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E-grāmata: Preference Query Analysis and Optimization

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Preference Query Analysis and OptimizationPalielināt
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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.

1 Introduction to Preference Query Analysis and Optimization
1(8)
1.1 Query Analysis and Optimization
1(2)
1.2 Preference Queries
3(1)
1.2.1 Quantitative Preference Queries
3(1)
1.2.2 Qualitative Preference Queries
4(1)
1.3 Research Issues and Challenges
4(2)
1.4 Overview of the Book
6(3)
References
6(3)
2 Causality and Responsibility Problem on Probabilistic Reverse Skyline Queries
9(22)
2.1 Introduction
9(3)
2.2 Related Work
12(1)
2.3 Problem Statement
13(3)
2.3.1 CRP Formulation
13(1)
2.3.2 CR2PRSQ Formulation
14(2)
2.4 CP Algorithm
16(6)
2.5 Discussion
22(2)
2.6 Performance Study
24(4)
2.6.1 Experimental Setup
24(1)
2.6.2 Experimental Results
25(3)
2.7 Conclusion
28(3)
References
28(3)
3 Why-Not and Why Questions on Reverse Top-k Queries
31(44)
3.1 Introduction
31(4)
3.2 Related Work
35(1)
3.3 Problem Statement
35(4)
3.4 Answering Why-Not Questions
39(15)
3.4.1 Modifying Query Point
40(5)
3.4.2 Modifying Why-Not Weighting Vector and k
45(6)
3.4.3 Modifying Query Point, Why-Not Weighting Vector, and k
51(3)
3.5 Answering Why Questions
54(9)
3.5.1 Modifying Query Point
54(4)
3.5.2 Modifying Why Weighting Vector and k
58(3)
3.5.3 Modifying Query Point, Why Weighting Vector, and k
61(2)
3.6 Performance Study
63(10)
3.6.1 Experimental Setup
63(1)
3.6.2 Results on Why-Not Questions
64(5)
3.6.3 Results on Why Questions
69(4)
3.7 Conclusion
73(2)
References
73(2)
4 Why-Few and Why-Many Questions on Reverse Skyline Queries
75(26)
4.1 Introduction
75(3)
4.2 Related Work
78(1)
4.3 Problem Statement
79(1)
4.4 Answering Why-Few and Why-Many Questions
80(13)
4.4.1 RI Algorithm
80(6)
4.4.2 SP Algorithm
86(7)
4.5 Performance Study
93(4)
4.5.1 Experimental Settings
94(1)
4.5.2 Experimental Results
94(3)
4.6 Conclusion
97(4)
References
98(3)
5 Reverse Top-k Query Result Analysis and Refinement System
101(8)
5.1 Introduction
101(2)
5.2 System Overview
103(1)
5.3 Demonstration
104(5)
References
108(1)
6 Conclusion and Future Work
109
6.1 Conclusions
109(1)
6.2 Future Work
110
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.
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