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You searched for subject:(Approximate Query Processing). Showing records 1 – 7 of 7 total matches.

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UCLA

1. ZENG, KAI. Approximation and Search Optimization on Massive Data Bases and Data Streams.

Degree: Computer Science, 2014, UCLA

 A fast response is critical in many data-intensive applications, including knowledge discovery analytics on big data, and queries searching for complex patterns in sequences, data… (more)

Subjects/Keywords: Computer science; Approximate Query Processing; Bootstrap; Complex Event Processing; Error Estimation; Visibly Pushdown Automata

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

APA (6th Edition):

ZENG, K. (2014). Approximation and Search Optimization on Massive Data Bases and Data Streams. (Thesis). UCLA. Retrieved from http://www.escholarship.org/uc/item/4pv2n0vs

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):

ZENG, KAI. “Approximation and Search Optimization on Massive Data Bases and Data Streams.” 2014. Thesis, UCLA. Accessed September 22, 2019. http://www.escholarship.org/uc/item/4pv2n0vs.

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

MLA Handbook (7th Edition):

ZENG, KAI. “Approximation and Search Optimization on Massive Data Bases and Data Streams.” 2014. Web. 22 Sep 2019.

Vancouver:

ZENG K. Approximation and Search Optimization on Massive Data Bases and Data Streams. [Internet] [Thesis]. UCLA; 2014. [cited 2019 Sep 22]. Available from: http://www.escholarship.org/uc/item/4pv2n0vs.

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

Council of Science Editors:

ZENG K. Approximation and Search Optimization on Massive Data Bases and Data Streams. [Thesis]. UCLA; 2014. Available from: http://www.escholarship.org/uc/item/4pv2n0vs

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


University of California – Berkeley

2. Agarwal, Sameer. Queries with Bounded Errors & Bounded Response Times on Very Large Data.

Degree: Computer Science, 2014, University of California – Berkeley

 Modern data analytics applications typically process massive amounts of data on clusters of tens, hundreds, or thousands of machines to support near-real-time decisions. The quantity… (more)

Subjects/Keywords: Computer science; Statistics; Approximate Query Processing; Bootstrap Diagnostics; Databases; Error Estimation; Sampling

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

APA (6th Edition):

Agarwal, S. (2014). Queries with Bounded Errors & Bounded Response Times on Very Large Data. (Thesis). University of California – Berkeley. Retrieved from http://www.escholarship.org/uc/item/58m3199x

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):

Agarwal, Sameer. “Queries with Bounded Errors & Bounded Response Times on Very Large Data.” 2014. Thesis, University of California – Berkeley. Accessed September 22, 2019. http://www.escholarship.org/uc/item/58m3199x.

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

MLA Handbook (7th Edition):

Agarwal, Sameer. “Queries with Bounded Errors & Bounded Response Times on Very Large Data.” 2014. Web. 22 Sep 2019.

Vancouver:

Agarwal S. Queries with Bounded Errors & Bounded Response Times on Very Large Data. [Internet] [Thesis]. University of California – Berkeley; 2014. [cited 2019 Sep 22]. Available from: http://www.escholarship.org/uc/item/58m3199x.

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

Council of Science Editors:

Agarwal S. Queries with Bounded Errors & Bounded Response Times on Very Large Data. [Thesis]. University of California – Berkeley; 2014. Available from: http://www.escholarship.org/uc/item/58m3199x

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


University of California – San Diego

3. Lin, Chunbin. Accelerating Analytic Queries on Compressed Data.

Degree: Computer Science, 2018, University of California – San Diego

 Data compression techniques (both lossless and lossy compression methods) are widely utilized in big data analytic applications in domains including health-care, transportation, and finance. The… (more)

Subjects/Keywords: Computer science; Analytic queries; Approximate query processing; Column database; Data compression; Time series database

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

APA (6th Edition):

Lin, C. (2018). Accelerating Analytic Queries on Compressed Data. (Thesis). University of California – San Diego. Retrieved from http://www.escholarship.org/uc/item/4rc3108c

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):

Lin, Chunbin. “Accelerating Analytic Queries on Compressed Data.” 2018. Thesis, University of California – San Diego. Accessed September 22, 2019. http://www.escholarship.org/uc/item/4rc3108c.

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

MLA Handbook (7th Edition):

Lin, Chunbin. “Accelerating Analytic Queries on Compressed Data.” 2018. Web. 22 Sep 2019.

Vancouver:

Lin C. Accelerating Analytic Queries on Compressed Data. [Internet] [Thesis]. University of California – San Diego; 2018. [cited 2019 Sep 22]. Available from: http://www.escholarship.org/uc/item/4rc3108c.

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

Council of Science Editors:

Lin C. Accelerating Analytic Queries on Compressed Data. [Thesis]. University of California – San Diego; 2018. Available from: http://www.escholarship.org/uc/item/4rc3108c

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


University of New South Wales

4. Wang, Yaoshu. Approximate Query Processing with Multiple Similarity Metrics.

Degree: Computer Science & Engineering, 2018, University of New South Wales

Approximate query processing based on multiple similarity metrics is prevalent and essential for many applications in the database area, such as information retrieval, data mining,… (more)

Subjects/Keywords: edit distance; approximate query processing; similarity metrics; Hamming distance; Jaro-Winkler distance

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

APA (6th Edition):

Wang, Y. (2018). Approximate Query Processing with Multiple Similarity Metrics. (Doctoral Dissertation). University of New South Wales. Retrieved from http://handle.unsw.edu.au/1959.4/61182 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:54579/SOURCE02?view=true

Chicago Manual of Style (16th Edition):

Wang, Yaoshu. “Approximate Query Processing with Multiple Similarity Metrics.” 2018. Doctoral Dissertation, University of New South Wales. Accessed September 22, 2019. http://handle.unsw.edu.au/1959.4/61182 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:54579/SOURCE02?view=true.

MLA Handbook (7th Edition):

Wang, Yaoshu. “Approximate Query Processing with Multiple Similarity Metrics.” 2018. Web. 22 Sep 2019.

Vancouver:

Wang Y. Approximate Query Processing with Multiple Similarity Metrics. [Internet] [Doctoral dissertation]. University of New South Wales; 2018. [cited 2019 Sep 22]. Available from: http://handle.unsw.edu.au/1959.4/61182 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:54579/SOURCE02?view=true.

Council of Science Editors:

Wang Y. Approximate Query Processing with Multiple Similarity Metrics. [Doctoral Dissertation]. University of New South Wales; 2018. Available from: http://handle.unsw.edu.au/1959.4/61182 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:54579/SOURCE02?view=true


EPFL

5. Sathe, Saket. Statistical Models for Querying and Managing Time-Series Data.

Degree: 2013, EPFL

Subjects/Keywords: time-series data management; statistical query processing; adaptive clustering; community sensing; probabilistic databases; ane transformations; view generation; approximate caching

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

APA (6th Edition):

Sathe, S. (2013). Statistical Models for Querying and Managing Time-Series Data. (Thesis). EPFL. Retrieved from http://infoscience.epfl.ch/record/187007

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):

Sathe, Saket. “Statistical Models for Querying and Managing Time-Series Data.” 2013. Thesis, EPFL. Accessed September 22, 2019. http://infoscience.epfl.ch/record/187007.

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

MLA Handbook (7th Edition):

Sathe, Saket. “Statistical Models for Querying and Managing Time-Series Data.” 2013. Web. 22 Sep 2019.

Vancouver:

Sathe S. Statistical Models for Querying and Managing Time-Series Data. [Internet] [Thesis]. EPFL; 2013. [cited 2019 Sep 22]. Available from: http://infoscience.epfl.ch/record/187007.

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

Council of Science Editors:

Sathe S. Statistical Models for Querying and Managing Time-Series Data. [Thesis]. EPFL; 2013. Available from: http://infoscience.epfl.ch/record/187007

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

6. Sanz Blasco, Ismael. Flexible techniques for heterogeneous XML data retrieval.

Degree: Departament d'Enginyeria i Ciència dels Computadors, 2007, Universitat Jaume I

 The progressive adoption of XML by new communities of users has motivated the appearance of applications that require the management of large and complex collections,… (more)

Subjects/Keywords: similarity; approximate query processing; heterogeneous data management; XML; Bases de dades / bases de datos / databases; 004

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

APA (6th Edition):

Sanz Blasco, I. (2007). Flexible techniques for heterogeneous XML data retrieval. (Thesis). Universitat Jaume I. Retrieved from http://hdl.handle.net/10803/10373

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):

Sanz Blasco, Ismael. “Flexible techniques for heterogeneous XML data retrieval.” 2007. Thesis, Universitat Jaume I. Accessed September 22, 2019. http://hdl.handle.net/10803/10373.

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

MLA Handbook (7th Edition):

Sanz Blasco, Ismael. “Flexible techniques for heterogeneous XML data retrieval.” 2007. Web. 22 Sep 2019.

Vancouver:

Sanz Blasco I. Flexible techniques for heterogeneous XML data retrieval. [Internet] [Thesis]. Universitat Jaume I; 2007. [cited 2019 Sep 22]. Available from: http://hdl.handle.net/10803/10373.

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

Council of Science Editors:

Sanz Blasco I. Flexible techniques for heterogeneous XML data retrieval. [Thesis]. Universitat Jaume I; 2007. Available from: http://hdl.handle.net/10803/10373

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

7. Park, Yongjoo. Fast Data Analytics by Learning.

Degree: PhD, Computer Science & Engineering, 2017, University of Michigan

 Today, we collect a large amount of data, and the volume of the data we collect is projected to grow faster than the growth of… (more)

Subjects/Keywords: big data analytics systems; database systems; approximate query processing; database learning; Computer Science; Engineering

…sufficient for real-time data analytics of big data. Approximate query processing (AQP)… …in an approximate query processing (AQP) context. We make the following… …dissertation. First, we report the speedup by Verdict, the approximate query processing system that… …existing approximate query processing system and the Verdict system implemented on top of the… …existing approximate query processing system. When the target error bound was 4%, Verdict could… 

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

APA (6th Edition):

Park, Y. (2017). Fast Data Analytics by Learning. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/138598

Chicago Manual of Style (16th Edition):

Park, Yongjoo. “Fast Data Analytics by Learning.” 2017. Doctoral Dissertation, University of Michigan. Accessed September 22, 2019. http://hdl.handle.net/2027.42/138598.

MLA Handbook (7th Edition):

Park, Yongjoo. “Fast Data Analytics by Learning.” 2017. Web. 22 Sep 2019.

Vancouver:

Park Y. Fast Data Analytics by Learning. [Internet] [Doctoral dissertation]. University of Michigan; 2017. [cited 2019 Sep 22]. Available from: http://hdl.handle.net/2027.42/138598.

Council of Science Editors:

Park Y. Fast Data Analytics by Learning. [Doctoral Dissertation]. University of Michigan; 2017. Available from: http://hdl.handle.net/2027.42/138598

.