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You searched for subject:(Big Data Computing). Showing records 1 – 30 of 135 total matches.

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University of Technology, Sydney

1. Zhang, X. Toward scalable and cost-effective privacy-preserving big data publishing in cloud computing.

Degree: 2014, University of Technology, Sydney

Big data and cloud computing are two disruptive trends nowadays, provisioning numerous opportunities to current IT industry and research communities while posing significant challenges on… (more)

Subjects/Keywords: Cloud computing.; Big data.; Privacy.

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

APA (6th Edition):

Zhang, X. (2014). Toward scalable and cost-effective privacy-preserving big data publishing in cloud computing. (Thesis). University of Technology, Sydney. Retrieved from http://hdl.handle.net/10453/30324

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

Chicago Manual of Style (16th Edition):

Zhang, X. “Toward scalable and cost-effective privacy-preserving big data publishing in cloud computing.” 2014. Thesis, University of Technology, Sydney. Accessed December 08, 2019. http://hdl.handle.net/10453/30324.

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

MLA Handbook (7th Edition):

Zhang, X. “Toward scalable and cost-effective privacy-preserving big data publishing in cloud computing.” 2014. Web. 08 Dec 2019.

Vancouver:

Zhang X. Toward scalable and cost-effective privacy-preserving big data publishing in cloud computing. [Internet] [Thesis]. University of Technology, Sydney; 2014. [cited 2019 Dec 08]. Available from: http://hdl.handle.net/10453/30324.

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

Council of Science Editors:

Zhang X. Toward scalable and cost-effective privacy-preserving big data publishing in cloud computing. [Thesis]. University of Technology, Sydney; 2014. Available from: http://hdl.handle.net/10453/30324

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


Australian National University

2. Wang, Meisong. Layered performance modelling and evaluation for cloud topic detection and tracking based big data applications .

Degree: 2016, Australian National University

 “Big Data” best characterized by its three features namely “Variety”, “Volume” and “Velocity” is revolutionizing nearly every aspect of our lives ranging from enterprises to… (more)

Subjects/Keywords: Cloud Computing; IoT; Big Data

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APA (6th Edition):

Wang, M. (2016). Layered performance modelling and evaluation for cloud topic detection and tracking based big data applications . (Thesis). Australian National University. Retrieved from http://hdl.handle.net/1885/107262

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, Meisong. “Layered performance modelling and evaluation for cloud topic detection and tracking based big data applications .” 2016. Thesis, Australian National University. Accessed December 08, 2019. http://hdl.handle.net/1885/107262.

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

MLA Handbook (7th Edition):

Wang, Meisong. “Layered performance modelling and evaluation for cloud topic detection and tracking based big data applications .” 2016. Web. 08 Dec 2019.

Vancouver:

Wang M. Layered performance modelling and evaluation for cloud topic detection and tracking based big data applications . [Internet] [Thesis]. Australian National University; 2016. [cited 2019 Dec 08]. Available from: http://hdl.handle.net/1885/107262.

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

Council of Science Editors:

Wang M. Layered performance modelling and evaluation for cloud topic detection and tracking based big data applications . [Thesis]. Australian National University; 2016. Available from: http://hdl.handle.net/1885/107262

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


University of North Carolina – Greensboro

3. Whitworth, Jeffrey N. Applying hybrid cloud systems to solve challenges posed by the big data problem.

Degree: 2013, University of North Carolina – Greensboro

 The problem of Big Data poses challenges to traditional compute systems used for Machine Learning (ML) techniques that extract, analyze and visualize important information. New… (more)

Subjects/Keywords: Big data; Cloud computing – Security measures

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APA (6th Edition):

Whitworth, J. N. (2013). Applying hybrid cloud systems to solve challenges posed by the big data problem. (Masters Thesis). University of North Carolina – Greensboro. Retrieved from http://libres.uncg.edu/ir/listing.aspx?styp=ti&id=15603

Chicago Manual of Style (16th Edition):

Whitworth, Jeffrey N. “Applying hybrid cloud systems to solve challenges posed by the big data problem.” 2013. Masters Thesis, University of North Carolina – Greensboro. Accessed December 08, 2019. http://libres.uncg.edu/ir/listing.aspx?styp=ti&id=15603.

MLA Handbook (7th Edition):

Whitworth, Jeffrey N. “Applying hybrid cloud systems to solve challenges posed by the big data problem.” 2013. Web. 08 Dec 2019.

Vancouver:

Whitworth JN. Applying hybrid cloud systems to solve challenges posed by the big data problem. [Internet] [Masters thesis]. University of North Carolina – Greensboro; 2013. [cited 2019 Dec 08]. Available from: http://libres.uncg.edu/ir/listing.aspx?styp=ti&id=15603.

Council of Science Editors:

Whitworth JN. Applying hybrid cloud systems to solve challenges posed by the big data problem. [Masters Thesis]. University of North Carolina – Greensboro; 2013. Available from: http://libres.uncg.edu/ir/listing.aspx?styp=ti&id=15603


Vanderbilt University

4. Tapdiya, Ashish. Large Scale Data Management for Enterprise Workloads.

Degree: PhD, Computer Science, 2018, Vanderbilt University

 The continual proliferation of mobile devices, social media platforms, gaming consoles, etc., combined with the ever-increasing online user population has resulted in a data deluge.… (more)

Subjects/Keywords: Cloud Computing; Big Data; Databases; Performance Evaluation

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

APA (6th Edition):

Tapdiya, A. (2018). Large Scale Data Management for Enterprise Workloads. (Doctoral Dissertation). Vanderbilt University. Retrieved from http://etd.library.vanderbilt.edu//available/etd-03262018-133402/ ;

Chicago Manual of Style (16th Edition):

Tapdiya, Ashish. “Large Scale Data Management for Enterprise Workloads.” 2018. Doctoral Dissertation, Vanderbilt University. Accessed December 08, 2019. http://etd.library.vanderbilt.edu//available/etd-03262018-133402/ ;.

MLA Handbook (7th Edition):

Tapdiya, Ashish. “Large Scale Data Management for Enterprise Workloads.” 2018. Web. 08 Dec 2019.

Vancouver:

Tapdiya A. Large Scale Data Management for Enterprise Workloads. [Internet] [Doctoral dissertation]. Vanderbilt University; 2018. [cited 2019 Dec 08]. Available from: http://etd.library.vanderbilt.edu//available/etd-03262018-133402/ ;.

Council of Science Editors:

Tapdiya A. Large Scale Data Management for Enterprise Workloads. [Doctoral Dissertation]. Vanderbilt University; 2018. Available from: http://etd.library.vanderbilt.edu//available/etd-03262018-133402/ ;


University of Bridgeport

5. Alshammari, Hamoud H. Improving Hadoop Performance by Using Metadata of Related Jobs in Text Datasets Via Enhancing MapReduce Workflow .

Degree: 2016, University of Bridgeport

 Cloud Computing provides different services to the users with regard to processing data. One of the main concepts in Cloud Computing is BigData and BigData… (more)

Subjects/Keywords: Big data; Cloud computing; Hadoop; MapReduce

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

APA (6th Edition):

Alshammari, H. H. (2016). Improving Hadoop Performance by Using Metadata of Related Jobs in Text Datasets Via Enhancing MapReduce Workflow . (Thesis). University of Bridgeport. Retrieved from https://scholarworks.bridgeport.edu/xmlui/handle/123456789/1660

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):

Alshammari, Hamoud H. “Improving Hadoop Performance by Using Metadata of Related Jobs in Text Datasets Via Enhancing MapReduce Workflow .” 2016. Thesis, University of Bridgeport. Accessed December 08, 2019. https://scholarworks.bridgeport.edu/xmlui/handle/123456789/1660.

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

MLA Handbook (7th Edition):

Alshammari, Hamoud H. “Improving Hadoop Performance by Using Metadata of Related Jobs in Text Datasets Via Enhancing MapReduce Workflow .” 2016. Web. 08 Dec 2019.

Vancouver:

Alshammari HH. Improving Hadoop Performance by Using Metadata of Related Jobs in Text Datasets Via Enhancing MapReduce Workflow . [Internet] [Thesis]. University of Bridgeport; 2016. [cited 2019 Dec 08]. Available from: https://scholarworks.bridgeport.edu/xmlui/handle/123456789/1660.

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

Council of Science Editors:

Alshammari HH. Improving Hadoop Performance by Using Metadata of Related Jobs in Text Datasets Via Enhancing MapReduce Workflow . [Thesis]. University of Bridgeport; 2016. Available from: https://scholarworks.bridgeport.edu/xmlui/handle/123456789/1660

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


Purdue University

6. Kambatla, Karthik Shashank. Methods to Improve Applicability and Efficiency of Distributed Data-Centric Compute Frameworks.

Degree: PhD, Computer Science, 2016, Purdue University

 The success of modern applications depends on the insights they collect from their data repositories. Data repositories for such applications currently exceed exabytes and are… (more)

Subjects/Keywords: big data; distributed computing; distributed systems

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

APA (6th Edition):

Kambatla, K. S. (2016). Methods to Improve Applicability and Efficiency of Distributed Data-Centric Compute Frameworks. (Doctoral Dissertation). Purdue University. Retrieved from https://docs.lib.purdue.edu/open_access_dissertations/1379

Chicago Manual of Style (16th Edition):

Kambatla, Karthik Shashank. “Methods to Improve Applicability and Efficiency of Distributed Data-Centric Compute Frameworks.” 2016. Doctoral Dissertation, Purdue University. Accessed December 08, 2019. https://docs.lib.purdue.edu/open_access_dissertations/1379.

MLA Handbook (7th Edition):

Kambatla, Karthik Shashank. “Methods to Improve Applicability and Efficiency of Distributed Data-Centric Compute Frameworks.” 2016. Web. 08 Dec 2019.

Vancouver:

Kambatla KS. Methods to Improve Applicability and Efficiency of Distributed Data-Centric Compute Frameworks. [Internet] [Doctoral dissertation]. Purdue University; 2016. [cited 2019 Dec 08]. Available from: https://docs.lib.purdue.edu/open_access_dissertations/1379.

Council of Science Editors:

Kambatla KS. Methods to Improve Applicability and Efficiency of Distributed Data-Centric Compute Frameworks. [Doctoral Dissertation]. Purdue University; 2016. Available from: https://docs.lib.purdue.edu/open_access_dissertations/1379

7. Zareian, Saeed. Toward Autonomic Data-Oriented Scalability in Cloud Computing Environments.

Degree: MA -MA, Information Systems and Technology, 2016, York University

 The applications deployed in modern data centers are highly diverse in terms of architecture and performance needs. It is a challenge to provide consistent services… (more)

Subjects/Keywords: Computer science; Cloud Computing; Big Data

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

APA (6th Edition):

Zareian, S. (2016). Toward Autonomic Data-Oriented Scalability in Cloud Computing Environments. (Masters Thesis). York University. Retrieved from http://hdl.handle.net/10315/32145

Chicago Manual of Style (16th Edition):

Zareian, Saeed. “Toward Autonomic Data-Oriented Scalability in Cloud Computing Environments.” 2016. Masters Thesis, York University. Accessed December 08, 2019. http://hdl.handle.net/10315/32145.

MLA Handbook (7th Edition):

Zareian, Saeed. “Toward Autonomic Data-Oriented Scalability in Cloud Computing Environments.” 2016. Web. 08 Dec 2019.

Vancouver:

Zareian S. Toward Autonomic Data-Oriented Scalability in Cloud Computing Environments. [Internet] [Masters thesis]. York University; 2016. [cited 2019 Dec 08]. Available from: http://hdl.handle.net/10315/32145.

Council of Science Editors:

Zareian S. Toward Autonomic Data-Oriented Scalability in Cloud Computing Environments. [Masters Thesis]. York University; 2016. Available from: http://hdl.handle.net/10315/32145


University of New South Wales

8. Wu, Dongyao. Big Data Processing on Arbitrarily Distributed Dataset.

Degree: Computer Science & Engineering, 2017, University of New South Wales

 Over the past years, frameworks such as MapReduce and Spark have been introduced to ease the task of developing big data programsand applications. These frameworks… (more)

Subjects/Keywords: Distributed Systems; Big Data; Data Analytics; Data Provenance; Cloud Computing

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

APA (6th Edition):

Wu, D. (2017). Big Data Processing on Arbitrarily Distributed Dataset. (Doctoral Dissertation). University of New South Wales. Retrieved from http://handle.unsw.edu.au/1959.4/57957 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:45191/SOURCE02?view=true

Chicago Manual of Style (16th Edition):

Wu, Dongyao. “Big Data Processing on Arbitrarily Distributed Dataset.” 2017. Doctoral Dissertation, University of New South Wales. Accessed December 08, 2019. http://handle.unsw.edu.au/1959.4/57957 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:45191/SOURCE02?view=true.

MLA Handbook (7th Edition):

Wu, Dongyao. “Big Data Processing on Arbitrarily Distributed Dataset.” 2017. Web. 08 Dec 2019.

Vancouver:

Wu D. Big Data Processing on Arbitrarily Distributed Dataset. [Internet] [Doctoral dissertation]. University of New South Wales; 2017. [cited 2019 Dec 08]. Available from: http://handle.unsw.edu.au/1959.4/57957 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:45191/SOURCE02?view=true.

Council of Science Editors:

Wu D. Big Data Processing on Arbitrarily Distributed Dataset. [Doctoral Dissertation]. University of New South Wales; 2017. Available from: http://handle.unsw.edu.au/1959.4/57957 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:45191/SOURCE02?view=true


NSYSU

9. Guo, Bao-Ren. Implementation of MPI Cloud Computing Platform Build upon System Kernel Environment.

Degree: Master, Electrical Engineering, 2016, NSYSU

 With the age of Big Data coming, the three defining characteristics of Big Data – Volume, variety and Velocity, make Cloud Computing facing new challenges. In… (more)

Subjects/Keywords: MPI; Big Data; Kernel Driver; Distributed Computing Cluster; Cloud Computing

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

APA (6th Edition):

Guo, B. (2016). Implementation of MPI Cloud Computing Platform Build upon System Kernel Environment. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0721116-203822

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):

Guo, Bao-Ren. “Implementation of MPI Cloud Computing Platform Build upon System Kernel Environment.” 2016. Thesis, NSYSU. Accessed December 08, 2019. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0721116-203822.

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

MLA Handbook (7th Edition):

Guo, Bao-Ren. “Implementation of MPI Cloud Computing Platform Build upon System Kernel Environment.” 2016. Web. 08 Dec 2019.

Vancouver:

Guo B. Implementation of MPI Cloud Computing Platform Build upon System Kernel Environment. [Internet] [Thesis]. NSYSU; 2016. [cited 2019 Dec 08]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0721116-203822.

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

Council of Science Editors:

Guo B. Implementation of MPI Cloud Computing Platform Build upon System Kernel Environment. [Thesis]. NSYSU; 2016. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0721116-203822

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


The Ohio State University

10. Bicer, Tekin. Supporting Data-Intensive Scientic Computing on Bandwidth and Space Constrained Environments.

Degree: PhD, Computer Science and Engineering, 2014, The Ohio State University

 Scientific applications, simulations and instruments generate massive amount of data. This data does not only contribute to the already existing scientific areas, but it also… (more)

Subjects/Keywords: Computer Science; Data-Intensive Computing; Map-Reduce; Cloud Computing; Big Data; Scientific Data Management; Compression

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

APA (6th Edition):

Bicer, T. (2014). Supporting Data-Intensive Scientic Computing on Bandwidth and Space Constrained Environments. (Doctoral Dissertation). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1397749544

Chicago Manual of Style (16th Edition):

Bicer, Tekin. “Supporting Data-Intensive Scientic Computing on Bandwidth and Space Constrained Environments.” 2014. Doctoral Dissertation, The Ohio State University. Accessed December 08, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1397749544.

MLA Handbook (7th Edition):

Bicer, Tekin. “Supporting Data-Intensive Scientic Computing on Bandwidth and Space Constrained Environments.” 2014. Web. 08 Dec 2019.

Vancouver:

Bicer T. Supporting Data-Intensive Scientic Computing on Bandwidth and Space Constrained Environments. [Internet] [Doctoral dissertation]. The Ohio State University; 2014. [cited 2019 Dec 08]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1397749544.

Council of Science Editors:

Bicer T. Supporting Data-Intensive Scientic Computing on Bandwidth and Space Constrained Environments. [Doctoral Dissertation]. The Ohio State University; 2014. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1397749544


Temple University

11. Huang, Xueli. Achieving Data Privacy and Security in Cloud.

Degree: PhD, 2016, Temple University

Computer and Information Science

The growing concerns in term of the privacy of data stored in public cloud have restrained the widespread adoption of cloud… (more)

Subjects/Keywords: Computer science;

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

APA (6th Edition):

Huang, X. (2016). Achieving Data Privacy and Security in Cloud. (Doctoral Dissertation). Temple University. Retrieved from http://digital.library.temple.edu/u?/p245801coll10,372805

Chicago Manual of Style (16th Edition):

Huang, Xueli. “Achieving Data Privacy and Security in Cloud.” 2016. Doctoral Dissertation, Temple University. Accessed December 08, 2019. http://digital.library.temple.edu/u?/p245801coll10,372805.

MLA Handbook (7th Edition):

Huang, Xueli. “Achieving Data Privacy and Security in Cloud.” 2016. Web. 08 Dec 2019.

Vancouver:

Huang X. Achieving Data Privacy and Security in Cloud. [Internet] [Doctoral dissertation]. Temple University; 2016. [cited 2019 Dec 08]. Available from: http://digital.library.temple.edu/u?/p245801coll10,372805.

Council of Science Editors:

Huang X. Achieving Data Privacy and Security in Cloud. [Doctoral Dissertation]. Temple University; 2016. Available from: http://digital.library.temple.edu/u?/p245801coll10,372805


Georgia Tech

12. Zhou, Yang. Innovative mining, processing, and application of big graphs.

Degree: PhD, Computer Science, 2017, Georgia Tech

 With continued advances in science and technology, big graph (or network) data, such as World Wide Web, social networks, academic collaboration networks, transportation networks, telecommunication… (more)

Subjects/Keywords: Big data; Data mining; Parallel and distributed computing; Machine learning; Databases

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

APA (6th Edition):

Zhou, Y. (2017). Innovative mining, processing, and application of big graphs. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/59173

Chicago Manual of Style (16th Edition):

Zhou, Yang. “Innovative mining, processing, and application of big graphs.” 2017. Doctoral Dissertation, Georgia Tech. Accessed December 08, 2019. http://hdl.handle.net/1853/59173.

MLA Handbook (7th Edition):

Zhou, Yang. “Innovative mining, processing, and application of big graphs.” 2017. Web. 08 Dec 2019.

Vancouver:

Zhou Y. Innovative mining, processing, and application of big graphs. [Internet] [Doctoral dissertation]. Georgia Tech; 2017. [cited 2019 Dec 08]. Available from: http://hdl.handle.net/1853/59173.

Council of Science Editors:

Zhou Y. Innovative mining, processing, and application of big graphs. [Doctoral Dissertation]. Georgia Tech; 2017. Available from: http://hdl.handle.net/1853/59173


Rhodes University

13. Sweeney, Michael John. A framework for scoring and tagging NetFlow data.

Degree: Faculty of Science, Computer Science, 2019, Rhodes University

 With the increase in link speeds and the growth of the Internet, the volume of NetFlow data generated has increased significantly over time and processing… (more)

Subjects/Keywords: Big data; Electronic data processing; High performance computing

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

APA (6th Edition):

Sweeney, M. J. (2019). A framework for scoring and tagging NetFlow data. (Thesis). Rhodes University. Retrieved from http://hdl.handle.net/10962/65022

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):

Sweeney, Michael John. “A framework for scoring and tagging NetFlow data.” 2019. Thesis, Rhodes University. Accessed December 08, 2019. http://hdl.handle.net/10962/65022.

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

MLA Handbook (7th Edition):

Sweeney, Michael John. “A framework for scoring and tagging NetFlow data.” 2019. Web. 08 Dec 2019.

Vancouver:

Sweeney MJ. A framework for scoring and tagging NetFlow data. [Internet] [Thesis]. Rhodes University; 2019. [cited 2019 Dec 08]. Available from: http://hdl.handle.net/10962/65022.

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

Council of Science Editors:

Sweeney MJ. A framework for scoring and tagging NetFlow data. [Thesis]. Rhodes University; 2019. Available from: http://hdl.handle.net/10962/65022

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


Georgia State University

14. Casturi, Narasimharao V. Enterprise Data Mining & Machine Learning Framework on Cloud Computing for Investment Platforms.

Degree: PhD, Computer Science, 2019, Georgia State University

  Machine Learning and Data Mining are two key components in decision making systems which can provide valuable in-sights quickly into huge data set. Turning… (more)

Subjects/Keywords: FinTech; Big Data; Data Mining; Machine Learning; Cloud Computing; Enterprise Architecture

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APA (6th Edition):

Casturi, N. V. (2019). Enterprise Data Mining & Machine Learning Framework on Cloud Computing for Investment Platforms. (Doctoral Dissertation). Georgia State University. Retrieved from https://scholarworks.gsu.edu/cs_diss/150

Chicago Manual of Style (16th Edition):

Casturi, Narasimharao V. “Enterprise Data Mining & Machine Learning Framework on Cloud Computing for Investment Platforms.” 2019. Doctoral Dissertation, Georgia State University. Accessed December 08, 2019. https://scholarworks.gsu.edu/cs_diss/150.

MLA Handbook (7th Edition):

Casturi, Narasimharao V. “Enterprise Data Mining & Machine Learning Framework on Cloud Computing for Investment Platforms.” 2019. Web. 08 Dec 2019.

Vancouver:

Casturi NV. Enterprise Data Mining & Machine Learning Framework on Cloud Computing for Investment Platforms. [Internet] [Doctoral dissertation]. Georgia State University; 2019. [cited 2019 Dec 08]. Available from: https://scholarworks.gsu.edu/cs_diss/150.

Council of Science Editors:

Casturi NV. Enterprise Data Mining & Machine Learning Framework on Cloud Computing for Investment Platforms. [Doctoral Dissertation]. Georgia State University; 2019. Available from: https://scholarworks.gsu.edu/cs_diss/150


Brunel University

15. Suthakar, Uthayanath. A scalable data store and analytic platform for real-time monitoring of data-intensive scientific infrastructure.

Degree: PhD, 2017, Brunel University

 Monitoring data-intensive scientific infrastructures in real-time such as jobs, data transfers, and hardware failures is vital for efficient operation. Due to the high volume and… (more)

Subjects/Keywords: Big data; Data science; Distributed system; Lambda Architecture; Parallel computing

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APA (6th Edition):

Suthakar, U. (2017). A scalable data store and analytic platform for real-time monitoring of data-intensive scientific infrastructure. (Doctoral Dissertation). Brunel University. Retrieved from http://bura.brunel.ac.uk/handle/2438/15788 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.764857

Chicago Manual of Style (16th Edition):

Suthakar, Uthayanath. “A scalable data store and analytic platform for real-time monitoring of data-intensive scientific infrastructure.” 2017. Doctoral Dissertation, Brunel University. Accessed December 08, 2019. http://bura.brunel.ac.uk/handle/2438/15788 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.764857.

MLA Handbook (7th Edition):

Suthakar, Uthayanath. “A scalable data store and analytic platform for real-time monitoring of data-intensive scientific infrastructure.” 2017. Web. 08 Dec 2019.

Vancouver:

Suthakar U. A scalable data store and analytic platform for real-time monitoring of data-intensive scientific infrastructure. [Internet] [Doctoral dissertation]. Brunel University; 2017. [cited 2019 Dec 08]. Available from: http://bura.brunel.ac.uk/handle/2438/15788 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.764857.

Council of Science Editors:

Suthakar U. A scalable data store and analytic platform for real-time monitoring of data-intensive scientific infrastructure. [Doctoral Dissertation]. Brunel University; 2017. Available from: http://bura.brunel.ac.uk/handle/2438/15788 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.764857


New Jersey Institute of Technology

16. Shu, Tong. Performance optimization and energy efficiency of big-data computing workflows.

Degree: PhD, Computer Science, 2017, New Jersey Institute of Technology

  Next-generation e-science is producing colossal amounts of data, now frequently termed as Big Data, on the order of terabyte at present and petabyte or… (more)

Subjects/Keywords: Big data; Scientific workflow; Green computing; Cloud computing; Parallel computing; Map reduce; Computer Sciences

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

APA (6th Edition):

Shu, T. (2017). Performance optimization and energy efficiency of big-data computing workflows. (Doctoral Dissertation). New Jersey Institute of Technology. Retrieved from https://digitalcommons.njit.edu/dissertations/41

Chicago Manual of Style (16th Edition):

Shu, Tong. “Performance optimization and energy efficiency of big-data computing workflows.” 2017. Doctoral Dissertation, New Jersey Institute of Technology. Accessed December 08, 2019. https://digitalcommons.njit.edu/dissertations/41.

MLA Handbook (7th Edition):

Shu, Tong. “Performance optimization and energy efficiency of big-data computing workflows.” 2017. Web. 08 Dec 2019.

Vancouver:

Shu T. Performance optimization and energy efficiency of big-data computing workflows. [Internet] [Doctoral dissertation]. New Jersey Institute of Technology; 2017. [cited 2019 Dec 08]. Available from: https://digitalcommons.njit.edu/dissertations/41.

Council of Science Editors:

Shu T. Performance optimization and energy efficiency of big-data computing workflows. [Doctoral Dissertation]. New Jersey Institute of Technology; 2017. Available from: https://digitalcommons.njit.edu/dissertations/41


Clemson University

17. Xuan, Pengfei. Accelerating Big Data Analytics on Traditional High-Performance Computing Systems Using Two-Level Storage.

Degree: PhD, School of Computing, 2016, Clemson University

  High-performance Computing (HPC) clusters, which consist of a large number of compute nodes, have traditionally been widely employed in industry and academia to run… (more)

Subjects/Keywords: Big Data Analytics; Data-intensive Computing; HPC; In-memory Computing; Parallel File System

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

APA (6th Edition):

Xuan, P. (2016). Accelerating Big Data Analytics on Traditional High-Performance Computing Systems Using Two-Level Storage. (Doctoral Dissertation). Clemson University. Retrieved from https://tigerprints.clemson.edu/all_dissertations/2318

Chicago Manual of Style (16th Edition):

Xuan, Pengfei. “Accelerating Big Data Analytics on Traditional High-Performance Computing Systems Using Two-Level Storage.” 2016. Doctoral Dissertation, Clemson University. Accessed December 08, 2019. https://tigerprints.clemson.edu/all_dissertations/2318.

MLA Handbook (7th Edition):

Xuan, Pengfei. “Accelerating Big Data Analytics on Traditional High-Performance Computing Systems Using Two-Level Storage.” 2016. Web. 08 Dec 2019.

Vancouver:

Xuan P. Accelerating Big Data Analytics on Traditional High-Performance Computing Systems Using Two-Level Storage. [Internet] [Doctoral dissertation]. Clemson University; 2016. [cited 2019 Dec 08]. Available from: https://tigerprints.clemson.edu/all_dissertations/2318.

Council of Science Editors:

Xuan P. Accelerating Big Data Analytics on Traditional High-Performance Computing Systems Using Two-Level Storage. [Doctoral Dissertation]. Clemson University; 2016. Available from: https://tigerprints.clemson.edu/all_dissertations/2318


KTH

18. Moré, Andre. HopsWorks : A project-based access control model for Hadoop.

Degree: Information and Communication Technology (ICT), 2015, KTH

The growth in the global data gathering capacity is producing a vast amount of data which is getting vaster at an increasingly faster rate.… (more)

Subjects/Keywords: Hops; HopsWorks; Hadoop; DataSets; Big Data; Distributed Computing; Hops; HopsWorks; Hadoop; DataSets; Big Data; Distributed Computing

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

APA (6th Edition):

Moré, A. (2015). HopsWorks : A project-based access control model for Hadoop. (Thesis). KTH. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-175742

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):

Moré, Andre. “HopsWorks : A project-based access control model for Hadoop.” 2015. Thesis, KTH. Accessed December 08, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-175742.

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

MLA Handbook (7th Edition):

Moré, Andre. “HopsWorks : A project-based access control model for Hadoop.” 2015. Web. 08 Dec 2019.

Vancouver:

Moré A. HopsWorks : A project-based access control model for Hadoop. [Internet] [Thesis]. KTH; 2015. [cited 2019 Dec 08]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-175742.

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

Council of Science Editors:

Moré A. HopsWorks : A project-based access control model for Hadoop. [Thesis]. KTH; 2015. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-175742

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

19. Song, Ge. Méthodes parallèles pour le traitement des flux de données continus : Parallel and continuous join processing for data stream.

Degree: Docteur es, Informatique, 2016, Paris Saclay

Nous vivons dans un monde où une grande quantité de données est généré en continu. Par exemple, quand on fait une recherche sur Google, quand… (more)

Subjects/Keywords: Big Data; Flux de Données; Calculation en Parallel; Exploration de Données; Big Data; Data Stream; Parallel Computing; Data Mining

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

APA (6th Edition):

Song, G. (2016). Méthodes parallèles pour le traitement des flux de données continus : Parallel and continuous join processing for data stream. (Doctoral Dissertation). Paris Saclay. Retrieved from http://www.theses.fr/2016SACLC059

Chicago Manual of Style (16th Edition):

Song, Ge. “Méthodes parallèles pour le traitement des flux de données continus : Parallel and continuous join processing for data stream.” 2016. Doctoral Dissertation, Paris Saclay. Accessed December 08, 2019. http://www.theses.fr/2016SACLC059.

MLA Handbook (7th Edition):

Song, Ge. “Méthodes parallèles pour le traitement des flux de données continus : Parallel and continuous join processing for data stream.” 2016. Web. 08 Dec 2019.

Vancouver:

Song G. Méthodes parallèles pour le traitement des flux de données continus : Parallel and continuous join processing for data stream. [Internet] [Doctoral dissertation]. Paris Saclay; 2016. [cited 2019 Dec 08]. Available from: http://www.theses.fr/2016SACLC059.

Council of Science Editors:

Song G. Méthodes parallèles pour le traitement des flux de données continus : Parallel and continuous join processing for data stream. [Doctoral Dissertation]. Paris Saclay; 2016. Available from: http://www.theses.fr/2016SACLC059

20. Ribot, Stephane. Adoption of Big Data And Cloud Computing Technologies for Large Scale Mobile Traffic Analysis : L’adoption des technologies Big Data et Cloud Computing dans le cadre de l’analyse des données de trafic mobile.

Degree: Docteur es, Science de Gestion, 2016, Lyon

L’émergence des technologies Big Data et Cloud computing pour répondre à l’accroissement constant de la complexité et de la diversité des données constituent un nouvel… (more)

Subjects/Keywords: Big Data; Nuage Informatique; Adoption; Big Data; Cloud Computing; Task-Technology Fit; Data value chain; 000

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

APA (6th Edition):

Ribot, S. (2016). Adoption of Big Data And Cloud Computing Technologies for Large Scale Mobile Traffic Analysis : L’adoption des technologies Big Data et Cloud Computing dans le cadre de l’analyse des données de trafic mobile. (Doctoral Dissertation). Lyon. Retrieved from http://www.theses.fr/2016LYSE3049

Chicago Manual of Style (16th Edition):

Ribot, Stephane. “Adoption of Big Data And Cloud Computing Technologies for Large Scale Mobile Traffic Analysis : L’adoption des technologies Big Data et Cloud Computing dans le cadre de l’analyse des données de trafic mobile.” 2016. Doctoral Dissertation, Lyon. Accessed December 08, 2019. http://www.theses.fr/2016LYSE3049.

MLA Handbook (7th Edition):

Ribot, Stephane. “Adoption of Big Data And Cloud Computing Technologies for Large Scale Mobile Traffic Analysis : L’adoption des technologies Big Data et Cloud Computing dans le cadre de l’analyse des données de trafic mobile.” 2016. Web. 08 Dec 2019.

Vancouver:

Ribot S. Adoption of Big Data And Cloud Computing Technologies for Large Scale Mobile Traffic Analysis : L’adoption des technologies Big Data et Cloud Computing dans le cadre de l’analyse des données de trafic mobile. [Internet] [Doctoral dissertation]. Lyon; 2016. [cited 2019 Dec 08]. Available from: http://www.theses.fr/2016LYSE3049.

Council of Science Editors:

Ribot S. Adoption of Big Data And Cloud Computing Technologies for Large Scale Mobile Traffic Analysis : L’adoption des technologies Big Data et Cloud Computing dans le cadre de l’analyse des données de trafic mobile. [Doctoral Dissertation]. Lyon; 2016. Available from: http://www.theses.fr/2016LYSE3049

21. Ahlcrona, Felix. Sakernas Internet : En studie om vehicular fog computing påverkan i trafiken.

Degree: Informatics, 2018, University of Skövde

Framtidens fordon kommer vara väldigt annorlunda jämfört med dagens fordon. Stor del av förändringen kommer ske med hjälp av IoT. Världen kommer bli oerhört… (more)

Subjects/Keywords: IoT; big data; fog computing; vehicular fog computing; connected vehicles; Sakernas internet; big data; fog computing; vehicular fog computing; uppkopplade bilar; Information Systems; Systemvetenskap, informationssystem och informatik

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

APA (6th Edition):

Ahlcrona, F. (2018). Sakernas Internet : En studie om vehicular fog computing påverkan i trafiken. (Thesis). University of Skövde. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-15713

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):

Ahlcrona, Felix. “Sakernas Internet : En studie om vehicular fog computing påverkan i trafiken.” 2018. Thesis, University of Skövde. Accessed December 08, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-15713.

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

MLA Handbook (7th Edition):

Ahlcrona, Felix. “Sakernas Internet : En studie om vehicular fog computing påverkan i trafiken.” 2018. Web. 08 Dec 2019.

Vancouver:

Ahlcrona F. Sakernas Internet : En studie om vehicular fog computing påverkan i trafiken. [Internet] [Thesis]. University of Skövde; 2018. [cited 2019 Dec 08]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-15713.

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

Council of Science Editors:

Ahlcrona F. Sakernas Internet : En studie om vehicular fog computing påverkan i trafiken. [Thesis]. University of Skövde; 2018. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-15713

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

22. Rabah, Mazouzi. Approches collaboratives pour la classification des données complexes : Collaborative approaches for complex data classification.

Degree: Docteur es, Informatique, 2016, Paris 8

La présente thèse s'intéresse à la classification collaborative dans un contexte de données complexes, notamment dans le cadre du Big Data, nous nous sommes penchés… (more)

Subjects/Keywords: Classification; Ensemble de classifieurs; Big data; Cloud-computing; Diversité

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

APA (6th Edition):

Rabah, M. (2016). Approches collaboratives pour la classification des données complexes : Collaborative approaches for complex data classification. (Doctoral Dissertation). Paris 8. Retrieved from http://www.theses.fr/2016PA080079

Chicago Manual of Style (16th Edition):

Rabah, Mazouzi. “Approches collaboratives pour la classification des données complexes : Collaborative approaches for complex data classification.” 2016. Doctoral Dissertation, Paris 8. Accessed December 08, 2019. http://www.theses.fr/2016PA080079.

MLA Handbook (7th Edition):

Rabah, Mazouzi. “Approches collaboratives pour la classification des données complexes : Collaborative approaches for complex data classification.” 2016. Web. 08 Dec 2019.

Vancouver:

Rabah M. Approches collaboratives pour la classification des données complexes : Collaborative approaches for complex data classification. [Internet] [Doctoral dissertation]. Paris 8; 2016. [cited 2019 Dec 08]. Available from: http://www.theses.fr/2016PA080079.

Council of Science Editors:

Rabah M. Approches collaboratives pour la classification des données complexes : Collaborative approaches for complex data classification. [Doctoral Dissertation]. Paris 8; 2016. Available from: http://www.theses.fr/2016PA080079


UCLA

23. Tetali, Sai Deep. Program Analyses for Cloud Computations.

Degree: Computer Science, 2015, UCLA

 Cloud computing has become an essential part of our computing infrastructure. In this model, data and programs are hosted in (often third-party) data centers that… (more)

Subjects/Keywords: Computer science; Big Data; Cloud Computing; Programming Analysis; Programming Languages

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

APA (6th Edition):

Tetali, S. D. (2015). Program Analyses for Cloud Computations. (Thesis). UCLA. Retrieved from http://www.escholarship.org/uc/item/0nh2k2c7

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):

Tetali, Sai Deep. “Program Analyses for Cloud Computations.” 2015. Thesis, UCLA. Accessed December 08, 2019. http://www.escholarship.org/uc/item/0nh2k2c7.

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

MLA Handbook (7th Edition):

Tetali, Sai Deep. “Program Analyses for Cloud Computations.” 2015. Web. 08 Dec 2019.

Vancouver:

Tetali SD. Program Analyses for Cloud Computations. [Internet] [Thesis]. UCLA; 2015. [cited 2019 Dec 08]. Available from: http://www.escholarship.org/uc/item/0nh2k2c7.

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

Council of Science Editors:

Tetali SD. Program Analyses for Cloud Computations. [Thesis]. UCLA; 2015. Available from: http://www.escholarship.org/uc/item/0nh2k2c7

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


Universidade Nova

24. Domingos, João Nuno Silva Tabar. On the cloud deployment of a session abstraction for service/data aggregation.

Degree: 2013, Universidade Nova

Dissertação para obtenção do Grau de Mestre em Engenharia Informática

The global cyber-infrastructure comprehends a growing number of resources, spanning over several abstraction layers. These… (more)

Subjects/Keywords: Big data; Cloud computing; Sessions; Dynamic reconfigurations; Mobile platforms

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

APA (6th Edition):

Domingos, J. N. S. T. (2013). On the cloud deployment of a session abstraction for service/data aggregation. (Thesis). Universidade Nova. Retrieved from http://www.rcaap.pt/detail.jsp?id=oai:run.unl.pt:10362/9923

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):

Domingos, João Nuno Silva Tabar. “On the cloud deployment of a session abstraction for service/data aggregation.” 2013. Thesis, Universidade Nova. Accessed December 08, 2019. http://www.rcaap.pt/detail.jsp?id=oai:run.unl.pt:10362/9923.

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

MLA Handbook (7th Edition):

Domingos, João Nuno Silva Tabar. “On the cloud deployment of a session abstraction for service/data aggregation.” 2013. Web. 08 Dec 2019.

Vancouver:

Domingos JNST. On the cloud deployment of a session abstraction for service/data aggregation. [Internet] [Thesis]. Universidade Nova; 2013. [cited 2019 Dec 08]. Available from: http://www.rcaap.pt/detail.jsp?id=oai:run.unl.pt:10362/9923.

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

Council of Science Editors:

Domingos JNST. On the cloud deployment of a session abstraction for service/data aggregation. [Thesis]. Universidade Nova; 2013. Available from: http://www.rcaap.pt/detail.jsp?id=oai:run.unl.pt:10362/9923

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


Virginia Tech

25. Uliana, David Christopher. FPGA-Based Accelerator Development for Non-Engineers.

Degree: MS, Electrical and Computer Engineering, 2014, Virginia Tech

 In today's world of big-data computing, access to massive, complex data sets has reached an unprecedented level, and the task of intelligently processing such data(more)

Subjects/Keywords: Big-data; HPC; FPGA; Heterogeneous Computing; Life Sciences

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

APA (6th Edition):

Uliana, D. C. (2014). FPGA-Based Accelerator Development for Non-Engineers. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/78091

Chicago Manual of Style (16th Edition):

Uliana, David Christopher. “FPGA-Based Accelerator Development for Non-Engineers.” 2014. Masters Thesis, Virginia Tech. Accessed December 08, 2019. http://hdl.handle.net/10919/78091.

MLA Handbook (7th Edition):

Uliana, David Christopher. “FPGA-Based Accelerator Development for Non-Engineers.” 2014. Web. 08 Dec 2019.

Vancouver:

Uliana DC. FPGA-Based Accelerator Development for Non-Engineers. [Internet] [Masters thesis]. Virginia Tech; 2014. [cited 2019 Dec 08]. Available from: http://hdl.handle.net/10919/78091.

Council of Science Editors:

Uliana DC. FPGA-Based Accelerator Development for Non-Engineers. [Masters Thesis]. Virginia Tech; 2014. Available from: http://hdl.handle.net/10919/78091


Universidad Nacional de La Plata

26. De Luca, Julián. Incidencia de idiomas populares en la lengua española con Big Data: análisis masivo de datos mediante Amazon Elastic MapReduce y Google N-grams.

Degree: 2016, Universidad Nacional de La Plata

La presente tesina analiza, diseña e implementa una solución para el problema de la detección de neologismos y extranjerismos en la lengua Española. Para este… (more)

Subjects/Keywords: Ciencias Informáticas; big data; cloud computing; neología; extranjerismos

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

APA (6th Edition):

De Luca, J. (2016). Incidencia de idiomas populares en la lengua española con Big Data: análisis masivo de datos mediante Amazon Elastic MapReduce y Google N-grams. (Thesis). Universidad Nacional de La Plata. Retrieved from http://hdl.handle.net/10915/59489

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):

De Luca, Julián. “Incidencia de idiomas populares en la lengua española con Big Data: análisis masivo de datos mediante Amazon Elastic MapReduce y Google N-grams.” 2016. Thesis, Universidad Nacional de La Plata. Accessed December 08, 2019. http://hdl.handle.net/10915/59489.

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

MLA Handbook (7th Edition):

De Luca, Julián. “Incidencia de idiomas populares en la lengua española con Big Data: análisis masivo de datos mediante Amazon Elastic MapReduce y Google N-grams.” 2016. Web. 08 Dec 2019.

Vancouver:

De Luca J. Incidencia de idiomas populares en la lengua española con Big Data: análisis masivo de datos mediante Amazon Elastic MapReduce y Google N-grams. [Internet] [Thesis]. Universidad Nacional de La Plata; 2016. [cited 2019 Dec 08]. Available from: http://hdl.handle.net/10915/59489.

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

Council of Science Editors:

De Luca J. Incidencia de idiomas populares en la lengua española con Big Data: análisis masivo de datos mediante Amazon Elastic MapReduce y Google N-grams. [Thesis]. Universidad Nacional de La Plata; 2016. Available from: http://hdl.handle.net/10915/59489

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


University of Notre Dame

27. Olivia Choudhury. Expediting Analysis and Improving Fidelity of Big Data Genomics</h1>.

Degree: PhD, Computer Science and Engineering, 2017, University of Notre Dame

  Genomics, or the study of genome-derived data, has had widespread impact in applications including medicine, forensic science, human evolution, environmental science, and social science.… (more)

Subjects/Keywords: Big Data; Genomics; Cloud Computing; Bioinformatics; Computational Biology; Machine Learning

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

APA (6th Edition):

Choudhury, O. (2017). Expediting Analysis and Improving Fidelity of Big Data Genomics</h1>. (Doctoral Dissertation). University of Notre Dame. Retrieved from https://curate.nd.edu/show/zp38w953f1h

Chicago Manual of Style (16th Edition):

Choudhury, Olivia. “Expediting Analysis and Improving Fidelity of Big Data Genomics</h1>.” 2017. Doctoral Dissertation, University of Notre Dame. Accessed December 08, 2019. https://curate.nd.edu/show/zp38w953f1h.

MLA Handbook (7th Edition):

Choudhury, Olivia. “Expediting Analysis and Improving Fidelity of Big Data Genomics</h1>.” 2017. Web. 08 Dec 2019.

Vancouver:

Choudhury O. Expediting Analysis and Improving Fidelity of Big Data Genomics</h1>. [Internet] [Doctoral dissertation]. University of Notre Dame; 2017. [cited 2019 Dec 08]. Available from: https://curate.nd.edu/show/zp38w953f1h.

Council of Science Editors:

Choudhury O. Expediting Analysis and Improving Fidelity of Big Data Genomics</h1>. [Doctoral Dissertation]. University of Notre Dame; 2017. Available from: https://curate.nd.edu/show/zp38w953f1h


University of Western Ontario

28. Nascimento de Aguiar, Rafael Felipe. Spatiotemporal Forecasting At Scale.

Degree: 2019, University of Western Ontario

 Spatiotemporal forecasting can be described as predicting the future value of a variable given when and where it will happen. This type of forecasting task… (more)

Subjects/Keywords: Spatiotemporal; Forecasting; Big Data; Distributed Computing; Ensemble Learning; Software Engineering

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

APA (6th Edition):

Nascimento de Aguiar, R. F. (2019). Spatiotemporal Forecasting At Scale. (Thesis). University of Western Ontario. Retrieved from https://ir.lib.uwo.ca/etd/6316

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):

Nascimento de Aguiar, Rafael Felipe. “Spatiotemporal Forecasting At Scale.” 2019. Thesis, University of Western Ontario. Accessed December 08, 2019. https://ir.lib.uwo.ca/etd/6316.

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

MLA Handbook (7th Edition):

Nascimento de Aguiar, Rafael Felipe. “Spatiotemporal Forecasting At Scale.” 2019. Web. 08 Dec 2019.

Vancouver:

Nascimento de Aguiar RF. Spatiotemporal Forecasting At Scale. [Internet] [Thesis]. University of Western Ontario; 2019. [cited 2019 Dec 08]. Available from: https://ir.lib.uwo.ca/etd/6316.

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

Council of Science Editors:

Nascimento de Aguiar RF. Spatiotemporal Forecasting At Scale. [Thesis]. University of Western Ontario; 2019. Available from: https://ir.lib.uwo.ca/etd/6316

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


University of Sydney

29. Khelghatdoust, Mansour. Scalable and Distributed Resource Management Protocols for Cloud and Big Data Clusters .

Degree: 2018, University of Sydney

 Cloud data centers require an operating system to manage resources and satisfy operational requirements and management objectives. The growth of popularity in cloud services causes… (more)

Subjects/Keywords: Resource Management; Scalability; Big data; Cloud Computing; Load Balancing; Scheduling; Consolidate

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

APA (6th Edition):

Khelghatdoust, M. (2018). Scalable and Distributed Resource Management Protocols for Cloud and Big Data Clusters . (Thesis). University of Sydney. Retrieved from http://hdl.handle.net/2123/19703

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):

Khelghatdoust, Mansour. “Scalable and Distributed Resource Management Protocols for Cloud and Big Data Clusters .” 2018. Thesis, University of Sydney. Accessed December 08, 2019. http://hdl.handle.net/2123/19703.

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

MLA Handbook (7th Edition):

Khelghatdoust, Mansour. “Scalable and Distributed Resource Management Protocols for Cloud and Big Data Clusters .” 2018. Web. 08 Dec 2019.

Vancouver:

Khelghatdoust M. Scalable and Distributed Resource Management Protocols for Cloud and Big Data Clusters . [Internet] [Thesis]. University of Sydney; 2018. [cited 2019 Dec 08]. Available from: http://hdl.handle.net/2123/19703.

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

Council of Science Editors:

Khelghatdoust M. Scalable and Distributed Resource Management Protocols for Cloud and Big Data Clusters . [Thesis]. University of Sydney; 2018. Available from: http://hdl.handle.net/2123/19703

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


Purdue University

30. Mohamed, Ahmed. A Cost-Effective Cloud-Based System for Analyzing Big Real-Time Visual Data From Thousands of Network Cameras.

Degree: PhD, Electrical and Computer Engineering, 2016, Purdue University

 Thousands of network cameras stream public real-time visual data from different environments, such as streets, shopping malls, and natural scenes. The big visual data from… (more)

Subjects/Keywords: Big Data; Cloud Computing; Computer Vision; Network Cameras; Resource Management

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

APA (6th Edition):

Mohamed, A. (2016). A Cost-Effective Cloud-Based System for Analyzing Big Real-Time Visual Data From Thousands of Network Cameras. (Doctoral Dissertation). Purdue University. Retrieved from https://docs.lib.purdue.edu/open_access_dissertations/1370

Chicago Manual of Style (16th Edition):

Mohamed, Ahmed. “A Cost-Effective Cloud-Based System for Analyzing Big Real-Time Visual Data From Thousands of Network Cameras.” 2016. Doctoral Dissertation, Purdue University. Accessed December 08, 2019. https://docs.lib.purdue.edu/open_access_dissertations/1370.

MLA Handbook (7th Edition):

Mohamed, Ahmed. “A Cost-Effective Cloud-Based System for Analyzing Big Real-Time Visual Data From Thousands of Network Cameras.” 2016. Web. 08 Dec 2019.

Vancouver:

Mohamed A. A Cost-Effective Cloud-Based System for Analyzing Big Real-Time Visual Data From Thousands of Network Cameras. [Internet] [Doctoral dissertation]. Purdue University; 2016. [cited 2019 Dec 08]. Available from: https://docs.lib.purdue.edu/open_access_dissertations/1370.

Council of Science Editors:

Mohamed A. A Cost-Effective Cloud-Based System for Analyzing Big Real-Time Visual Data From Thousands of Network Cameras. [Doctoral Dissertation]. Purdue University; 2016. Available from: https://docs.lib.purdue.edu/open_access_dissertations/1370

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