Clue-based spatio-textual query

Liu, Junling, Ge, Yu, Deng, Ke, Zhou, Xiaofang, Sun, Huanliang and Jensen, Christian S. (2016). Clue-based spatio-textual query. In: Proceedings of the VLDB Endowment. 43rd International Conference on Very Large Data Bases, VLDB 2017, Munich, Germany, (529-540). 28 August - 1 September 2017. doi:10.14778/3055540.3055546

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Author Liu, Junling
Ge, Yu
Deng, Ke
Zhou, Xiaofang
Sun, Huanliang
Jensen, Christian S.
Title of paper Clue-based spatio-textual query
Conference name 43rd International Conference on Very Large Data Bases, VLDB 2017
Conference location Munich, Germany
Conference dates 28 August - 1 September 2017
Convener ACM
Proceedings title Proceedings of the VLDB Endowment   Check publisher's open access policy
Journal name Proceedings of the VLDB Endowment   Check publisher's open access policy
Series Proceedings of the VLDB Endowment
Place of Publication New York, NY, United States
Publisher Association for Computing Machinery
Publication Year 2016
Sub-type Fully published paper
DOI 10.14778/3055540.3055546
Open Access Status Not yet assessed
ISSN 2150-8097
Volume 10
Issue 5
Start page 529
End page 540
Total pages 12
Language eng
Abstract/Summary Along with the proliferation of online digital map and location- based service, very large POI (point of interest) databases have been constructed where a record corresponds to a POI with information including name, category, address, geographical location and other features. A basic spatial query in POI database is POI retrieval. In many scenarios, a user cannot provide enough information to pinpoint the POI except some clue. For example, a user wants to identify a cafe in a city visited many years ago. SHe cannot remember the name and address but she still recalls that "the cafe is about 200 meters away from a restaurant; and turning left at the restaurant there is a bakery 500 meters away, etc.". Intuitively, the clue, even partial and approximate, describes the spatio-textual context around the targeted POI. Motivated by this observation, this work investigates clue-based spatio-textual query which allows user providing clue, i.e., some nearby POIs and the spatial relationships between them, in POI retrieval. The objective is to retrieve k POIs from a POI database with the highest spatio-textual context similarities against the clue. This work has deliberately designed data-quality-tolerant spatio-textual context similarity metric to cope with various data quality problems in both the clue and the POI database. Through crossing valuation, the query accuracy is further enhanced by ensemble method. Also, this work has developed an index called roll-out-star R-tree (RSR-tree) to dramatically improve the query processing efficiency. The extensive tests on data sets from the real world have verified the superiority of our methods in all aspects.
Subjects 1701 Computer Science (miscellaneous)
1700 Computer Science
Q-Index Code E1
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Conference Paper
Sub-type: Fully published paper
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School of Information Technology and Electrical Engineering Publications
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