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SPRINGER BRIEFS IN COMPUTER SCIENCE Yunjun Gao Qing Liu Preference Query Analysis and Optimization 123 SpringerBriefs in Computer Science Series editors Stan Zdonik, Brown University, Providence, Rhode Island, USA Shashi Shekhar, University of Minnesota, Minneapolis, Minnesota, USA Xindong Wu, University of Vermont, Burlington, Vermont, USA LakhmiC.Jain,UniversityofSouthAustralia,Adelaide,SouthAustralia,Australia David Padua, University of Illinois Urbana-Champaign, Urbana, Illinois, USA Xuemin (Sherman) Shen, University of Waterloo, Waterloo, Ontario, Canada Borko Furht, Florida Atlantic University, Boca Raton, Florida, USA V.S. Subrahmanian, University of Maryland, College Park, Maryland, USA Martial Hebert, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA Katsushi Ikeuchi, University of Tokyo, Tokyo, Japan Bruno Siciliano, Università di Napoli Federico II, Napoli, Italy Sushil Jajodia, George Mason University, Fairfax, Virginia, USA Newton Lee, Newton Lee Laboratories, LLC, Tujunga, California, USA More information about this series at http://www.springer.com/series/10028 Yunjun Gao Qing Liu (cid:129) Preference Query Analysis and Optimization 123 YunjunGao QingLiu ZhejiangUniversity ZhejiangUniversity Hangzhou, Zhejiang Hangzhou, Zhejiang China China ISSN 2191-5768 ISSN 2191-5776 (electronic) SpringerBriefs inComputer Science ISBN978-981-10-6634-4 ISBN978-981-10-6635-1 (eBook) https://doi.org/10.1007/978-981-10-6635-1 LibraryofCongressControlNumber:2017956738 ©TheAuthor(s)2017 This book was advertised with a copyright holder in the name of the publisher in error, whereas the author(s)holdsthecopyright. Thisworkissubjecttocopyright.AllrightsarereservedbythePublisher,whetherthewholeorpart of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission orinformationstorageandretrieval,electronicadaptation,computersoftware,orbysimilarordissimilar methodologynowknownorhereafterdeveloped. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publicationdoesnotimply,evenintheabsenceofaspecificstatement,thatsuchnamesareexemptfrom therelevantprotectivelawsandregulationsandthereforefreeforgeneraluse. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authorsortheeditorsgiveawarranty,expressorimplied,withrespecttothematerialcontainedhereinor for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictionalclaimsinpublishedmapsandinstitutionalaffiliations. Printedonacid-freepaper ThisSpringerimprintispublishedbySpringerNature TheregisteredcompanyisSpringerNatureSingaporePteLtd. Theregisteredcompanyaddressis:152BeachRoad,#21-01/04GatewayEast,Singapore189721,Singapore Preface The capability of database has been significantly improved over the decades. However, the usability of database is far from meeting users’ requirements. This book covers a comprehensive overview of preference query analysis and opti- mization, which is the key tactic for improving database usability. It is worth mentioning that this book spotlights on two representative preference queries, i.e., the reverse top-k query and the reverse skyline query. The three key problems of preference query result analytics include: causality and responsibility problem, why-not and why questions, and why-few and why-manyquestions.Thisbookelaboratesfouraspectsofpreferencequeryanalysis and optimization, including: (i) causality and responsibility problem: If preference query results include users’ unexpected objects or users' expected objects do not appearinthepreferencequeryresults,theusersmaywanttoknowwhatcausesthe appearance of the unexpected objects and/or what causes the absence of the expectedobjects.Towardthis,weexplorethecausalityandresponsibility problem on probabilistic reverse skyline queries. (ii) Why-not and why questions: Usually, the users would also like to know how to obtain expected preference query result objects and/or exclude unexpected preference query result objects. In view of this, we investigate the why-not and why questions on reverse top-k queries. (iii) Why-few and why-many questions: In real applications, preference queries mightreturntoofew(evenempty)ortoomanyanswerobjectstousers.Tothisend, westudythewhy-fewandwhy-manyquestionsonreverseskylinequeries.(iv)We developaninteractivesystem,termedasIS2R,toanalyzeunexpectedreversetop-k queryresults.Basedonthefeedbacksfromusers,thesystemofferstheexplanations of the unexpected reverse top-k query results as well as the suggestions on how to gettheexpectedreversetop-kqueryresults,totheusers,including(a)tocontainthe expected objects in reverse top-k query results, (b) to remove the unexpected objects from reverse top-k query results, (c) to increase the reverse top-k query result objects, and (d) to reduce the reverse top-k query result objects. This book is aimed toward the readers with an interest in database usability. It provides researchers and postgraduates a comprehensive overview of the general conceptsandtechniquesforpreferencequeryanalysisandoptimization.Thisbook v vi Preface can be an introductory book for the newcomers in the related research areas. Moreover, it is also able to help practitioners and developers to improve the usability of the database. Hangzhou, Zhejiang, China Yunjun Gao September 2017 Qing Liu Acknowledgements This work is partially supported by the 973 Program of China under Grant No. 2015CB352502, the National Nature Science Foundation of China (NSFC) under Grant No. 61522208 and 61379033, and the NSFC-Zhejiang Joint Fund under Grant No. U1609217. The authors conducted the research at the College of Computer Science, ZhejiangUniversity,Hangzhou,China.Duringtheprocessofwritingthisbook,we refer to the scientific researches of many scholars and experts. Consequently, we would like to express our heartfelt thanks to them. We thank Prof. Gang Chen from Zhejiang University (China), Associate Professor Baihua Zheng from Singapore Management University (Singapore), and Assistant Professor Lu Chen from Aalborg University (Denmark), for their countless discussions and constructive comments. Meanwhile, we would like to thank Miss. Linlin Zhou (with now working at NetEase (Hangzhou) Research Institute) and Miss Wanqi Liu (with now working toward the Ph.D. degree at the UniversityofTechnology,Sydney(Australia)), fortheireffectivealgorithmic code implementation. Also, we want to thank Senior Editor Celine Lanlan Chang and EditorJaneLifromSpringerforthevaluablefeedbacktoimprovethepresentation of this book. Lastbutnotleast,wewouldliketothankourownfamiliesforhavingourback. Thanks a lot for all of your kind understanding and great support. vii Contents 1 Introduction to Preference Query Analysis and Optimization. . . . . . 1 1.1 Query Analysis and Optimization . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Preference Queries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.2.1 Quantitative Preference Queries . . . . . . . . . . . . . . . . . . . . 3 1.2.2 Qualitative Preference Queries . . . . . . . . . . . . . . . . . . . . . 4 1.3 Research Issues and Challenges. . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.4 Overview of the Book . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2 Causality and Responsibility Problem on Probabilistic Reverse Skyline Queries. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.2 Related Work. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.3 Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.3.1 CRP Formulation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.3.2 CR2PRSQ Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . 14 2.4 CP Algorithm. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 2.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 2.6 Performance Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 2.6.1 Experimental Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 2.6.2 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 2.7 Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 3 Why-Not and Why Questions on Reverse Top-k Queries . . . . . . . . . 31 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 3.2 Related Work. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 3.3 Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 3.4 Answering Why-Not Questions. . . . . . . . . . . . . . . . . . . . . . . . . . 39 3.4.1 Modifying Query Point . . . . . . . . . . . . . . . . . . . . . . . . . . 40 ix x Contents 3.4.2 Modifying Why-Not Weighting Vector and k . . . . . . . . . . 45 3.4.3 Modifying Query Point, Why-Not Weighting Vector, and k . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 3.5 Answering Why Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 3.5.1 Modifying Query Point . . . . . . . . . . . . . . . . . . . . . . . . . . 54 3.5.2 Modifying Why Weighting Vector and k . . . . . . . . . . . . . 58 3.5.3 Modifying Query Point, Why Weighting Vector, and k . . . 61 3.6 Performance Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 3.6.1 Experimental Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 3.6.2 Results on Why-Not Questions. . . . . . . . . . . . . . . . . . . . . 64 3.6.3 Results on Why Questions . . . . . . . . . . . . . . . . . . . . . . . . 69 3.7 Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 4 Why-Few and Why-Many Questions on Reverse Skyline Queries. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75 4.2 Related Work. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78 4.3 Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 4.4 Answering Why-Few and Why-Many Questions . . . . . . . . . . . . . 80 4.4.1 RI Algorithm. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 4.4.2 SP Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86 4.5 Performance Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 4.5.1 Experimental Settings . . . . . . . . . . . . . . . . . . . . . . . . . . . 94 4.5.2 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94 4.6 Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98 5 Reverse Top-k Query Result Analysis and Refinement System. . . . . 101 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 5.2 System Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103 5.3 Demonstration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108 6 Conclusion and Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 6.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 6.2 Future Work. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110

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