TY - GEN
T1 - Answering why-not questions on spatial keyword top-k queries
AU - Chen, Lei
AU - Lin, Xin
AU - HU, Haibo
AU - Jensen, Christian S.
AU - Xu, Jianliang
N1 - This work is partially supported by HK-RGC GRF grants HKBU211512 & HKBU12202414 and HKBU FRG2/12- 13/081. The work of Xin Lin was supported by China Post- doctoral Science Foundation, Shanghai Postdoctoral Scientific Program and Shanghai Pujiang Program.
PY - 2015/4/13
Y1 - 2015/4/13
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84940841078&partnerID=8YFLogxK
U2 - 10.1109/ICDE.2015.7113291
DO - 10.1109/ICDE.2015.7113291
M3 - Conference proceeding
AN - SCOPUS:84940841078
T3 - Proceedings - International Conference on Data Engineering
SP - 279
EP - 290
BT - 2015 IEEE 31st International Conference on Data Engineering, ICDE 2015
PB - IEEE
T2 - 31st IEEE International Conference on Data Engineering, ICDE 2015
Y2 - 13 April 2015 through 17 April 2015
ER -
