Paris Cité. Retrieved from http://www.theses.fr/2016USPCD052

Chicago Manual of Style (16th Edition):

Adjout Rehab, Moufida. “Big Data : le nouvel enjeu de l'apprentissage à partir des données massives : Big Data : the new challenge Learning from data Massive.” 2016. Doctoral Dissertation, Sorbonne Paris Cité. Accessed April 23, 2021. http://www.theses.fr/2016USPCD052.

MLA Handbook (7th Edition):

Adjout Rehab, Moufida. “Big Data : le nouvel enjeu de l'apprentissage à partir des données massives : Big Data : the new challenge Learning from data Massive.” 2016. Web. 23 Apr 2021.

Vancouver:

Adjout Rehab M. Big Data : le nouvel enjeu de l'apprentissage à partir des données massives : Big Data : the new challenge Learning from data Massive. [Internet] [Doctoral dissertation]. Sorbonne Paris Cité; 2016. [cited 2021 Apr 23]. Available from: http://www.theses.fr/2016USPCD052.

Council of Science Editors:

Adjout Rehab M. Big Data : le nouvel enjeu de l'apprentissage à partir des données massives : Big Data : the new challenge Learning from data Massive. [Doctoral Dissertation]. Sorbonne Paris Cité; 2016. Available from: http://www.theses.fr/2016USPCD052

13. Liu, Qingyun. Data-driven Graph Analysis.

Degree: 2017, University of California – eScholarship, University of California

 The ever-expanding demands for network utilities today have greatly changed people’s lives. We are all around by various networks, from Internet, social net- works, to… (more)

Subjects/Keywords: Computer science; Data-driven Analysis; Graph Analysis; Graph Modeling; Large-scale Data; Online Social Networks

Page 1 Page 2 Page 3 Page 4 Page 5 Page 6 Page 7

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APA (6th Edition):

Liu, Q. (2017). Data-driven Graph Analysis. (Thesis). University of California – eScholarship, University of California. Retrieved from http://www.escholarship.org/uc/item/89z8k2r7

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):

Liu, Qingyun. “Data-driven Graph Analysis.” 2017. Thesis, University of California – eScholarship, University of California. Accessed April 23, 2021. http://www.escholarship.org/uc/item/89z8k2r7.

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

MLA Handbook (7th Edition):

Liu, Qingyun. “Data-driven Graph Analysis.” 2017. Web. 23 Apr 2021.

Vancouver:

Liu Q. Data-driven Graph Analysis. [Internet] [Thesis]. University of California – eScholarship, University of California; 2017. [cited 2021 Apr 23]. Available from: http://www.escholarship.org/uc/item/89z8k2r7.

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

Council of Science Editors:

Liu Q. Data-driven Graph Analysis. [Thesis]. University of California – eScholarship, University of California; 2017. Available from: http://www.escholarship.org/uc/item/89z8k2r7

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


King Abdullah University of Science and Technology

14. Ibrahim, Mohamed. Interactive High-Quality Visualization of Large-Scale Particle Data.

Degree: Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, 2019, King Abdullah University of Science and Technology

Large-scale particle data sets, such as those computed in molecular dynamics (MD) simulations, are crucial to investigating important processes in physics and thermodynamics. The simulated… (more)

Subjects/Keywords: large-scale data; molecular dynamics; scientific visualization; particle data; hight-quality rendering

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APA (6th Edition):

Ibrahim, M. (2019). Interactive High-Quality Visualization of Large-Scale Particle Data. (Thesis). King Abdullah University of Science and Technology. Retrieved from http://hdl.handle.net/10754/660131

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):

Ibrahim, Mohamed. “Interactive High-Quality Visualization of Large-Scale Particle Data.” 2019. Thesis, King Abdullah University of Science and Technology. Accessed April 23, 2021. http://hdl.handle.net/10754/660131.

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

MLA Handbook (7th Edition):

Ibrahim, Mohamed. “Interactive High-Quality Visualization of Large-Scale Particle Data.” 2019. Web. 23 Apr 2021.

Vancouver:

Ibrahim M. Interactive High-Quality Visualization of Large-Scale Particle Data. [Internet] [Thesis]. King Abdullah University of Science and Technology; 2019. [cited 2021 Apr 23]. Available from: http://hdl.handle.net/10754/660131.

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

Council of Science Editors:

Ibrahim M. Interactive High-Quality Visualization of Large-Scale Particle Data. [Thesis]. King Abdullah University of Science and Technology; 2019. Available from: http://hdl.handle.net/10754/660131

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


Cornell University

15. Shrivastava, Anshumali. Probabilistic Hashing Techniques For Big Data.

Degree: PhD, Computer Science, 2015, Cornell University

 We investigate probabilistic hashing techniques for addressing computational and memory challenges in large scale machine learning and data mining systems. In this thesis, we show… (more)

Subjects/Keywords: Large Scale Machine Learning; Randomized Algorithms for Big-Data; Hashing, Sketching

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APA (6th Edition):

Shrivastava, A. (2015). Probabilistic Hashing Techniques For Big Data. (Doctoral Dissertation). Cornell University. Retrieved from http://hdl.handle.net/1813/40886

Chicago Manual of Style (16th Edition):

Shrivastava, Anshumali. “Probabilistic Hashing Techniques For Big Data.” 2015. Doctoral Dissertation, Cornell University. Accessed April 23, 2021. http://hdl.handle.net/1813/40886.

MLA Handbook (7th Edition):

Shrivastava, Anshumali. “Probabilistic Hashing Techniques For Big Data.” 2015. Web. 23 Apr 2021.

Vancouver:

Shrivastava A. Probabilistic Hashing Techniques For Big Data. [Internet] [Doctoral dissertation]. Cornell University; 2015. [cited 2021 Apr 23]. Available from: http://hdl.handle.net/1813/40886.

Council of Science Editors:

Shrivastava A. Probabilistic Hashing Techniques For Big Data. [Doctoral Dissertation]. Cornell University; 2015. Available from: http://hdl.handle.net/1813/40886


Vanderbilt University

16. Mercaldo, Sarah Fletcher. On Optimal Prediction Rules With Prospective Missingness and Bagged Empirical Null Inference in Large-Scale Data.

Degree: PhD, Biostatistics, 2017, Vanderbilt University

 This dissertation consists of three papers related to missing data, prediction, and large scale inference. The first paper defines the problem of obtaining predictions from… (more)

Subjects/Keywords: missing data; imputation; prediction models; large-scale inference; p-values

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

APA (6th Edition):

Mercaldo, S. F. (2017). On Optimal Prediction Rules With Prospective Missingness and Bagged Empirical Null Inference in Large-Scale Data. (Doctoral Dissertation). Vanderbilt University. Retrieved from http://hdl.handle.net/1803/14094

Chicago Manual of Style (16th Edition):

Mercaldo, Sarah Fletcher. “On Optimal Prediction Rules With Prospective Missingness and Bagged Empirical Null Inference in Large-Scale Data.” 2017. Doctoral Dissertation, Vanderbilt University. Accessed April 23, 2021. http://hdl.handle.net/1803/14094.

MLA Handbook (7th Edition):

Mercaldo, Sarah Fletcher. “On Optimal Prediction Rules With Prospective Missingness and Bagged Empirical Null Inference in Large-Scale Data.” 2017. Web. 23 Apr 2021.

Vancouver:

Mercaldo SF. On Optimal Prediction Rules With Prospective Missingness and Bagged Empirical Null Inference in Large-Scale Data. [Internet] [Doctoral dissertation]. Vanderbilt University; 2017. [cited 2021 Apr 23]. Available from: http://hdl.handle.net/1803/14094.

Council of Science Editors:

Mercaldo SF. On Optimal Prediction Rules With Prospective Missingness and Bagged Empirical Null Inference in Large-Scale Data. [Doctoral Dissertation]. Vanderbilt University; 2017. Available from: http://hdl.handle.net/1803/14094


University of Iowa

17. Alsulaiman, Thamer. Detecting complex genetic mutations in large human genome data.

Degree: PhD, Computer Science, 2019, University of Iowa

  All cellular forms of life contain Deoxyribonucleic acid (DNA). DNA is a molecule that carries all the information necessary to perform both, basic and… (more)

Subjects/Keywords: DNA; Large Data; MMBIR; Mutations; Parallel Programming; Computer Sciences

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

APA (6th Edition):

Alsulaiman, T. (2019). Detecting complex genetic mutations in large human genome data. (Doctoral Dissertation). University of Iowa. Retrieved from https://ir.uiowa.edu/etd/6908

Chicago Manual of Style (16th Edition):

Alsulaiman, Thamer. “Detecting complex genetic mutations in large human genome data.” 2019. Doctoral Dissertation, University of Iowa. Accessed April 23, 2021. https://ir.uiowa.edu/etd/6908.

MLA Handbook (7th Edition):

Alsulaiman, Thamer. “Detecting complex genetic mutations in large human genome data.” 2019. Web. 23 Apr 2021.

Vancouver:

Alsulaiman T. Detecting complex genetic mutations in large human genome data. [Internet] [Doctoral dissertation]. University of Iowa; 2019. [cited 2021 Apr 23]. Available from: https://ir.uiowa.edu/etd/6908.

Council of Science Editors:

Alsulaiman T. Detecting complex genetic mutations in large human genome data. [Doctoral Dissertation]. University of Iowa; 2019. Available from: https://ir.uiowa.edu/etd/6908

18. Chen, Tianyue. The cross-correlation between large scale structure, HI intensity maps and CMB maps.

Degree: 2019, University of Manchester

 HI intensity mapping is a new and efficient technique for mapping the large-scale-structures in the Universe and its expansion history in three dimensions. Due to… (more)

Subjects/Keywords: cosmology; cross-correlation; large-scale-structure; data analysis; systematics

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APA (6th Edition):

Chen, T. (2019). The cross-correlation between large scale structure, HI intensity maps and CMB maps. (Doctoral Dissertation). University of Manchester. Retrieved from http://www.manchester.ac.uk/escholar/uk-ac-man-scw:318277

Chicago Manual of Style (16th Edition):

Chen, Tianyue. “The cross-correlation between large scale structure, HI intensity maps and CMB maps.” 2019. Doctoral Dissertation, University of Manchester. Accessed April 23, 2021. http://www.manchester.ac.uk/escholar/uk-ac-man-scw:318277.

MLA Handbook (7th Edition):

Chen, Tianyue. “The cross-correlation between large scale structure, HI intensity maps and CMB maps.” 2019. Web. 23 Apr 2021.

Vancouver:

Chen T. The cross-correlation between large scale structure, HI intensity maps and CMB maps. [Internet] [Doctoral dissertation]. University of Manchester; 2019. [cited 2021 Apr 23]. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:318277.

Council of Science Editors:

Chen T. The cross-correlation between large scale structure, HI intensity maps and CMB maps. [Doctoral Dissertation]. University of Manchester; 2019. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:318277


Colorado State University

19. Roads, Eric. Analysis of contaminant mass in place in transmissive and low-k zones.

Degree: MS(M.S.), Civil and Environmental Engineering, 2020, Colorado State University

 Contaminant hydrology has been challenged by the common perception of homogeneous subsurface media. Previous sampling methods neglect the importance of differentiating between transmissive and low-k… (more)

Subjects/Keywords: cryogenic core collection; remediation; hydrocarbon chlorinated solvent; contaminant hydrology; large data

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APA (6th Edition):

Roads, E. (2020). Analysis of contaminant mass in place in transmissive and low-k zones. (Masters Thesis). Colorado State University. Retrieved from http://hdl.handle.net/10217/212025

Chicago Manual of Style (16th Edition):

Roads, Eric. “Analysis of contaminant mass in place in transmissive and low-k zones.” 2020. Masters Thesis, Colorado State University. Accessed April 23, 2021. http://hdl.handle.net/10217/212025.

MLA Handbook (7th Edition):

Roads, Eric. “Analysis of contaminant mass in place in transmissive and low-k zones.” 2020. Web. 23 Apr 2021.

Vancouver:

Roads E. Analysis of contaminant mass in place in transmissive and low-k zones. [Internet] [Masters thesis]. Colorado State University; 2020. [cited 2021 Apr 23]. Available from: http://hdl.handle.net/10217/212025.

Council of Science Editors:

Roads E. Analysis of contaminant mass in place in transmissive and low-k zones. [Masters Thesis]. Colorado State University; 2020. Available from: http://hdl.handle.net/10217/212025


University of Cambridge

20. Gog, Ionel Corneliu. Flexible and efficient computation in large data centres.

Degree: PhD, 2018, University of Cambridge

 Increasingly, online computer applications rely on large-scale data analyses to offer personalised and improved products. These large-scale analyses are performed on distributed data processing execution… (more)

Subjects/Keywords: large scale data processing; cluster management; dataflow systems; cluster scheduling

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APA (6th Edition):

Gog, I. C. (2018). Flexible and efficient computation in large data centres. (Doctoral Dissertation). University of Cambridge. Retrieved from https://www.repository.cam.ac.uk/bitstream/1810/271804/6/Gog-2018-PhD.jpg ; https://www.repository.cam.ac.uk/bitstream/1810/271804/5/Gog-2018-PhD.txt ; https://www.repository.cam.ac.uk/bitstream/1810/271804/4/f55882e2-fc7e-42eb-aa10-a0dd917a4415.zip ; https://www.repository.cam.ac.uk/bitstream/1810/271804/3/license.txt ; https://www.repository.cam.ac.uk/bitstream/1810/271804/2/f55882e2-fc7e-42eb-aa10-a0dd917a4415_confirmations.txt ; https://www.repository.cam.ac.uk/bitstream/1810/271804/1/Gog-2018-PhD

Chicago Manual of Style (16th Edition):

Gog, Ionel Corneliu. “Flexible and efficient computation in large data centres.” 2018. Doctoral Dissertation, University of Cambridge. Accessed April 23, 2021. https://www.repository.cam.ac.uk/bitstream/1810/271804/6/Gog-2018-PhD.jpg ; https://www.repository.cam.ac.uk/bitstream/1810/271804/5/Gog-2018-PhD.txt ; https://www.repository.cam.ac.uk/bitstream/1810/271804/4/f55882e2-fc7e-42eb-aa10-a0dd917a4415.zip ; https://www.repository.cam.ac.uk/bitstream/1810/271804/3/license.txt ; https://www.repository.cam.ac.uk/bitstream/1810/271804/2/f55882e2-fc7e-42eb-aa10-a0dd917a4415_confirmations.txt ; https://www.repository.cam.ac.uk/bitstream/1810/271804/1/Gog-2018-PhD.

MLA Handbook (7th Edition):

Gog, Ionel Corneliu. “Flexible and efficient computation in large data centres.” 2018. Web. 23 Apr 2021.

Vancouver:

Gog IC. Flexible and efficient computation in large data centres. [Internet] [Doctoral dissertation]. University of Cambridge; 2018. [cited 2021 Apr 23]. Available from: https://www.repository.cam.ac.uk/bitstream/1810/271804/6/Gog-2018-PhD.jpg ; https://www.repository.cam.ac.uk/bitstream/1810/271804/5/Gog-2018-PhD.txt ; https://www.repository.cam.ac.uk/bitstream/1810/271804/4/f55882e2-fc7e-42eb-aa10-a0dd917a4415.zip ; https://www.repository.cam.ac.uk/bitstream/1810/271804/3/license.txt ; https://www.repository.cam.ac.uk/bitstream/1810/271804/2/f55882e2-fc7e-42eb-aa10-a0dd917a4415_confirmations.txt ; https://www.repository.cam.ac.uk/bitstream/1810/271804/1/Gog-2018-PhD.

Council of Science Editors:

Gog IC. Flexible and efficient computation in large data centres. [Doctoral Dissertation]. University of Cambridge; 2018. Available from: https://www.repository.cam.ac.uk/bitstream/1810/271804/6/Gog-2018-PhD.jpg ; https://www.repository.cam.ac.uk/bitstream/1810/271804/5/Gog-2018-PhD.txt ; https://www.repository.cam.ac.uk/bitstream/1810/271804/4/f55882e2-fc7e-42eb-aa10-a0dd917a4415.zip ; https://www.repository.cam.ac.uk/bitstream/1810/271804/3/license.txt ; https://www.repository.cam.ac.uk/bitstream/1810/271804/2/f55882e2-fc7e-42eb-aa10-a0dd917a4415_confirmations.txt ; https://www.repository.cam.ac.uk/bitstream/1810/271804/1/Gog-2018-PhD

21. Chen, Tianyue. The cross-correlation between large scale structure, HI intensity maps and CMB maps.

Degree: PhD, 2019, University of Manchester

 HI intensity mapping is a new and efficient technique for mapping the large-scale-structures in the Universe and its expansion history in three dimensions. Due to… (more)

Subjects/Keywords: 500; data analysis; systematics; large-scale-structure; cosmology; cross-correlation

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

APA (6th Edition):

Chen, T. (2019). The cross-correlation between large scale structure, HI intensity maps and CMB maps. (Doctoral Dissertation). University of Manchester. Retrieved from https://www.research.manchester.ac.uk/portal/en/theses/the-crosscorrelation-between-large-scale-structure-hi-intensity-maps-and-cmb-maps(ff86e256-5e62-4643-90fe-e4ea853eb9ac).html ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.771470

Chicago Manual of Style (16th Edition):

Chen, Tianyue. “The cross-correlation between large scale structure, HI intensity maps and CMB maps.” 2019. Doctoral Dissertation, University of Manchester. Accessed April 23, 2021. https://www.research.manchester.ac.uk/portal/en/theses/the-crosscorrelation-between-large-scale-structure-hi-intensity-maps-and-cmb-maps(ff86e256-5e62-4643-90fe-e4ea853eb9ac).html ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.771470.

MLA Handbook (7th Edition):

Chen, Tianyue. “The cross-correlation between large scale structure, HI intensity maps and CMB maps.” 2019. Web. 23 Apr 2021.

Vancouver:

Chen T. The cross-correlation between large scale structure, HI intensity maps and CMB maps. [Internet] [Doctoral dissertation]. University of Manchester; 2019. [cited 2021 Apr 23]. Available from: https://www.research.manchester.ac.uk/portal/en/theses/the-crosscorrelation-between-large-scale-structure-hi-intensity-maps-and-cmb-maps(ff86e256-5e62-4643-90fe-e4ea853eb9ac).html ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.771470.

Council of Science Editors:

Chen T. The cross-correlation between large scale structure, HI intensity maps and CMB maps. [Doctoral Dissertation]. University of Manchester; 2019. Available from: https://www.research.manchester.ac.uk/portal/en/theses/the-crosscorrelation-between-large-scale-structure-hi-intensity-maps-and-cmb-maps(ff86e256-5e62-4643-90fe-e4ea853eb9ac).html ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.771470


University of Minnesota

22. Mardani, Morteza. Leveraging Sparsity and Low Rank for Large-Scale Networks and Data Science.

Degree: PhD, Electrical/Computer Engineering, 2015, University of Minnesota

 We live in an era of ``data deluge," with pervasive sensors collecting massive amounts of information on every bit of our lives, churning out enormous… (more)

Subjects/Keywords: Big data; Large-scale networks; learning; Low rank; Sparsity

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

APA (6th Edition):

Mardani, M. (2015). Leveraging Sparsity and Low Rank for Large-Scale Networks and Data Science. (Doctoral Dissertation). University of Minnesota. Retrieved from http://hdl.handle.net/11299/174873

Chicago Manual of Style (16th Edition):

Mardani, Morteza. “Leveraging Sparsity and Low Rank for Large-Scale Networks and Data Science.” 2015. Doctoral Dissertation, University of Minnesota. Accessed April 23, 2021. http://hdl.handle.net/11299/174873.

MLA Handbook (7th Edition):

Mardani, Morteza. “Leveraging Sparsity and Low Rank for Large-Scale Networks and Data Science.” 2015. Web. 23 Apr 2021.

Vancouver:

Mardani M. Leveraging Sparsity and Low Rank for Large-Scale Networks and Data Science. [Internet] [Doctoral dissertation]. University of Minnesota; 2015. [cited 2021 Apr 23]. Available from: http://hdl.handle.net/11299/174873.

Council of Science Editors:

Mardani M. Leveraging Sparsity and Low Rank for Large-Scale Networks and Data Science. [Doctoral Dissertation]. University of Minnesota; 2015. Available from: http://hdl.handle.net/11299/174873


University of Southern California

23. Weidner, Martin. Large N, T asymptotic analysis of panel data models with incidental parameters.

Degree: PhD, Economics, 2011, University of Southern California

 This dissertation contributes to the econometrics of panel data models and their application to economic problems. In particular, it considers "large T'' panels, where in… (more)

Subjects/Keywords: factor models; incidental parameters; large panels; panel data models

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APA (6th Edition):

Weidner, M. (2011). Large N, T asymptotic analysis of panel data models with incidental parameters. (Doctoral Dissertation). University of Southern California. Retrieved from http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/449644/rec/3742

Chicago Manual of Style (16th Edition):

Weidner, Martin. “Large N, T asymptotic analysis of panel data models with incidental parameters.” 2011. Doctoral Dissertation, University of Southern California. Accessed April 23, 2021. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/449644/rec/3742.

MLA Handbook (7th Edition):

Weidner, Martin. “Large N, T asymptotic analysis of panel data models with incidental parameters.” 2011. Web. 23 Apr 2021.

Vancouver:

Weidner M. Large N, T asymptotic analysis of panel data models with incidental parameters. [Internet] [Doctoral dissertation]. University of Southern California; 2011. [cited 2021 Apr 23]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/449644/rec/3742.

Council of Science Editors:

Weidner M. Large N, T asymptotic analysis of panel data models with incidental parameters. [Doctoral Dissertation]. University of Southern California; 2011. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/449644/rec/3742


Louisiana State University

24. Yu, Wuyi. Large-scale Geometric Data Decomposition, Processing and Structured Mesh Generation.

Degree: PhD, Computer Sciences, 2015, Louisiana State University

 Mesh generation is a fundamental and critical problem in geometric data modeling and processing. In most scientific and engineering tasks that involve numerical computations and… (more)

Subjects/Keywords: Large-scale data; Parallel Mesh Generation; Structured Mesh Generation

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APA (6th Edition):

Yu, W. (2015). Large-scale Geometric Data Decomposition, Processing and Structured Mesh Generation. (Doctoral Dissertation). Louisiana State University. Retrieved from etd-01072016-092541 ; https://digitalcommons.lsu.edu/gradschool_dissertations/211

Chicago Manual of Style (16th Edition):

Yu, Wuyi. “Large-scale Geometric Data Decomposition, Processing and Structured Mesh Generation.” 2015. Doctoral Dissertation, Louisiana State University. Accessed April 23, 2021. etd-01072016-092541 ; https://digitalcommons.lsu.edu/gradschool_dissertations/211.

MLA Handbook (7th Edition):

Yu, Wuyi. “Large-scale Geometric Data Decomposition, Processing and Structured Mesh Generation.” 2015. Web. 23 Apr 2021.

Vancouver:

Yu W. Large-scale Geometric Data Decomposition, Processing and Structured Mesh Generation. [Internet] [Doctoral dissertation]. Louisiana State University; 2015. [cited 2021 Apr 23]. Available from: etd-01072016-092541 ; https://digitalcommons.lsu.edu/gradschool_dissertations/211.

Council of Science Editors:

Yu W. Large-scale Geometric Data Decomposition, Processing and Structured Mesh Generation. [Doctoral Dissertation]. Louisiana State University; 2015. Available from: etd-01072016-092541 ; https://digitalcommons.lsu.edu/gradschool_dissertations/211


Louisiana State University

25. Jiang, Lei. Parallel surrogate detection in large-scale simulations.

Degree: MSCS, Computer Sciences, 2011, Louisiana State University

 Simulation has become a useful approach in scientific computing and engineering for its ability to model real natural or human systems. In particular, for complex… (more)

Subjects/Keywords: large-scale simulations; data mining; scalability; surrogate modeling; automated workflow

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APA (6th Edition):

Jiang, L. (2011). Parallel surrogate detection in large-scale simulations. (Masters Thesis). Louisiana State University. Retrieved from etd-04262011-114601 ; https://digitalcommons.lsu.edu/gradschool_theses/3058

Chicago Manual of Style (16th Edition):

Jiang, Lei. “Parallel surrogate detection in large-scale simulations.” 2011. Masters Thesis, Louisiana State University. Accessed April 23, 2021. etd-04262011-114601 ; https://digitalcommons.lsu.edu/gradschool_theses/3058.

MLA Handbook (7th Edition):

Jiang, Lei. “Parallel surrogate detection in large-scale simulations.” 2011. Web. 23 Apr 2021.

Vancouver:

Jiang L. Parallel surrogate detection in large-scale simulations. [Internet] [Masters thesis]. Louisiana State University; 2011. [cited 2021 Apr 23]. Available from: etd-04262011-114601 ; https://digitalcommons.lsu.edu/gradschool_theses/3058.

Council of Science Editors:

Jiang L. Parallel surrogate detection in large-scale simulations. [Masters Thesis]. Louisiana State University; 2011. Available from: etd-04262011-114601 ; https://digitalcommons.lsu.edu/gradschool_theses/3058


Kansas State University

26. Ofori, Francis Ohene. The impact of weather change on nitrous oxide emission with spatial pattern detection and large data approximation.

Degree: PhD, Department of Statistics, 2019, Kansas State University

 The correlations between agriculture, climate change, and greenhouse gas concentration are multiplex and manifold. Agriculture has been a focus due to its vital connection with… (more)

Subjects/Keywords: Spatial pattern detection; Geographical algorithm machine; Large Data Approximation

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APA (6th Edition):

Ofori, F. O. (2019). The impact of weather change on nitrous oxide emission with spatial pattern detection and large data approximation. (Doctoral Dissertation). Kansas State University. Retrieved from http://hdl.handle.net/2097/39641

Chicago Manual of Style (16th Edition):

Ofori, Francis Ohene. “The impact of weather change on nitrous oxide emission with spatial pattern detection and large data approximation.” 2019. Doctoral Dissertation, Kansas State University. Accessed April 23, 2021. http://hdl.handle.net/2097/39641.

MLA Handbook (7th Edition):

Ofori, Francis Ohene. “The impact of weather change on nitrous oxide emission with spatial pattern detection and large data approximation.” 2019. Web. 23 Apr 2021.

Vancouver:

Ofori FO. The impact of weather change on nitrous oxide emission with spatial pattern detection and large data approximation. [Internet] [Doctoral dissertation]. Kansas State University; 2019. [cited 2021 Apr 23]. Available from: http://hdl.handle.net/2097/39641.

Council of Science Editors:

Ofori FO. The impact of weather change on nitrous oxide emission with spatial pattern detection and large data approximation. [Doctoral Dissertation]. Kansas State University; 2019. Available from: http://hdl.handle.net/2097/39641


University of Cambridge

27. Gog, Ionel Corneliu. Flexible and efficient computation in large data centres.

Degree: PhD, 2018, University of Cambridge

 Increasingly, online computer applications rely on large-scale data analyses to offer personalised and improved products. These large-scale analyses are performed on distributed data processing execution… (more)

Subjects/Keywords: 004; large scale data processing; cluster management; dataflow systems; cluster scheduling

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

APA (6th Edition):

Gog, I. C. (2018). Flexible and efficient computation in large data centres. (Doctoral Dissertation). University of Cambridge. Retrieved from https://doi.org/10.17863/CAM.18802 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.744511

Chicago Manual of Style (16th Edition):

Gog, Ionel Corneliu. “Flexible and efficient computation in large data centres.” 2018. Doctoral Dissertation, University of Cambridge. Accessed April 23, 2021. https://doi.org/10.17863/CAM.18802 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.744511.

MLA Handbook (7th Edition):

Gog, Ionel Corneliu. “Flexible and efficient computation in large data centres.” 2018. Web. 23 Apr 2021.

Vancouver:

Gog IC. Flexible and efficient computation in large data centres. [Internet] [Doctoral dissertation]. University of Cambridge; 2018. [cited 2021 Apr 23]. Available from: https://doi.org/10.17863/CAM.18802 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.744511.

Council of Science Editors:

Gog IC. Flexible and efficient computation in large data centres. [Doctoral Dissertation]. University of Cambridge; 2018. Available from: https://doi.org/10.17863/CAM.18802 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.744511


Hong Kong University of Science and Technology

28. Wang, Guangju IEDA. Data-driven decision making in large scale service systems.

Degree: 2020, Hong Kong University of Science and Technology

 Modern telecommunication technology enables service systems to operate on a larger scale than ever. With a large user base, new challenges also emerge in the… (more)

Subjects/Keywords: Large scale systems ; Decision making ; Express service ; Data processing ; Ridesharing

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APA (6th Edition):

Wang, G. I. (2020). Data-driven decision making in large scale service systems. (Thesis). Hong Kong University of Science and Technology. Retrieved from http://repository.ust.hk/ir/Record/1783.1-105166 ; https://doi.org/10.14711/thesis-991012818768303412 ; http://repository.ust.hk/ir/bitstream/1783.1-105166/1/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, Guangju IEDA. “Data-driven decision making in large scale service systems.” 2020. Thesis, Hong Kong University of Science and Technology. Accessed April 23, 2021. http://repository.ust.hk/ir/Record/1783.1-105166 ; https://doi.org/10.14711/thesis-991012818768303412 ; http://repository.ust.hk/ir/bitstream/1783.1-105166/1/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, Guangju IEDA. “Data-driven decision making in large scale service systems.” 2020. Web. 23 Apr 2021.

Vancouver:

Wang GI. Data-driven decision making in large scale service systems. [Internet] [Thesis]. Hong Kong University of Science and Technology; 2020. [cited 2021 Apr 23]. Available from: http://repository.ust.hk/ir/Record/1783.1-105166 ; https://doi.org/10.14711/thesis-991012818768303412 ; http://repository.ust.hk/ir/bitstream/1783.1-105166/1/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 GI. Data-driven decision making in large scale service systems. [Thesis]. Hong Kong University of Science and Technology; 2020. Available from: http://repository.ust.hk/ir/Record/1783.1-105166 ; https://doi.org/10.14711/thesis-991012818768303412 ; http://repository.ust.hk/ir/bitstream/1783.1-105166/1/th_redirect.html

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


University of Helsinki

29. Fornaro, Paolo. Dynamic Factor Models and Forecasting Finnish Macroeconomic Variables.

Degree: Department of Political and Economic Studies; Helsingfors universitet, Statsvetenskapliga fakulteten, Institutionen för politik och ekonomi, 2011, University of Helsinki

 In recent years, thanks to developments in information technology, large-dimensional datasets have been increasingly available. Researchers now have access to thousands of economic series and… (more)

Subjects/Keywords: forecasting; factor model; large datasets; micro data; Economics; Kansantaloustiede; Nationalekonomi

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APA (6th Edition):

Fornaro, P. (2011). Dynamic Factor Models and Forecasting Finnish Macroeconomic Variables. (Masters Thesis). University of Helsinki. Retrieved from http://hdl.handle.net/10138/26610

Chicago Manual of Style (16th Edition):

Fornaro, Paolo. “Dynamic Factor Models and Forecasting Finnish Macroeconomic Variables.” 2011. Masters Thesis, University of Helsinki. Accessed April 23, 2021. http://hdl.handle.net/10138/26610.

MLA Handbook (7th Edition):

Fornaro, Paolo. “Dynamic Factor Models and Forecasting Finnish Macroeconomic Variables.” 2011. Web. 23 Apr 2021.

Vancouver:

Fornaro P. Dynamic Factor Models and Forecasting Finnish Macroeconomic Variables. [Internet] [Masters thesis]. University of Helsinki; 2011. [cited 2021 Apr 23]. Available from: http://hdl.handle.net/10138/26610.

Council of Science Editors:

Fornaro P. Dynamic Factor Models and Forecasting Finnish Macroeconomic Variables. [Masters Thesis]. University of Helsinki; 2011. Available from: http://hdl.handle.net/10138/26610


Delft University of Technology

30. Colaço Baptista Cerqueira, Paulo (author). Data-Driven Filtering for Large-Scale Adaptive Optics.

Degree: 2020, Delft University of Technology

The visualization of objects within or beyond a turbulent medium is hampered by the aberrations the medium induces in the wavefront. The sharpness of an… (more)

Subjects/Keywords: Kalman Filtering; Data-Driven; Adaptive Optics; Large-Scale

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

APA (6th Edition):

Colaço Baptista Cerqueira, P. (. (2020). Data-Driven Filtering for Large-Scale Adaptive Optics. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:15786830-1e6d-410e-9dd5-29bff53e6d1f

Chicago Manual of Style (16th Edition):

Colaço Baptista Cerqueira, Paulo (author). “Data-Driven Filtering for Large-Scale Adaptive Optics.” 2020. Masters Thesis, Delft University of Technology. Accessed April 23, 2021. http://resolver.tudelft.nl/uuid:15786830-1e6d-410e-9dd5-29bff53e6d1f.

MLA Handbook (7th Edition):

Colaço Baptista Cerqueira, Paulo (author). “Data-Driven Filtering for Large-Scale Adaptive Optics.” 2020. Web. 23 Apr 2021.

Vancouver:

Colaço Baptista Cerqueira P(. Data-Driven Filtering for Large-Scale Adaptive Optics. [Internet] [Masters thesis]. Delft University of Technology; 2020. [cited 2021 Apr 23]. Available from: http://resolver.tudelft.nl/uuid:15786830-1e6d-410e-9dd5-29bff53e6d1f.

Council of Science Editors:

Colaço Baptista Cerqueira P(. Data-Driven Filtering for Large-Scale Adaptive Optics. [Masters Thesis]. Delft University of Technology; 2020. Available from: http://resolver.tudelft.nl/uuid:15786830-1e6d-410e-9dd5-29bff53e6d1f

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