大葉大學圖書館 |

Query processing over incomplete databases /

紀錄類型:	書目-電子資源 : Monograph/item
杜威分類號:	005.7565
書名/作者:	Query processing over incomplete databases // Yunjun Gao, Xiaoye Miao.
作者:	Gao, Yunjun,
其他作者:	Miao, Xiaoye,
出版者:	[San Rafael, California] : : Morgan & Claypool,, 2018.
面頁冊數:	1 PDF (xv, 106 pages) : : illustrations.
附註:	Part of: Synthesis digital library of engineering and computer science.
標題:	Querying (Computer science)
標題:	Database searching.
標題:	Missing observations (Statistics)
ISBN:	9781681734217
書目註:	Includes bibliographical references (pages 87-103).
內容註:	1. Introduction -- 1.1 Applications of incomplete data management -- 1.2 Overview of incomplete databases -- 1.2.1 Indexing incomplete databases -- 1.2.2 Querying incomplete databases -- 1.2.3 Incomplete database management systems -- 1.3 Challenges of querying incomplete databases -- 1.4 Organization --
摘要、提要註:	Incomplete data is part of life and almost all areas of scientific studies. Users tend to skip certain fields when they fill out online forms; participants choose to ignore sensitive questions on surveys; sensors fail, resulting in the loss of certain readings; publicly viewable satellite map services have missing data in many mobile applications; and in privacy-preserving applications, the data is incomplete deliberately in order to preserve the sensitivity of some attribute values. Query processing is a fundamental problem in computer science, and is useful in a variety of applications. In this book, we mostly focus on the query processing over incomplete databases, which involves finding a set of qualified objects from a specified incomplete dataset in order to support a wide spectrum of real-life applications. We first elaborate the three general kinds of methods of handling incomplete data, including (i) discarding the data with missing values, (ii) imputation for the missing values, and (iii) just depending on the observed data values. For the third method type, we introduce the semantics of k-nearest neighbor (kNN) search, skyline query, and top-k dominating query on incomplete data, respectively. In terms of the three representative queries over incomplete data, we investigate some advanced techniques to process incomplete data queries, including indexing, pruning as well as crowdsourcing techniques.
電子資源:	https://ieeexplore.ieee.org/servlet/opac?bknumber=8444554

Query processing over incomplete databases /
Gao, Yunjun,

Query processing over incomplete databases /Yunjun Gao, Xiaoye Miao. - [San Rafael, California] :Morgan & Claypool,2018. - 1 PDF (xv, 106 pages) :illustrations. - Synthesis lectures on data management,# 502153-5426 ;. - Synthesis digital library of engineering and computer science..

Part of: Synthesis digital library of engineering and computer science.

Includes bibliographical references (pages 87-103).

1. Introduction -- 1.1 Applications of incomplete data management -- 1.2 Overview of incomplete databases -- 1.2.1 Indexing incomplete databases -- 1.2.2 Querying incomplete databases -- 1.2.3 Incomplete database management systems -- 1.3 Challenges of querying incomplete databases -- 1.4 Organization --

Abstract freely available; full-text restricted to subscribers or individual document purchasers.

Compendex

Incomplete data is part of life and almost all areas of scientific studies. Users tend to skip certain fields when they fill out online forms; participants choose to ignore sensitive questions on surveys; sensors fail, resulting in the loss of certain readings; publicly viewable satellite map services have missing data in many mobile applications; and in privacy-preserving applications, the data is incomplete deliberately in order to preserve the sensitivity of some attribute values. Query processing is a fundamental problem in computer science, and is useful in a variety of applications. In this book, we mostly focus on the query processing over incomplete databases, which involves finding a set of qualified objects from a specified incomplete dataset in order to support a wide spectrum of real-life applications. We first elaborate the three general kinds of methods of handling incomplete data, including (i) discarding the data with missing values, (ii) imputation for the missing values, and (iii) just depending on the observed data values. For the third method type, we introduce the semantics of k-nearest neighbor (kNN) search, skyline query, and top-k dominating query on incomplete data, respectively. In terms of the three representative queries over incomplete data, we investigate some advanced techniques to process incomplete data queries, including indexing, pruning as well as crowdsourcing techniques.

Mode of access: World Wide Web.

ISBN: 9781681734217

Standard No.: 10.2200/S00870ED1V01Y201807DTM050doiSubjects--Topical Terms:

418063
Querying (Computer science)
Subjects--Index Terms:

query processing

LC Class. No.: QA76.9.D3 / G266 2018

Dewey Class. No.: 005.7565

Query processing over incomplete databases /
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245 1 0 $a Query processing over incomplete databases / $c Yunjun Gao, Xiaoye Miao.
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264 1 $a [San Rafael, California] : $b Morgan & Claypool, $c 2018.
300 $a 1 PDF (xv, 106 pages) : $b illustrations.
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490 1 $a Synthesis lectures on data management, $x 2153-5426 ; $v # 50
500 $a Part of: Synthesis digital library of engineering and computer science.
504 $a Includes bibliographical references (pages 87-103).
505 0 $a 1. Introduction -- 1.1 Applications of incomplete data management -- 1.2 Overview of incomplete databases -- 1.2.1 Indexing incomplete databases -- 1.2.2 Querying incomplete databases -- 1.2.3 Incomplete database management systems -- 1.3 Challenges of querying incomplete databases -- 1.4 Organization --
505 8 $a 2. Handling incomplete data methods -- 2.1 Method taxonomy -- 2.2 Overview of imputation methods -- 2.2.1 Statistical imputation -- 2.2.2 Machine learning-based imputation -- 2.2.3 Modern imputation methods --
505 8 $a 3. Query semantics on incomplete data -- 3.1 K-nearest neighbor search on incomplete data -- 3.1.1 Background -- 3.1.2 Problem definition -- 3.2 Skyline queries on incomplete data -- 3.2.1 Background -- 3.2.2 Problem definition -- 3.3 Top-k dominating queries on incomplete data -- 3.3.1 Background -- 3.3.2 Problem definition --
505 8 $a 4. Advanced techniques -- 4.1 Index structures -- 4.1.1 Lab index for k-nearest neighbor search on incomplete data -- 4.1.2 Histogram index for k-nearest neighbor search on incomplete data -- 4.1.3 Bitmap index for top-k dominating queries on incomplete data -- 4.2 Pruning heuristics -- 4.2.1 Alpha value pruning for k-nearest neighbor search on incomplete data -- 4.2.2 Histogram-based pruning for k-nearest neighbor search on incomplete data -- 4.2.3 Local skyband pruning for top-k dominating queries on incomplete data -- 4.2.4 Upper bound score pruning for top-k dominating queries on incomplete data -- 4.2.5 Bitmap pruning for top-k dominating queries on incomplete data -- 4.3 Crowdsourcing techniques -- 4.3.1 Crowdsourcing framework for skyline queries on incomplete data -- 4.3.2 C-table construction -- 4.3.3 Probability computation -- 4.3.4 Crowd task selection --
505 8 $a 5. Conclusions -- Bibliography -- Authors' biographies.
506 $a Abstract freely available; full-text restricted to subscribers or individual document purchasers.
510 0 $a Compendex
510 0 $a INSPEC
510 0 $a Google scholar
510 0 $a Google book search
520 3 $a Incomplete data is part of life and almost all areas of scientific studies. Users tend to skip certain fields when they fill out online forms; participants choose to ignore sensitive questions on surveys; sensors fail, resulting in the loss of certain readings; publicly viewable satellite map services have missing data in many mobile applications; and in privacy-preserving applications, the data is incomplete deliberately in order to preserve the sensitivity of some attribute values. Query processing is a fundamental problem in computer science, and is useful in a variety of applications. In this book, we mostly focus on the query processing over incomplete databases, which involves finding a set of qualified objects from a specified incomplete dataset in order to support a wide spectrum of real-life applications. We first elaborate the three general kinds of methods of handling incomplete data, including (i) discarding the data with missing values, (ii) imputation for the missing values, and (iii) just depending on the observed data values. For the third method type, we introduce the semantics of k-nearest neighbor (kNN) search, skyline query, and top-k dominating query on incomplete data, respectively. In terms of the three representative queries over incomplete data, we investigate some advanced techniques to process incomplete data queries, including indexing, pruning as well as crowdsourcing techniques.
530 $a Also available in print.
538 $a Mode of access: World Wide Web.
538 $a System requirements: Adobe Acrobat Reader.
588 $a Title from PDF title page (viewed on August 29, 2018).
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650 0 $a Database searching. $3 480672
650 0 $a Missing observations (Statistics) $3 342018
653 $a query processing
653 $a incomplete data
653 $a missing data
653 $a similarity search
653 $a k-nearest neighbor search
653 $a skyline query
653 $a top-k dominating query
653 $a crowdsourcing
700 1 $a Miao, Xiaoye, $e author. $3 728985
776 0 8 $i Print version: $z 9781681734200 $z 9781681734224
830 0 $a Synthesis digital library of engineering and computer science. $3 461208
830 0 $a Synthesis lectures on data management ; $v # 48. $x 2153-5426 $3 728978
856 4 2 $3 Abstract with links to resource $u https://ieeexplore.ieee.org/servlet/opac?bknumber=8444554