Evaluating pattern matching queries for spatial databases
In this paper, we study the spatial pattern matching (SPM) query. Given a set D of spatial objects (e.g., houses and shops), each with a textual description, we aim at finding all combinations of objects from D that match a user-defined spatial pattern P . A pattern P is a graph whose vertices repre...
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| Published in | The VLDB journal Vol. 28; no. 5; pp. 649 - 673 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Berlin/Heidelberg
Springer Berlin Heidelberg
01.10.2019
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1066-8888 0949-877X |
| DOI | 10.1007/s00778-019-00550-3 |
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| Abstract | In this paper, we study the
spatial pattern matching
(SPM) query. Given a set
D
of spatial objects (e.g., houses and shops), each with a textual description, we aim at finding all combinations of objects from
D
that match a user-defined
spatial pattern
P
. A pattern
P
is a graph whose vertices represent spatial objects, and edges denote distance relationships between them. The SPM query returns the instances that satisfy
P
. An example of
P
can be “a
house
within 10-min walk from a
school
, which is at least 2 km away from a
hospital
.” The SPM query can benefit users such as house buyers, urban planners, and archeologists. We prove that answering such queries is computationally intractable and propose two efficient algorithms for their evaluation. Moreover, we study efficient solutions to address two related problems of the SPM: (1) find top-
k
matches that are close to a query location and (2) return partial matches for a query pattern. Experiments and case studies on real datasets show that our proposed solutions are highly effective and efficient. |
|---|---|
| AbstractList | In this paper, we study the spatial pattern matching (SPM) query. Given a set D of spatial objects (e.g., houses and shops), each with a textual description, we aim at finding all combinations of objects from D that match a user-defined spatial patternP. A pattern P is a graph whose vertices represent spatial objects, and edges denote distance relationships between them. The SPM query returns the instances that satisfy P. An example of P can be “a house within 10-min walk from a school, which is at least 2 km away from a hospital.” The SPM query can benefit users such as house buyers, urban planners, and archeologists. We prove that answering such queries is computationally intractable and propose two efficient algorithms for their evaluation. Moreover, we study efficient solutions to address two related problems of the SPM: (1) find top-k matches that are close to a query location and (2) return partial matches for a query pattern. Experiments and case studies on real datasets show that our proposed solutions are highly effective and efficient. In this paper, we study the spatial pattern matching (SPM) query. Given a set D of spatial objects (e.g., houses and shops), each with a textual description, we aim at finding all combinations of objects from D that match a user-defined spatial pattern P . A pattern P is a graph whose vertices represent spatial objects, and edges denote distance relationships between them. The SPM query returns the instances that satisfy P . An example of P can be “a house within 10-min walk from a school , which is at least 2 km away from a hospital .” The SPM query can benefit users such as house buyers, urban planners, and archeologists. We prove that answering such queries is computationally intractable and propose two efficient algorithms for their evaluation. Moreover, we study efficient solutions to address two related problems of the SPM: (1) find top- k matches that are close to a query location and (2) return partial matches for a query pattern. Experiments and case studies on real datasets show that our proposed solutions are highly effective and efficient. |
| Author | Fang, Yixiang Cheng, Reynold Li, Yun Cong, Gao Mamoulis, Nikos |
| Author_xml | – sequence: 1 givenname: Yixiang orcidid: 0000-0002-5047-8593 surname: Fang fullname: Fang, Yixiang email: yxfang@cs.hku.hk organization: Department of Computer Science, The University of Hong Kong – sequence: 2 givenname: Yun surname: Li fullname: Li, Yun organization: Department of Computer Science and Technology, Nanjing University – sequence: 3 givenname: Reynold surname: Cheng fullname: Cheng, Reynold organization: Department of Computer Science, The University of Hong Kong – sequence: 4 givenname: Nikos surname: Mamoulis fullname: Mamoulis, Nikos organization: Department of Computer Science and Engineering, University of Ioannina – sequence: 5 givenname: Gao surname: Cong fullname: Cong, Gao organization: School of Computer Science and Engineering, Nanyang Technological University |
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| Keywords | Spatial keywords Spatial pattern Spatial query Pattern matching Pattern query Spatial database |
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ACM (2012) Tian, Y., et al.: Tale: a tool for approximate large graph matching. In: ICDE, pp. 963–972. IEEE (2008) MamoulisNPapadiasDIntegration of spatial join algorithms for processing multiple inputsSIGMOD199928211210.1145/304181.304183 Tong, H., Faloutsos, C., Gallagher, B., Eliassi-Rad, T.: Fast best-effort pattern matching in large attributed graphs. In: KDD, pp. 737–746 (2007) CongGJensenCSWuDEfficient retrieval of the top-k most relevant spatial web objectsVLDB200921337348 Fang, Y., Cheng, R., Wang, J., Budiman, Cong, G., Mamoulis, N.: SpaceKey: exploring patterns in spatial databases. In: ICDE, pp. 1577–1580. IEEE (2018) FangYChengRTangWManiuSYangXScalable algorithms for nearest-neighbor joins on big trajectory dataTKDE2016283785800 Zhang, D., et al.: Keyword search in spatial databases: towards searching by document. In: ICDE, pp. 688–699. 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ACM (2011) Papadias, D., Mamoulis, N., Theodoridis, Y.: Processing and optimization of multiway spatial joins using r-trees. In: PODS (1999) GallagherBMatching structure and semantics: a survey on graph-based pattern matchingAAAI FS200664553 Guo, T., Cao, X., Cong, G.: Efficient algorithms for answering the m-closest keywords query. In: SIGMOD, pp. 405–418. ACM (2015) Jin, J., An, N., Sivasubramaniam, A.: Analyzing range queries on spatial data. In: ICDE, pp. 525–534 (2000) ThomasLTValluriSRKarlapalemKMargin: maximal frequent subgraph miningTKDD2010431010.1145/1839490.1839491 MamoulisNPapadiasDMultiway spatial joinsTODS200126442447510.1145/503099.503101 Mahmood, A.R., Aref, W.G., Aly, A.M., Tang, M.: Atlas: on the expression of spatial-keyword group queries using extended relational constructs. In: SIGSPATIAL, p. 45. ACM (2016) Choi, D., Pei, J., Lin, X.: Finding the minimum spatial keyword cover. In: ICDE, pp. 685–696. IEEE (2016) Huang, W., Li, G., Tan, K.-L., Feng, J.: Efficient safe-region construction for moving top-k spatial keyword queries. In: CIKM, pp. 932–941. ACM (2012) Batagelj, V., Zaversnik, M.: An o(m) algorithm for cores decomposition of networks (2003). arXiv preprint arXiv:cs/0310049 Brinkhoff, T., Kriegel, H.-P., Seeger, B.: Efficient processing of spatial joins using r-trees. In: SIGMOD, pp 237–246 (1993) TangMSimilarity group-by operators for multi-dimensional relational dataTKDE2016282510523 WuDingmingYiuMan LungCongGaoJensenChristian S.Joint Top-K Spatial Keyword Query ProcessingIEEE Transactions on Knowledge and Data Engineering201224101889190310.1109/TKDE.2011.172 https://en.wikipedia.org/wiki/Geometric_distribution Papadias, D., et al.: Algorithms for querying by spatial structure. In: VLDB, pp. 546–557 (1998) LiYunFangYixiangChengReynoldZhangWenjieSpatial pattern matchingSIGSPATIAL Special201911131210.1145/3355491.3355493 NiemeläJEcology and urban planningBiodivers. Conserv.19998111913110.1023/A:1008817325994 Carletti, V., et al.: Challenging the time complexity of exact subgraph isomorphism for huge and dense graphs with VF3. In: TPAMI (2017) SeidmanSBNetwork structure and minimum degreeSoc Netw19835326928772129510.1016/0378-8733(83)90028-X Fang, Y., Cheng, R., Cong, G., Mamoulis, N., Li, Y.: On spatial pattern matching. In: ICDE, pp. 293–304. IEEE (2018) ZhangCZhangYZhangWLinXInverted linear quadtree: efficient top-k spatial keyword searchTKDE201628717061721 MongioviMSigma: a set-cover-based inexact graph matching algorithmJ. Bioinform. Comput. Biol.201080219921810.1142/S021972001000477X J Liu (550_CR39) 2017; 10 L Zou (550_CR11) 2009; 2 Y Fang (550_CR31) 2016; 28 N Mamoulis (550_CR10) 2001; 26 550_CR27 550_CR25 550_CR24 S Zhang (550_CR26) 2010; 3 550_CR46 550_CR9 550_CR8 550_CR7 LT Thomas (550_CR28) 2010; 4 J Schnaiberg (550_CR6) 2002; 30 550_CR12 C Zhang (550_CR34) 2016; 28 550_CR1 Yun Li (550_CR16) 2019; 11 Fei Chen (550_CR22) 2014 550_CR4 550_CR2 K Deng (550_CR3) 2015; 27 G Cong (550_CR33) 2009; 2 550_CR38 550_CR15 550_CR37 550_CR14 550_CR36 550_CR13 550_CR35 SB Seidman (550_CR29) 1983; 5 550_CR18 550_CR17 M Tang (550_CR44) 2016; 28 550_CR40 Y Fang (550_CR30) 2017; 10 B Gallagher (550_CR42) 2006; 6 M Mongiovi (550_CR45) 2010; 8 550_CR23 Y Fang (550_CR32) 2019; 31 550_CR21 550_CR43 550_CR20 J Niemelä (550_CR5) 1999; 8 Dingming Wu (550_CR19) 2012; 24 N Mamoulis (550_CR41) 1999; 28 |
| References_xml | – reference: Choi, D., Pei, J., Lin, X.: Finding the minimum spatial keyword cover. In: ICDE, pp. 685–696. IEEE (2016) – reference: Papadias, D., Mamoulis, N., Theodoridis, Y.: Processing and optimization of multiway spatial joins using r-trees. In: PODS (1999) – reference: Zhu, G., et al.: Treespan: efficiently computing similarity all-matching. In: SIGMOD, pp. 529–540. ACM (2012) – reference: DengKLiXLuJZhouXBest keyword cover searchTKDE20152716173 – reference: Fang, Y., Li, Y., Cheng, R., Mamoulis, N., Cong, G.: On spatial pattern matching. http://www.cse.unsw.edu.au/~z3525370/spm2019.pdf – reference: SeidmanSBNetwork structure and minimum degreeSoc Netw19835326928772129510.1016/0378-8733(83)90028-X – reference: NiemeläJEcology and urban planningBiodivers. Conserv.19998111913110.1023/A:1008817325994 – reference: MamoulisNPapadiasDMultiway spatial joinsTODS200126442447510.1145/503099.503101 – reference: SchnaibergJRieraJTurnerMGVossPRExplaining human settlement patterns in a recreational lake district: Vilas county, Wisconsin, USAEnviron. Manag.2002301243410.1007/s00267-002-2450-z – reference: Tian, Y., et al.: Tale: a tool for approximate large graph matching. In: ICDE, pp. 963–972. IEEE (2008) – reference: https://en.wikipedia.org/wiki/Floyd-Warshall_algorithm – reference: ZhangCZhangYZhangWLinXInverted linear quadtree: efficient top-k spatial keyword searchTKDE201628717061721 – reference: Zhang, C., Zhang, Y., Zhang, W., Lin, X., Cheema, M.A., Wang, X.: Diversified spatial keyword search on road networks. In: EDBT, pp. 367–378 (2014) – reference: Mahmood, A.R., Aref, W.G., Aly, A.M., Tang, M.: Atlas: on the expression of spatial-keyword group queries using extended relational constructs. In: SIGSPATIAL, p. 45. ACM (2016) – reference: Zhang, D., et al.: Keyword search in spatial databases: towards searching by document. In: ICDE, pp. 688–699. IEEE (2009) – reference: CongGJensenCSWuDEfficient retrieval of the top-k most relevant spatial web objectsVLDB200921337348 – reference: FangYChengRTangWManiuSYangXScalable algorithms for nearest-neighbor joins on big trajectory dataTKDE2016283785800 – reference: Settlement patterns (2017). http://geography.parkfieldprimary.com/the-united-kingdom/settlement-patterns – reference: Huang, W., Li, G., Tan, K.-L., Feng, J.: Efficient safe-region construction for moving top-k spatial keyword queries. In: CIKM, pp. 932–941. ACM (2012) – reference: Jin, J., An, N., Sivasubramaniam, A.: Analyzing range queries on spatial data. In: ICDE, pp. 525–534 (2000) – reference: Cao, X., Cong, G., Jensen, C.S., Ooi, B.C.: Collective spatial keyword querying. In: SIGMOD, pp. 373–384. ACM (2011) – reference: Tong, H., Faloutsos, C., Gallagher, B., Eliassi-Rad, T.: Fast best-effort pattern matching in large attributed graphs. In: KDD, pp. 737–746 (2007) – reference: ZouLChenLÖzsuMTDistance-join: pattern match query in a large graph databasePVLDB200921886897 – reference: FangYChengRLiXLuoSHuJEffective community search over large spatial graphsPVLDB2017106709720 – reference: ChenFeiWuXiaoweiPerfect Pipelining for Streaming Large File in Peer-to-Peer NetworksLecture Notes in Computer Science2014Berlin, HeidelbergSpringer Berlin Heidelberg2738 – reference: MongioviMSigma: a set-cover-based inexact graph matching algorithmJ. Bioinform. Comput. Biol.201080219921810.1142/S021972001000477X – reference: Fang, Y., Cheng, R., Cong, G., Mamoulis, N., Li, Y.: On spatial pattern matching. In: ICDE, pp. 293–304. IEEE (2018) – reference: Wu, Y., Patel, J.M., Jagadish, H.: Structural join order selection for XML query optimization. In: ICDE, pp. 443–454. IEEE (2003) – reference: LiuJDengKSunHGeYZhouXJensenCSClue-based spatio-textual queryPVLDB2017105529540 – reference: Ministry of Education of Singapore (2017). https://www.moe.gov.sg/admissions/primary-one-registration/allocation – reference: MamoulisNPapadiasDIntegration of spatial join algorithms for processing multiple inputsSIGMOD199928211210.1145/304181.304183 – reference: Batagelj, V., Zaversnik, M.: An o(m) algorithm for cores decomposition of networks (2003). arXiv preprint arXiv:cs/0310049 – reference: Papadias, D., et al.: Algorithms for querying by spatial structure. In: VLDB, pp. 546–557 (1998) – reference: ZhangSYangJJinWSapper: subgraph indexing and approximate matching in large graphsPVLDB201031–211851194 – reference: Carletti, V., et al.: Challenging the time complexity of exact subgraph isomorphism for huge and dense graphs with VF3. In: TPAMI (2017) – reference: GallagherBMatching structure and semantics: a survey on graph-based pattern matchingAAAI FS200664553 – reference: TangMSimilarity group-by operators for multi-dimensional relational dataTKDE2016282510523 – reference: LiYunFangYixiangChengReynoldZhangWenjieSpatial pattern matchingSIGSPATIAL Special201911131210.1145/3355491.3355493 – reference: https://en.wikipedia.org/wiki/Geometric_distribution – reference: FangYWangZChengRLiXLuoSHuJChenXOn spatial-aware community search. TKDE2019314783798 – reference: Guo, T., Cao, X., Cong, G.: Efficient algorithms for answering the m-closest keywords query. In: SIGMOD, pp. 405–418. ACM (2015) – reference: ThomasLTValluriSRKarlapalemKMargin: maximal frequent subgraph miningTKDD2010431010.1145/1839490.1839491 – reference: Chen, L., Cong, G., Jensen, C.S., Wu, D.: Spatial keyword query processing: an experimental evaluation. 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| Snippet | In this paper, we study the
spatial pattern matching
(SPM) query. Given a set
D
of spatial objects (e.g., houses and shops), each with a textual description,... In this paper, we study the spatial pattern matching (SPM) query. Given a set D of spatial objects (e.g., houses and shops), each with a textual description,... |
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| SubjectTerms | Algorithms Apexes Computer Science Database Management Graph theory Pattern matching Queries Regular Paper |
| Title | Evaluating pattern matching queries for spatial databases |
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