Answering why-not questions on spatial keyword top-k queries

Large volumes of geo-tagged text objects are available on the web. Spatial keyword top-k queries retrieve k such objects with the best score according to a ranking function that takes into account a query location and query keywords. In this setting, users may wonder why some known object is unexpectedly missing from a result; and understanding why may aid users in retrieving better results. While spatial keyword querying has been studied intensively, no proposals exist for how to offer users explanations of why such expected objects are missing from results. We provide techniques that allow the revision of spatial keyword queries such that their results include one or more desired, but missing objects. In doing so, we adopt a query refinement approach to provide a basic algorithm that reduces the problem to a two-dimensional geometrical problem. To improve performance, we propose an index-based ranking estimation algorithm that prunes candidate results early. Extensive experimental results offer insight into design properties of the proposed techniques and suggest that they are efficient in terms of both running time and I/O cost.

[1]  Anthony K. H. Tung,et al.  Keyword Search in Spatial Databases: Towards Searching by Document , 2009, 2009 IEEE 25th International Conference on Data Engineering.

[2]  Gang Chen,et al.  Answering Why-not Questions on Reverse Top-k Queries , 2015, Proc. VLDB Endow..

[3]  Christian S. Jensen,et al.  Efficient Retrieval of the Top-k Most Relevant Spatial Web Objects , 2009, Proc. VLDB Endow..

[4]  Quoc Trung Tran,et al.  How to ConQueR why-not questions , 2010, SIGMOD Conference.

[5]  Marios Hadjieleftheriou,et al.  R-Trees - A Dynamic Index Structure for Spatial Searching , 2008, ACM SIGSPATIAL International Workshop on Advances in Geographic Information Systems.

[6]  Jianliang Xu,et al.  Answering why-not spatial keyword top-k queries via keyword adaption , 2016, 2016 IEEE 32nd International Conference on Data Engineering (ICDE).

[7]  Christian S. Jensen,et al.  Spatial Keyword Query Processing: An Experimental Evaluation , 2013, Proc. VLDB Endow..

[8]  Jeffrey F. Naughton,et al.  On the provenance of non-answers to queries over extracted data , 2008, Proc. VLDB Endow..

[9]  Sourav S. Bhowmick,et al.  Why not, WINE?: towards answering why-not questions in social image search , 2013, MM '13.

[10]  Jun Hu,et al.  SEAL: Spatio-Textual Similarity Search , 2012, Proc. VLDB Endow..

[11]  Christian S. Jensen,et al.  A framework for efficient spatial web object retrieval , 2012, The VLDB Journal.

[12]  Jian Pei,et al.  Efficient Skyline and Top-k Retrieval in Subspaces , 2007, IEEE Transactions on Knowledge and Data Engineering.

[13]  Jiaheng Lu,et al.  Reverse spatial and textual k nearest neighbor search , 2011, SIGMOD '11.

[14]  Christian S. Jensen,et al.  Spatial Keyword Querying , 2012, ER.

[15]  João B. Rocha-Junior,et al.  Top-k spatial keyword queries on road networks , 2012, EDBT '12.

[16]  Chengfei Liu,et al.  On answering why-not questions in reverse skyline queries , 2013, 2013 IEEE 29th International Conference on Data Engineering (ICDE).

[17]  Christian S. Jensen,et al.  Efficient continuously moving top-k spatial keyword query processing , 2011, 2011 IEEE 27th International Conference on Data Engineering.

[18]  J. Shane Culpepper,et al.  Maximizing Bichromatic Reverse Spatial and Textual k Nearest Neighbor Queries , 2016, Proc. VLDB Endow..

[19]  Torsten Suel,et al.  Efficient query processing in geographic web search engines , 2006, SIGMOD Conference.

[20]  Jianliang Xu,et al.  Reverse keyword search for spatio-textual top-k queries in location-based services , 2015, 2016 IEEE 32nd International Conference on Data Engineering (ICDE).

[21]  Melanie Herschel,et al.  Explaining missing answers to SPJUA queries , 2010, Proc. VLDB Endow..

[22]  Feifei Li,et al.  Spatial Approximate String Search , 2013, IEEE Transactions on Knowledge and Data Engineering.

[23]  João B. Rocha-Junior,et al.  Efficient Processing of Top-k Spatial Keyword Queries , 2011, SSTD.

[24]  Xing Xie,et al.  Hybrid index structures for location-based web search , 2005, CIKM '05.

[25]  Eric Lo,et al.  Answering Why-Not Questions on Top-K Queries , 2012, IEEE Transactions on Knowledge and Data Engineering.

[26]  Christian S. Jensen,et al.  Retrieving top-k prestige-based relevant spatial web objects , 2010, Proc. VLDB Endow..

[27]  Ken C. K. Lee,et al.  IR-Tree: An Efficient Index for Geographic Document Search , 2011, IEEE Trans. Knowl. Data Eng..

[28]  Seung-won Hwang,et al.  Processing and Optimizing Main Memory Spatial-Keyword Queries , 2015, Proc. VLDB Endow..

[29]  Gang Chen,et al.  Answering why-not questions on metric probabilistic range queries , 2016, 2016 IEEE 32nd International Conference on Data Engineering (ICDE).

[30]  Srinivasan Venkatesh,et al.  Indexing for Vector Projections , 2011, DASFAA.

[31]  Anthony K. H. Tung,et al.  Locating mapped resources in Web 2.0 , 2010, 2010 IEEE 26th International Conference on Data Engineering (ICDE 2010).

[32]  Jing Xu,et al.  DESKS: Direction-Aware Spatial Keyword Search , 2012, 2012 IEEE 28th International Conference on Data Engineering.

[33]  Beng Chin Ooi,et al.  Collective spatial keyword querying , 2011, SIGMOD '11.

[34]  Nikos Mamoulis,et al.  Spatio-textual similarity joins , 2012, Proc. VLDB Endow..

[35]  Adriane Chapman,et al.  Why Not? , 1965, SIGMOD Conference.

[36]  Jianliang Xu,et al.  Social-Aware Top-k Spatial Keyword Search , 2014, 2014 IEEE 15th International Conference on Mobile Data Management.

[37]  Hinrich Schütze,et al.  Introduction to information retrieval , 2008 .

[38]  Naphtali Rishe,et al.  Keyword Search on Spatial Databases , 2008, 2008 IEEE 24th International Conference on Data Engineering.

[39]  Adriane Chapman,et al.  Making database systems usable , 2007, SIGMOD '07.

[40]  Mário J. Silva,et al.  Indexing and ranking in Geo-IR systems , 2005, GIR '05.

[41]  Nikos Mamoulis,et al.  Top-k Relevant Semantic Place Retrieval on Spatial RDF Data , 2016, SIGMOD Conference.