TY - JOUR
T1 - Towards Why-Not Spatial Keyword Top-k Queries
T2 - A Direction-Aware Approach
AU - Chen, Lei
AU - Li, Yafei
AU - Xu, Jianliang
AU - Jensen, Christian S.
N1 - Funding Information:
This work is supported by HK-RGC Grants 12201615, 12244916, and 12200817. The work of Yafei Li is supported by NSFC Grant 61602420.
PY - 2018/4/1
Y1 - 2018/4/1
N2 - With the continued proliferation of location-based services, a growing number of web-accessible data objects are geo-tagged and have text descriptions. An important query over such web objects is the direction-aware spatial keyword query that aims to retrieve the top-k objects that best match query parameters in terms of spatial distance and textual similarity in a given query direction. In some cases, it can be difficult for users to specify appropriate query parameters. After getting a query result, users may find some desired objects are unexpectedly missing and may therefore question the entire result. Enabling why-not questions in this setting may aid users to retrieve better results, thus improving the overall utility of the query functionality. This paper studies the direction-aware why-not spatial keyword top- k query problem. We propose efficient query refinement techniques to revive missing objects by minimally modifying users' direction-aware queries. We prove that the best refined query directions lie in a finite solution space for a special case and reduce the search for the optimal refinement to a linear programming problem for the general case. Extensive experimental studies demonstrate that the proposed techniques outperform a baseline method by two orders of magnitude and are robust in a broad range of settings.
AB - With the continued proliferation of location-based services, a growing number of web-accessible data objects are geo-tagged and have text descriptions. An important query over such web objects is the direction-aware spatial keyword query that aims to retrieve the top-k objects that best match query parameters in terms of spatial distance and textual similarity in a given query direction. In some cases, it can be difficult for users to specify appropriate query parameters. After getting a query result, users may find some desired objects are unexpectedly missing and may therefore question the entire result. Enabling why-not questions in this setting may aid users to retrieve better results, thus improving the overall utility of the query functionality. This paper studies the direction-aware why-not spatial keyword top- k query problem. We propose efficient query refinement techniques to revive missing objects by minimally modifying users' direction-aware queries. We prove that the best refined query directions lie in a finite solution space for a special case and reduce the search for the optimal refinement to a linear programming problem for the general case. Extensive experimental studies demonstrate that the proposed techniques outperform a baseline method by two orders of magnitude and are robust in a broad range of settings.
KW - direction-aware
KW - query refinement
KW - spatial keyword top-k query
KW - Why-not question
UR - http://www.scopus.com/inward/record.url?scp=85037663590&partnerID=8YFLogxK
U2 - 10.1109/TKDE.2017.2778731
DO - 10.1109/TKDE.2017.2778731
M3 - Journal article
AN - SCOPUS:85037663590
SN - 1041-4347
VL - 30
SP - 796
EP - 809
JO - IEEE Transactions on Knowledge and Data Engineering
JF - IEEE Transactions on Knowledge and Data Engineering
IS - 4
ER -