Preference Query Analysis and Optimization
Description
This book analyzes unexpected preference query results for three problems: causality and responsibility problems, why-not and why questions, and why-few and why-many questions. Further, it refines preference queries and discusses how to modify the original preference query based on different objectives, in order to obtain satisfying results. This highly informative and carefully presented book provides valuable insights for researchers, postgraduates and practitioners with an interest in database usability.
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Persons
Yunjun Gao is a professor at the College of Computer Science, Zhejiang University, China. His research interests include database and big data management. He has published more than 90 papers in several leading international journals and conferences including TODS, VLDBJ, TKDE, SIGMOD, VLDB, ICDE, and SIGIR. He is a member of the ACM and the IEEE, and a senior member of the CCF. He was an awardee of the NSFC Excellent Young Scholars Program in 2015, the SIGMOD 2015 Best Paper Nomination, one of the ICDE 2015 Best Papers, the First Prize of the MOE Science and Technology Progress (2016), and the First Prize of the Zhejiang Province Science and Technology (2011).
Qing Liu received his M.S. degree in computer science from Zhejiang University, China, in 2013, and his B.S. degree in software engineering from Zhejiang Normal University, China, in 2010. He completed his Ph.D. degree at the College of Computer Science, Zhejiang University in June 2017 and is currently pursuing postdoctoral research at Hong Kong Baptist University. His research interests include spatial databases and database usability. He has published more than 10 papers in several leading international journals and conferences including VLDBJ, TKDE, VLDB, and ICDE.Content
- Intro
- Preface
- Acknowledgements
- Contents
- Acronyms
- 1 Introduction to Preference Query Analysis and Optimization
- 1.1 Query Analysis and Optimization
- 1.2 Preference Queries
- 1.2.1 Quantitative Preference Queries
- 1.2.2 Qualitative Preference Queries
- 1.3 Research Issues and Challenges
- 1.4 Overview of the Book
- References
- 2 Causality and Responsibility Problem on Probabilistic Reverse Skyline Queries
- 2.1 Introduction
- 2.2 Related Work
- 2.3 Problem Statement
- 2.3.1 CRP Formulation
- 2.3.2 CR2PRSQ Formulation
- 2.4 CP Algorithm
- 2.5 Discussion
- 2.6 Performance Study
- 2.6.1 Experimental Setup
- 2.6.2 Experimental Results
- 2.7 Conclusion
- References
- 3 Why-Not and Why Questions on Reverse Top-k Queries
- 3.1 Introduction
- 3.2 Related Work
- 3.3 Problem Statement
- 3.4 Answering Why-Not Questions
- 3.4.1 Modifying Query Point
- 3.4.2 Modifying Why-Not Weighting Vector and k
- 3.4.3 Modifying Query Point, Why-Not Weighting Vector, and k
- 3.5 Answering Why Questions
- 3.5.1 Modifying Query Point
- 3.5.2 Modifying Why Weighting Vector and k
- 3.5.3 Modifying Query Point, Why Weighting Vector, and k
- 3.6 Performance Study
- 3.6.1 Experimental Setup
- 3.6.2 Results on Why-Not Questions
- 3.6.3 Results on Why Questions
- 3.7 Conclusion
- References
- 4 Why-Few and Why-Many Questions on Reverse Skyline Queries
- 4.1 Introduction
- 4.2 Related Work
- 4.3 Problem Statement
- 4.4 Answering Why-Few and Why-Many Questions
- 4.4.1 RI Algorithm
- 4.4.2 SP Algorithm
- 4.5 Performance Study
- 4.5.1 Experimental Settings
- 4.5.2 Experimental Results
- 4.6 Conclusion
- References
- 5 Reverse Top-k Query Result Analysis and Refinement System
- 5.1 Introduction
- 5.2 System Overview
- 5.3 Demonstration
- References
- 6 Conclusion and Future Work
- 6.1 Conclusions
- 6.2 Future Work
