QUERY PROCESSING OVER INCOMPLE

Erscheinungsjahr 2018
ISBN: 978-1-68173-422-4
Verlag: MORGAN & CLAYPOOL

Buch, Englisch, 122 Seiten, Hardback, Format (B × H): 190 mm x 235 mm

Reihe: Synthesis Lectures on Data Management

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.

Autoren/Hrsg.

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik
• Mathematik | Informatik EDV | Informatik EDV & Informatik Allgemein
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Automatische Datenerfassung, Datenanalyse

Inhaltsverzeichnis

- Preface
- Acknowledgments
- Introduction
- Handling Incomplete Data Methods
- Query Semantics on Incomplete Data
- Advanced Techniques
- Conclusions
- Bibliography
- Authors' Biographies

Über Autor(innen)

Yunjun Gao is a professor at the College of Computer Science, Zhejiang University, China. He received a Ph.D. in computer science from Zhejiang University, China, in 2008. Prior to joining the faculty in 2010, he was a postdoctoral fellow (scientist) at Singapore Management University from 2008–2010, and a visiting scholar or research assistant at Nanyang Technological University, Simon Fraser University, and City University of Hong Kong, respectively. His primary research areas are database, big data management, and AI Interaction with DB Technology. In particular, his current research interests include Data-Driven Machine Learning, Big Graph Data Management and Mining, Geo-Social Data Processing, Data Quality, Metric and Incomplete/Uncertain Data Management, Database Usability, and Spatial and Spatio-Temporal Databases. He has published more than 100 papers in several premium/leading international journals and conferences including TODS, VLDBJ, TKDE, TOIS, SIGMOD, VLDB, ICDE, and SIGIR. He is a member of the ACM and the IEEE, and a senior member of the CCF. He is or was an associate editor of DAPD and IJSSOE, a guest editor of WWWJ, IJDSN, and DSE, and a referee/reviewer of several prestigious journals such as TODS, VLDBJ, TKDE, TMC, and TKDD. He is serving or has served as a PC co-chair, workshop co-chair, publication chair, publicity co-chair, local poster chair, program committee member, or (external) reviewer for various important international conferences such as SIGMOD, VLDB, ICDE, EDBT, CIKM, DASFAA, SIGSPATIAL GIS, APWeb, WAIM, WISE, MDM, among others. He was an awardee/recipient of the NSFC Excellent Young Scholars Program in 2015, the Best Paper Award of APWeb-WAIM 2018, the 2017 CCF Outstanding Doctoral Dissertation Award (Advisor), the 2016 Zhejiang Provincial Outstanding Master’s Dissertation Award (Advisor), the First Prize of the Ministry of Education Science and Technology Progress Award (2016), the Nomination of the Best Paper of SIGMOD 2015, one of the Best Papers of ICDE 2015, and the First Prize of Zhejiang Province Science and Technology Award (2011).

Xiaoye Miao is a Postdoctoral Fellow at the Department of Computer Science, City University of Hong Kong, China. She received a Ph.D. in computer science from Zhejiang University, China in 2017 and received a B.S. from the College of Computer Science at Xi'an Jiaotong University, China, in 2012. Her research interests include Incomplete/Uncertain Data Management, Graph Data Management, Data Pricing, and Data Cleaning. She has published more than 15 papers in several premium/leading international journals and conferences including VLDBJ, TKDE, VLDB, and ICDE. She is serving or has served as an (external) reviewer for a variety of important international journals and conferences such as VLDB Journal, TKDE, Information Sciences, WWW Journal, JCST, VLDB, ICDE, DASFAA, SIGSPATIAL GIS, APWeb, WAIM, DEXA, among others.

H. V. Jagadish is Bernard A Galler Collegiate Professor of Electrical Engineering and Computer Science, and Distinguished Scientist at the Institute for Data Science, at the University of Michigan in Ann Arbor. Prior to 1999, he was Head of the Database Research Department at AT&T Labs, Florham Park, NJ.

Professor Jagadish is well known for his broad-ranging research on information management, and has approximately 200 major papers and 37 patents. He is a fellow of the ACM, ""The First Society in Computing,"" (since 2003) and serves on the board of the Computing Research Association (since 2009). He has been an Associate Editor for the ACM Transactions on Database Systems (1992-1995), Program Chair of the ACM SIGMOD annual conference (1996), Program Chair of the ISMB conference (2005), a trustee of the VLDB (Very Large DataBase) foundation (2004-2009), Founding Editor-in-Chief of the Proceedings of the VLDB Endowment (2008-2014), and Program Chair of the VLDB Conference (2014). Among his many awards, he won the ACM SIGMOD Contributions Award in 2013 and the David E Liddle Research Excellence Award (at the University of Michigan) in 2008.