Retrieving regions of interest for user exploration
We consider an application scenario where points of interest (PoIs) each have a web presence and where a web user wants to iden- tify a region that contains relevant PoIs that are relevant to a set of keywords, e.g., in preparation for deciding where to go to conve- niently explore the PoIs. Motivat...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
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2014
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| Online Access: | https://hdl.handle.net/10356/105001 http://hdl.handle.net/10220/20433 http://www.vldb.org/pvldb/vol7/p733-cao.pdf |
| _version_ | 1826127698237325312 |
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| author | Cao, Xin Cong, Gao Jensen, Christian S. Yiu, Man Lung |
| author2 | School of Computer Engineering |
| author_facet | School of Computer Engineering Cao, Xin Cong, Gao Jensen, Christian S. Yiu, Man Lung |
| author_sort | Cao, Xin |
| collection | NTU |
| description | We consider an application scenario where points of interest (PoIs) each have a web presence and where a web user wants to iden- tify a region that contains relevant PoIs that are relevant to a set of keywords, e.g., in preparation for deciding where to go to conve- niently explore the PoIs. Motivated by this, we propose the length- constrained maximum-sum region (LCMSR) query that returns a spatial-network region that is located within a general region of in- terest, that does not exceed a given size constraint, and that best matches query keywords. Such a query maximizes the total weight of the PoIs in it w.r.t. the query keywords. We show that it is NP- hard to answer this query. We develop an approximation algorithm with a (5 + ε) approximation ratio utilizing a technique that scales node weights into integers. We also propose a more efficient heuris- tic algorithm and a greedy algorithm. Empirical studies on real data offer detailed insight into the accuracy of the proposed algorithms and show that the proposed algorithms are capable of computing results efficiently and effectively. |
| first_indexed | 2024-10-01T07:13:07Z |
| format | Journal Article |
| id | ntu-10356/105001 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T07:13:07Z |
| publishDate | 2014 |
| record_format | dspace |
| spelling | ntu-10356/1050012020-05-28T07:18:49Z Retrieving regions of interest for user exploration Cao, Xin Cong, Gao Jensen, Christian S. Yiu, Man Lung School of Computer Engineering DRNTU::Engineering::Computer science and engineering We consider an application scenario where points of interest (PoIs) each have a web presence and where a web user wants to iden- tify a region that contains relevant PoIs that are relevant to a set of keywords, e.g., in preparation for deciding where to go to conve- niently explore the PoIs. Motivated by this, we propose the length- constrained maximum-sum region (LCMSR) query that returns a spatial-network region that is located within a general region of in- terest, that does not exceed a given size constraint, and that best matches query keywords. Such a query maximizes the total weight of the PoIs in it w.r.t. the query keywords. We show that it is NP- hard to answer this query. We develop an approximation algorithm with a (5 + ε) approximation ratio utilizing a technique that scales node weights into integers. We also propose a more efficient heuris- tic algorithm and a greedy algorithm. Empirical studies on real data offer detailed insight into the accuracy of the proposed algorithms and show that the proposed algorithms are capable of computing results efficiently and effectively. Published version 2014-08-28T07:28:00Z 2019-12-06T21:44:21Z 2014-08-28T07:28:00Z 2019-12-06T21:44:21Z 2014 2014 Journal Article Cao, X., Cong, G., Jensen, C. S., & Yiu, M. L. (2014). Retrieving regions of interest for user exploration. VLDB endowment, 7(9), 733-744. doi: 10.14778/2732939.2732946 https://hdl.handle.net/10356/105001 http://hdl.handle.net/10220/20433 10.14778/2732939.2732946 http://www.vldb.org/pvldb/vol7/p733-cao.pdf en Proceedings of the VLDB endowment © 2014 VLDB Endowment. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-nd/3.0/. Obtain permission prior to any use beyond those covered by the license. Contact copyright holder by emailing info@vldb.org. Articles from this volume were invited to present their results at the 40th International Conference on Very Large Data Bases, September 1st - 5th 2014, Hangzhou, China. 12 p. application/pdf |
| spellingShingle | DRNTU::Engineering::Computer science and engineering Cao, Xin Cong, Gao Jensen, Christian S. Yiu, Man Lung Retrieving regions of interest for user exploration |
| title | Retrieving regions of interest for user exploration |
| title_full | Retrieving regions of interest for user exploration |
| title_fullStr | Retrieving regions of interest for user exploration |
| title_full_unstemmed | Retrieving regions of interest for user exploration |
| title_short | Retrieving regions of interest for user exploration |
| title_sort | retrieving regions of interest for user exploration |
| topic | DRNTU::Engineering::Computer science and engineering |
| url | https://hdl.handle.net/10356/105001 http://hdl.handle.net/10220/20433 http://www.vldb.org/pvldb/vol7/p733-cao.pdf |
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