Query Processing over Incomplete Databases (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.

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, oneof the Best Papers of ICDE 2015, and the First Prize of Zhejiang Province Science and Technology Award (2011).