Query processing over incomplete databases

by Gao, Yunjun

Type Book

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Title Statements

Full title :Query processing over incomplete databases / Yunjun Gao, Xiaoye Miao.

Summary

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.

Edition Statements

Publication Date:2018.

Publication Statements

Publisher(s):Morgan & Claypool
Place of Publication:San Rafael, California
Language:eng
Publication Date:2018.

Notes

Titre de l'écran-titre (visionné en 2019).

In Morgan and Claypool publishers.

The Physical Object

Description:1 ressource en ligne (xv, 106 pages) : : illustrations, fichier PDF.

Edition Identifiers

ISBN:9781681734217, 1681734214
oclc:on1132043046
LC Classification:QA76.9.D3
Dewey Decimal:005.7565

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

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

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

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 d…