research-article

Extending Graph Patterns with Conditions

Authors:

Jingren ZhouAuthors Info & Claims

SIGMOD '20: Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data

Pages 715 - 729

https://doi.org/10.1145/3318464.3380585

Published: 31 May 2020 Publication History

Abstract

We propose an extension of graph patterns, referred to as conditional graph patterns and denoted as CGPs. In a CGP,one can specify a simple condition on each edge such that the edge exists if and only if the condition is satisfied. We show that CGPs allow us to catch missing links, increase the expressivity of graph functional dependencies, and provide a succinct representation of graph patterns. We settle the complexity of their consistency, matching, incremental matching and containment problems, in linear time,NP-complete,NP-complete and p2-complete, respectively. These tell us that despite the increased expressive power of CGPs, the matching and incremental matching problems for CGPs are no harder than their counterparts for conventional patterns. We develop algorithms for matching and incremental matching of CGPs, and for (incremental) multi-CGP matching and optimization. Using real-life and synthetic graphs, we empirically verify the efficiency and effectiveness of our algorithms.

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References

[1]

Serge Abiteboul, Richard Hull, and Victor Vianu. 1995. Foundations of Databases .Addison-Wesley.

Digital Library

[2]

Bibek Bhattarai, Hang Liu, and H Howie Huang. 2019. CECI: Compact Embedding Cluster Index for Scalable Subgraph Matching. In SIGMOD.

Digital Library

[3]

Fei Bi, Lijun Chang,Xuemin Lin, Lu Qin, and Wenjie Zhang. 2016. Efficient subgraph matching by postponing cartesian products. In SIGMOD.

[4]

Sutanay Choudhury,Lawrence B Holder, Abhik Ray,George Chin Jr, and John T Feo. 2012. Continuous queries for multi-relational graphs. CoRR, Vol. abs/1209.2178 (2012).

[5]

Luigi P Cordella, Pasquale Foggia, Carlo Sansone, and Mario Vento. 2004. A (sub) graph isomorphism algorithm for matching large graphs. TPAMI, Vol. 26, 10 (2004), 1367--1372.

Digital Library

[6]

Wenfei Fan, Chunming Hu,and Chao Tian. 2017. Incremental graph computations: Doable and undoable. In SIGMOD.

[7]

Wenfei Fan, Xueli Liu, Ping Lu, and Chao Tian. 2018. Catching Numeric Inconsistencies in Graphs. In SIGMOD.

[8]

Wenfei Fan and Ping Lu. 2019. Dependencies for Graphs. TODS, Vol. 44, 2 (2019), 5:1--5:40.

[9]

Wenfei Fan, Yinghui Wu,and Jingbo Xu. 2016. Functional dependencies for graphs. In SIGMOD.

[10]

Sheldon Finkelstein. 1982. Common expression analysis in database applications. In SIGMOD.

[11]

Zvi Galil and Giuseppe F. Italiano. 1991. Data Structures and Algorithms for Disjoint Set Union Problems. ACM Comput. Surv., Vol. 23, 3 (1991), 319--344.

Digital Library

[12]

Michael Garey and David Johnson. 1979. Computers and Intractability: A Guide to the Theory of NP-Completeness .W. H. Freeman and Company.

Digital Library

[13]

Gösta Grahne. 1991. The Problem of Incomplete Information inRelational Databases. Springer.

[14]

Wook-Shin Han, JinsooLee, and Jeong-Hoon Lee. 2013. Turbo iso: Towards ultrafast and robust subgraph isomorphism search in large graph databases. In SIGMOD.

[15]

Huahai He and Ambuj KSingh. 2008. Graphs-at-a-time: Query language and access methods for graph databases. In SIGMOD.

Digital Library

[16]

Tomasz Imielinski and Witold Lipski, Jr. 1984. Incomplete Information in Relational Databases. JACM, Vol. 31, 4 (1984).

Digital Library

[17]

Chathura Kankanamge. 2018. Multiple Continuous Subgraph Query Optimization Using Delta Subgraph Queries. Master's thesis. University of Waterloo.

[18]

Chathura Kankanamge,Siddhartha Sahu, Amine Mhedbhi, Jeremy Chen, and Semih Salihoglu. 2017. Graphflow: An active graph database. In SIGMOD.

Digital Library

[19]

Ina Kim, Kyong-Ha Lee,and Kyu-Chul Lee. 2018a. SAMUEL: A Sharing-based Approach to processing Multiple SPARQL Queries with MapReduce. In EDBT.

[20]

Kyoungmin Kim, In Seo,Wook-Shin Han, Jeong-Hoon Lee,Sungpack Hong, Hassan Chafi, Hyungyu Shin, and Geonhwa Jeong. 2018b. TurboFlux: A fast continuous subgraph matching system for streaming graph data. In SIGMOD.

[21]

Anthony C. Klug. 1988. On conjunctive queries containing inequalities. J. ACM, Vol. 35, 1 (1988), 146--160.

Digital Library

[22]

Wangchao Le, Anastasios Kementsietsidis, Songyun Duan, and Feifei Li. 2012. Scalable Multi-query Optimization for SPARQL. In ICDE.

[23]

Han Myoungji, KimHyunjoon, Gu Geonmo, Park Kunsoo, andHan Wook-Shin. 2019. Efficient Subgraph Matching: Harmonizing Dynamic Programming, Adaptive Matching Order, and Failing Set Together. In SIGMOD.

[24]

Jooseok Park and ArieSegev. 1988. Using common subexpressions to optimize multiplequeries. In ICDE.

[25]

Jorge Pérez, Marcelo Arenas, and Claudio Gutierrez. 2009. Semantics and complexity of SPARQL. TODS, Vol. 34, 3 (2009), 16:1--16:45.

Digital Library

[26]

Xuguang Ren and Junhu Wang. 2016. Multi-Query Optimization for Subgraph Isomorphism Search. PVLDB, Vol. 10, 3 (2016), 121--132.

Digital Library

[27]

Marcus Schaefer and Christopher Umans. 2002. Completeness in the polynomial-time hierarchy: Acompendium. SIGACT news, Vol. 33, 3 (2002), 32--49.

[28]

Haichuan Shang, Ying Zhang, Xuemin Lin, and Jeffrey Xu Yu. 2008. Taming verification hardness: An efficient algorithm for testing subgraph isomorphism. PVLDB, Vol. 1, 1 (2008), 364--375.

Digital Library

[29]

Fabian M. Suchanek, Gjergji Kasneci, and Gerhard Weikum. 2007. Yago: A core of semantic knowledge. In WWW.

Digital Library

[30]

Zhao Sun, Hongzhi Wang,Haixun Wang, Bin Shao, and Jianzhong Li. 2012. Efficient subgraph matching on billion nodegraphs. PVLDB, Vol. 5, 9 (2012), 788--799.

Digital Library

[31]

Julian R Ullmann. 1976. An algorithm for subgraph isomorphism. JACM, Vol. 23, 1 (1976), 31--42.

Digital Library

[32]

Wiki. 2019. Alibaba Group. sl https://en.wikipedia.org/wiki/Alibaba_Group.

[33]

Lefteris Zervakis, VinaySetty, Christos Tryfonopoulos, and Katja Hose. 2019. Efficient Continuous Multi-Query Processing over Graph Streams. CoRR, Vol. abs/1902.05134 (2019).

[34]

Peixiang Zhao and Jiawei Han. 2010. On graph query optimization in large networks. PVLDB, Vol. 3, 1--2 (2010), 340--351.

Digital Library

[35]

Jingren Zhou, Per-AkeLarson, Johann-Christoph Freytag, and Wolfgang Lehner. 2007. Efficient exploitation of similar subexpressions for query processing. In SIGMOD.

Cited By

Lu PDeng TZhang HJin YLiu FMao TLiu L(2024)Ontology-Mediated Query Answering Using Graph Patterns with Conditions2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00036(382-395)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00036
Xu LChoi BPeng YXu JS Bhowmick S(2023)A Framework for Privacy Preserving Localized Graph Pattern Query ProcessingProceedings of the ACM on Management of Data10.1145/35892741:2(1-27)Online publication date: 20-Jun-2023
https://dl.acm.org/doi/10.1145/3589274
Jiang PWang WLiu XSun LDong B(2023)Event Association Analysis Using Graph RulesArtificial Neural Networks and Machine Learning – ICANN 202310.1007/978-3-031-44216-2_29(352-363)Online publication date: 22-Sep-2023
https://doi.org/10.1007/978-3-031-44216-2_29
Show More Cited By

Index Terms

Extending Graph Patterns with Conditions
1. Theory of computation
  1. Design and analysis of algorithms
    1. Data structures design and analysis
      1. Pattern matching

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cover image ACM Conferences

SIGMOD '20: Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data

June 2020

2925 pages

ISBN:9781450367356

DOI:10.1145/3318464

General Chairs:
David Maier
Portland State University, USA
,
Rachel Pottinger
University of British Columbia, Canada
,
Program Chairs:
AnHai Doan
University of Wisconsin, USA
,
Wang-Chiew Tan
Megagon Labs, USA
,
Publications Chairs:
Abdussalam Alawini
University of Illinois at Urbana-Champaign, USA
,
Hung Q. Ngo
RelationalAI, USA

Copyright © 2020 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 31 May 2020

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Research-article

Funding Sources

Royal Society Wolfson Research Merit Award
European Research Council
Shenzhen Institute of Computer Sciences
Engineering and Physical Sciences Research Council
Beijing Advanced Innovation Center for Big Data and Brain Computing
National Natural Science Foundation of China

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SIGMOD/PODS '20

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SIGMOD

SIGMOD/PODS '20: International Conference on Management of Data

June 14 - 19, 2020

OR, Portland, USA

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Overall Acceptance Rate 785 of 4,003 submissions, 20%

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Cited By

Lu PDeng TZhang HJin YLiu FMao TLiu L(2024)Ontology-Mediated Query Answering Using Graph Patterns with Conditions2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00036(382-395)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00036
Xu LChoi BPeng YXu JS Bhowmick S(2023)A Framework for Privacy Preserving Localized Graph Pattern Query ProcessingProceedings of the ACM on Management of Data10.1145/35892741:2(1-27)Online publication date: 20-Jun-2023
https://dl.acm.org/doi/10.1145/3589274
Jiang PWang WLiu XSun LDong B(2023)Event Association Analysis Using Graph RulesArtificial Neural Networks and Machine Learning – ICANN 202310.1007/978-3-031-44216-2_29(352-363)Online publication date: 22-Sep-2023
https://doi.org/10.1007/978-3-031-44216-2_29
Fan W(2022)Big graphsProceedings of the VLDB Endowment10.14778/3554821.355489915:12(3782-3797)Online publication date: 1-Aug-2022
https://dl.acm.org/doi/10.14778/3554821.3554899
Jamshidi KMariappan MVora KKalavri VSalihoğlu S(2022)Anti-vertex for neighborhood constraints in subgraph queriesProceedings of the 5th ACM SIGMOD Joint International Workshop on Graph Data Management Experiences & Systems (GRADES) and Network Data Analytics (NDA)10.1145/3534540.3534690(1-9)Online publication date: 12-Jun-2022
https://dl.acm.org/doi/10.1145/3534540.3534690
Sasaki YFletcher GMakoto O(2022)Language-aware Indexing for Conjunctive Path Queries2022 IEEE 38th International Conference on Data Engineering (ICDE)10.1109/ICDE53745.2022.00054(661-673)Online publication date: May-2022
https://doi.org/10.1109/ICDE53745.2022.00054
Min SPark SPark KGiammarresi DItaliano GHan W(2021)Symmetric continuous subgraph matching with bidirectional dynamic programmingProceedings of the VLDB Endowment10.14778/3457390.345739514:8(1298-1310)Online publication date: 21-Oct-2021
https://dl.acm.org/doi/10.14778/3457390.3457395
Song QLin PMa HWu Y(2021)Explaining Missing Data in Graphs: A Constraint-based Approach2021 IEEE 37th International Conference on Data Engineering (ICDE)10.1109/ICDE51399.2021.00131(1476-1487)Online publication date: Apr-2021
https://doi.org/10.1109/ICDE51399.2021.00131
Mahfoud H(2021)Expressive top-k matching for conditional graph patternsNeural Computing and Applications10.1007/s00521-021-06590-734:17(14205-14221)Online publication date: 29-Oct-2021
https://doi.org/10.1007/s00521-021-06590-7
Qiao RFeng KHe HZhong X(2020)Graph Pattern Matching: Capturing Bisimilar SubgraphInternational Journal of Pattern Recognition and Artificial Intelligence10.1142/S021800142150011735:03(2150011)Online publication date: 28-Oct-2020
https://doi.org/10.1142/S0218001421500117

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