research-article

SkyGraph: retrieving regions of interest using skyline subgraph queries

Authors:

Shiladitya Pande,

Arnab BhattacharyaAuthors Info & Claims

Proceedings of the VLDB Endowment, Volume 10, Issue 11

Pages 1382 - 1393

https://doi.org/10.14778/3137628.3137647

Published: 01 August 2017 Publication History

Abstract

Several services today are annotated with points of interest (PoIs) such as "coffee shop", "park", etc. A region of interest (RoI) is a neighborhood that contains PoIs relevant to the user. In this paper, we study the scenario where a user wants to identify the best RoI in a city. The user expresses relevance through a set of keywords denoting PoIs. Ideally, the RoI should be small enough in size such that the user can conveniently explore the PoIs. On the other hand, it should be as relevant as possible. How does one balance the importance of size versus relevance? To a user exploring the RoI on foot, size is more critical. However, for a user equipped with a vehicle, relevance is a more important factor. In this paper, we solve this dilemma through skyline subgraph queries on keyword-embedded road networks. Skyline subgraphs subsume the choice of optimization function for an RoI since the optimal RoI for any rational user is necessarily a part of the skyline set. Our analysis reveals that the problem of computing the skyline set is NP-hard. We overcome the computational bottleneck by proposing a polynomial-time approximation algorithm called SkyGraph. To further expedite the running time, we develop an index structure, Partner Index, that drastically prunes the search space and provides up to 3 orders of magnitude speed-up on real road networks over the baseline approach. The datasets and executables are available at http://www.cse.iitd.ac.in/~sayan/software.html.

References

[1]

B. Awerbuch, Y. Azar, A. Blum, and S. Vempala. Improved approximation guarantees for minimum-weight k-trees and prize-collecting salesmen. In STOC, pages 277--283, 1995.

Digital Library

[2]

X. Cao, G. Cong, and C. S. Jensen. Retrieving top-k prestige-based relevant spatial web objects. PVLDB, 3(1--2):373--384, 2010.

Digital Library

[3]

X. Cao, G. Cong, C. S. Jensen, and B. C. Ooi. Collective spatial keyword querying. In SIGMOD, pages 373--384. ACM, 2011.

Digital Library

[4]

X. Cao, G. Cong, C. S. Jensen, and M. L. Yiu. Retrieving regions of interest for user exploration. PVLDB, 7(9):733--744, 2014.

Digital Library

[5]

A. Cary, O. Wolfson, and N. Rishe. Efficient and scalable method for processing top-k spatial boolean queries. In SSDBM, pages 87--95. Springer, 2010.

Digital Library

[6]

H.-J. Cho and C.-W. Chung. An efficient and scalable approach to cnn queries in a road network. In PVLDB, pages 865--876. VLDB Endowment, 2005.

Digital Library

[7]

H.-J. Cho, S. J. Kwon, and T.-S. Chung. Alps: an efficient algorithm for top-k spatial preference search in road networks. Knowledge and Information Systems, 42(3):599--631, 2015.

Digital Library

[8]

G. Cong, C. S. Jensen, and D. Wu. Efficient retrieval of the top-k most relevant spatial web objects. PVLDB, 2(1):337--348, 2009.

Digital Library

[9]

I. De Felipe, V. Hristidis, and N. Rishe. Keyword search on spatial databases. In ICDE, pages 656--665. IEEE, 2008.

Digital Library

[10]

N. Garg. A 3-approximation for the minimum tree spanning k vertices. In focs, volume 96, pages 302--309, 1996.

Digital Library

[11]

T. Guo, X. Cao, and G. Cong. Efficient algorithms for answering the m-closest keywords query. In SIGMOD, pages 405--418. ACM, 2015.

Digital Library

[12]

R. Hariharan, B. Hore, C. Li, and S. Mehrotra. Processing spatial-keyword (sk) queries in geographic information retrieval (gir) systems. In SSDBM, pages 16--16. IEEE, 2007.

Digital Library

[13]

H. Hu, D. L. Lee, and V. Lee. Distance indexing on road networks. In PVLDB, pages 894--905. VLDB Endowment, 2006.

Digital Library

[14]

H. Hu, D. L. Lee, and J. Xu. Fast nearest neighbor search on road networks. In EDBT, pages 186--203. Springer, 2006.

Digital Library

[15]

M. Jiang, A. W.-C. Fu, R. C.-W. Wong, and Y. Xu. Hop doubling label indexing for point-to-point distance querying on scale-free networks. PVLDB, 7(12):1203--1214, 2014.

Digital Library

[16]

M. Kolahdouzan and C. Shahabi. Voronoi-based k nearest neighbor search for spatial network databases. In VLDB, pages 840--851, 2004.

Digital Library

[17]

K. C. Lee, W.-C. Lee, and B. Zheng. Fast object search on road networks. In EDBT, pages 1018--1029. ACM, 2009.

Digital Library

[18]

K. C. Lee, W.-C. Lee, B. Zheng, and Y. Tian. Road: A new spatial object search framework for road networks. TKDE, 24(3):547--560, 2012.

Digital Library

[19]

B. Liao, M. L. Yiu, Z. Gong, et al. Beyond millisecond latency nn search on commodity machine. TKDE, 27(10):2618--2631, 2015.

Digital Library

[20]

C. Long, R. C.-W. Wong, K. Wang, and A. W.-C. Fu. Collective spatial keyword queries: a distance owner-driven approach. In SIGMOD, pages 689--700. ACM, 2013.

Digital Library

[21]

D. Papadias, J. Zhang, N. Mamoulis, and Y. Tao. Query processing in spatial network databases. In PVLDB, pages 802--813. VLDB Endowment, 2003.

Digital Library

[22]

J. B. Rocha-Junior and K. Nørvåg. Top-k spatial keyword queries on road networks. In EDBT, pages 168--179. ACM, 2012.

Digital Library

[23]

H. Samet, J. Sankaranarayanan, and H. Alborzi. Scalable network distance browsing in spatial databases. In SIGMOD, pages 43--54. ACM, 2008.

Digital Library

[24]

S. Srivastava, S. Pande, and S. Ranu. Geo-social clustering of places from check-in data. In ICDM, pages 985--990, 2015.

Digital Library

[25]

D. Zhang, Y. M. Chee, A. Mondal, A. K. Tung, and M. Kitsuregawa. Keyword search in spatial databases: Towards searching by document. In ICDE, pages 688--699. IEEE, 2009.

Digital Library

[26]

D. Zhang, B. C. Ooi, and A. K. Tung. Locating mapped resources in web 2.0. In ICDE, pages 521--532. IEEE, 2010.

[27]

B. Zheng, K. Zheng, X. Xiao, H. Su, H. Yin, X. Zhou, and G. Li. Keyword-aware continuous knn query on road networks. In ICDE, pages 871--882. IEEE, 2016.

[28]

K. Zheng, H. Su, B. Zheng, S. Shang, J. Xu, J. Liu, and X. Zhou. Interactive top-k spatial keyword queries. In ICDE, pages 423--434. IEEE, 2015.

[29]

R. Zhong, G. Li, K.-L. Tan, and L. Zhou. G-tree: An efficient index for knn search on road networks. In CIKM, pages 39--48. ACM, 2013.

Digital Library

Cited By

Gupta MKodamana HRanu SSingh ASun YAkoglu LGunopulos DYan XKumar ROzcan FYe J(2023)Frigate: Frugal Spatio-temporal Forecasting on Road NetworksProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599357(649-660)Online publication date: 6-Aug-2023
https://dl.acm.org/doi/10.1145/3580305.3599357
Mishra SKhetarpaul S(2023)uR-tree: a spatial index structure for handling multiple point selection queriesMultimedia Tools and Applications10.1007/s11042-022-13357-282:6(8093-8111)Online publication date: 1-Mar-2023
https://dl.acm.org/doi/10.1007/s11042-022-13357-2
Liu ZLi LZhang MHua WZhou X(2022)FHL-cubeProceedings of the VLDB Endowment10.14778/3551793.355185615:11(3112-3125)Online publication date: 1-Jul-2022
https://dl.acm.org/doi/10.14778/3551793.3551856
Show More Cited By

SkyGraph: retrieving regions of interest using skyline subgraph queries
1. Information systems
2. Theory of computation
  1. Theory and algorithms for application domains
    1. Database theory

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Published In

cover image Proceedings of the VLDB Endowment

Proceedings of the VLDB Endowment Volume 10, Issue 11

August 2017

432 pages

ISSN:2150-8097

Editors:
Peter Boncz
CWI
,
Ken Salem
University of Waterloo

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VLDB Endowment

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Published: 01 August 2017

Published in PVLDB Volume 10, Issue 11

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Cited By

Gupta MKodamana HRanu SSingh ASun YAkoglu LGunopulos DYan XKumar ROzcan FYe J(2023)Frigate: Frugal Spatio-temporal Forecasting on Road NetworksProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599357(649-660)Online publication date: 6-Aug-2023
https://dl.acm.org/doi/10.1145/3580305.3599357
Mishra SKhetarpaul S(2023)uR-tree: a spatial index structure for handling multiple point selection queriesMultimedia Tools and Applications10.1007/s11042-022-13357-282:6(8093-8111)Online publication date: 1-Mar-2023
https://dl.acm.org/doi/10.1007/s11042-022-13357-2
Liu ZLi LZhang MHua WZhou X(2022)FHL-cubeProceedings of the VLDB Endowment10.14778/3551793.355185615:11(3112-3125)Online publication date: 1-Jul-2022
https://dl.acm.org/doi/10.14778/3551793.3551856
Akgün ÖEnright JJefferson CMcCreesh CProsser PZschaler S(2021)Finding Subgraphs with Side ConstraintsIntegration of Constraint Programming, Artificial Intelligence, and Operations Research10.1007/978-3-030-78230-6_22(348-364)Online publication date: 5-Jul-2021
https://dl.acm.org/doi/10.1007/978-3-030-78230-6_22
Prateek AKhan AGoyal ARanu S(2020)Mining Top-k pairs of correlated subgraphs in a large networkProceedings of the VLDB Endowment10.14778/3397230.339724513:9(1511-1524)Online publication date: 26-Jun-2020
https://dl.acm.org/doi/10.14778/3397230.3397245
Yuen CSingh AGoyal SRanu SBagchi A(2019)Beyond Shortest Paths: Route Recommendations for Ride-sharingThe World Wide Web Conference10.1145/3308558.3313465(2258-2269)Online publication date: 13-May-2019
https://dl.acm.org/doi/10.1145/3308558.3313465

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