Cited By
View all- Chen TQiu DWu YKhan AKe XGao Y(2024)View-based Explanations for Graph Neural NetworksProceedings of the ACM on Management of Data10.1145/36392952:1(1-27)Online publication date: 26-Mar-2024
Automatic View Selection in Graph Databases
Authors: 
Chao Zhang, 
Jiaheng Lu, 
Qingsong Guo, Xinyong Zhang, Xiaochun Han, Minqi ZhouAuthors Info & Claims
Chao Zhang, Jiaheng Lu, Qingsong Guo, Xinyong Zhang, Xiaochun Han, Minqi ZhouAuthors Info & ClaimsPages 197 - 202
Abstract
Recently, several works have studied the problem of view selection in graph databases. However, existing methods cannot fully exploit the graph properties of views, e.g., supergraph views and common subgraph views, which leads to a low view utility and duplicate view content. To address the problem, we propose an extended graph view that persists all the edge-induced subgraphs to answer the subgraph and supergraph queries simultaneously. Furthermore, we present the graph gene algorithm (GGA), which relies on a set of view transformations to reduce the view space and optimize the view benefit. Extensive experiments on real-life and synthetic datasets demonstrated GGA outperformed other selection methods in both effectiveness and efficiency.
References
[1]
Sanjay Agrawal, Surajit Chaudhuri, and Vivek R Narasayya. 2000. Automated selection of materialized views and indexes in SQL databases. In VLDB, Vol. 2000. 496–505.
[2]
David Beasley, David R Bull, and Ralph Robert Martin. 1993. An overview of genetic algorithms: Part 1, fundamentals. University computing 15, 2 (1993), 56–69.
[3]
Roger Castillo and Ulf Leser. 2010. Selecting materialized views for RDF data. In International Conference on Web Engineering. Springer, 126–137.
[4]
Leonardo Weiss F Chaves, Erik Buchmann, Fabian Hueske, and Klemens Böhm. 2009. Towards materialized view selection for distributed databases. In EDBT. 1088–1099.
[5]
Rada Chirkova, Jun Yang, 2012. Materialized views. Foundations and Trends® in Databases 4, 4 (2012), 295–405.
[6]
Luigi P Cordella, Pasquale Foggia, Carlo Sansone, and Mario Vento. 2004. A (sub) graph isomorphism algorithm for matching large graphs. TPAMI 26, 10 (2004), 1367–1372.
[7]
Joana MF da Trindade, Konstantinos Karanasos, Carlo Curino, Samuel Madden, and Julian Shun. 2020. Kaskade: Graph Views for Efficient Graph Analytics. In ICDE.
[8]
Orri Erling, Alex Averbuch, Josep Larriba-Pey, Hassan Chafi, Andrey Gubichev, Arnau Prat, Minh-Duc Pham, and Peter Boncz. 2015. The LDBC social network benchmark: Interactive workload. In SIGMOD. ACM, 619–630.
[9]
Wenfei Fan, Xin Wang, and Yinghui Wu. 2014. Answering graph pattern queries using views. In ICDE. IEEE, 184–195.
[10]
François Goasdoué, Konstantinos Karanasos, Julien Leblay, and Ioana Manolescu. 2011. View Selection in Semantic Web Databases. PVLDB 5, 2 (2011), 97–108.
[11]
Andrey Gubichev. 2015. Query Processing and Optimization in Graph Databases. Ph.D. Dissertation. Technische Universität München.
[12]
Himanshu Gupta and Inderpal Singh Mumick. 2005. Selection of views to materialize in a data warehouse. IEEE Transactions on Knowledge and Data Engineering 17, 1(2005), 24–43.
[13]
Asterios Katsifodimos, Ioana Manolescu, and Vasilis Vassalos. 2012. Materialized view selection for XQuery workloads. In SIGMOD. 565–576.
[14]
LDBC task force. 2019. The LDBC social network benchmark (version 0.3.2). Technical Report. Linked Data Benchmark Council.
[15]
Jinsoo Lee, Wook-Shin Han, Romans Kasperovics, and Jeong-Hoon Lee. 2012. An in-depth comparison of subgraph isomorphism algorithms in graph databases. Proceedings of the VLDB Endowment 6, 2 (2012), 133–144.
[16]
Jure Leskovec, Lada A Adamic, and Bernardo A Huberman. 2007. The dynamics of viral marketing. TWEB 1, 1 (2007), 5–es.
[17]
Jure Leskovec, Ajit Singh, and Jon Kleinberg. 2006. Patterns of influence in a recommendation network. In PAKDD. Springer, 380–389.
[18]
Imene Mami and Zohra Bellahsene. 2012. A survey of view selection methods. Acm Sigmod Record 41, 1 (2012), 20–29.
[19]
Bhushan Mandhani and Dan Suciu. 2005. Query caching and view selection for XML databases. In VLDB. VLDB Endowment, 469–480.
[20]
Neo4j. 2021. Cypher: the Neo4j graph query Language. https://neo4j.com/cypher-graph-query-language/.
[21]
Jie Tang, Jing Zhang, Limin Yao, Juanzi Li, Li Zhang, and Zhong Su. 2008. Arnetminer: extraction and mining of academic social networks. In SIGKDD. 990–998.
[22]
Nan Tang, Jeffrey Xu Yu, Hao Tang, M Tamer Özsu, and Peter Boncz. 2009. Materialized view selection in XML databases. In DASFAA. 616–630.
[23]
Robert Tarjan. 1972. Depth-first search and linear graph algorithms. SIAM journal on computing 1, 2 (1972), 146–160.
[24]
[24] Apache Tinkerpop.2020. https://tinkerpop.apache.org/docs/3.4.4/.
[25]
Haitao Yuan, Guoliang Li, Ling Feng, Ji Sun, and Yue Han. 2020. Automatic View Generation with Deep Learning and Reinforcement Learning. ICDE.
[26]
Chao Zhang, Jiaheng Lu, Qingsong Guo, Xinyong Zhang, Xiaochun Han, and Minqi Zhou. 2021. Automatic View Selection in Graph Databases. https://arxiv.org/abs/2105.09160.
Recommendations
Efficient algorithms for supergraph query processing on graph databases
We study the problem of processing supergraph queries on graph databases. A graph database D is a large set of graphs. A supergraph query q on D is to retrieve all the graphs in D such that q is a supergraph of them. The large ...
Graph Databases: Their Power and Limitations
Computer Information Systems and Industrial ManagementAbstractReal world data offers a lot of possibilities to be represented as graphs. As a result we obtain undirected or directed graphs, multigraphs and hypergraphs, labelled or weighted graphs and their variants. A development of graph modelling brings ...
Functional querying in graph databases
The paper is focused on a functional querying in graph databases. We consider labelled property graph model and mention also the graph model behind XML databases. An attention is devoted to functional modelling of graph databases both at a conceptual ...
Comments
Information & Contributors
Information
Published In
July 2021
275 pages
ISBN:9781450384131
DOI:10.1145/3468791
Copyright © 2021 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 11 August 2021
Check for updates
Author Tags
Qualifiers
- Short-paper
- Research
- Refereed limited
Conference
SSDBM 2021
SSDBM 2021: 33rd International Conference on Scientific and Statistical Database Management
July 6 - 7, 2021
FL, Tampa, USA
Acceptance Rates
Overall Acceptance Rate 56 of 146 submissions, 38%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- View Citations1Total Citations
- 131Total Downloads
- Downloads (Last 12 months)24
- Downloads (Last 6 weeks)0
Reflects downloads up to 27 Jul 2024
Other Metrics
Citations
Cited By
View all- Chen TQiu DWu YKhan AKe XGao Y(2024)View-based Explanations for Graph Neural NetworksProceedings of the ACM on Management of Data10.1145/36392952:1(1-27)Online publication date: 26-Mar-2024
View Options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format