ABSTRACT
With more and more large networks becoming available, mining and querying such networks are increasingly important tasks which are not being supported by database models and querying languages. This paper wants to alleviate this situation by proposing a data model and a query language for facilitating the analysis of networks. Key features include support for executing external tools on the networks, flexible contexts on the network each resulting in a different graph, primitives for querying subgraphs (including paths) and transforming graphs.
The data model provides for a closure property, in which the output of every query can be stored in the database and used for further querying.
- B. Amann and M. Scholl. Gram: a graph data model and query language. In HT, pages 201--211. ACM, 1992. Google ScholarDigital Library
- R. Angles and C. Gutierrez. Survey of graph database models. ACM Computing Surveys, 40(1):1--39, 2008. Google ScholarDigital Library
- I. Bhattacharya and L. Getoor. Collective entity resolution in relational data. Data Engineering Bulletin, 29(2):4--12, 2006.Google Scholar
- S. Brin and L. Page. The anatomy of a large-scale hypertextual web search engine. Computer Networks, 30(1-7):107--117, 1998. Google ScholarDigital Library
- R. H. Guting. GraphDB: Modeling and querying graphs in databases. In VLDB, pages 297--308. Morgan Kaufmann Publishers Inc., 1994. Google ScholarDigital Library
- M. Gyssens, J. Paredaens, and D. van Gucht. A graph-oriented ob ject database model. In PODS, pages 417--424. ACM, 1990. Google ScholarDigital Library
- J. Han, L. V. S. Lakshmanan, and R. T. Ng. Constraint-based multidimensional data mining. IEEE Computer, 32(8):46--50, 1999. Google ScholarDigital Library
- H. He and A. K. Singh. Graphs-at-a-time: query language and access methods for graph databases. In SIGMOD, pages 405--418. ACM, 2008. Google ScholarDigital Library
- J. Hidders. Typing graph manipulation operations. In ICDT, pages 394--409. Springer-Verlag, 2003. Google ScholarDigital Library
- T. Imielinski and H. Mannila. A database perspective on knowledge discovery. Communications of the ACM, 39(11):58--64, 1996. Google ScholarDigital Library
- A. Inokuchi, T. Washio, and H. Motoda. Complete mining of frequent patterns from graphs: Mining graph data. Machine Learning, 50(3):321--354, 2003. Google ScholarDigital Library
- G. Karypis and V. Kumar. Multilevel k-way partitioning scheme for irregular graphs. Journal on Parallel and Distributed Computing, 48(1):96--129, 1998. Google ScholarDigital Library
- U. Leser. A query language for biological networks. Bioinformatics, 21(2):33--39, 2005. Google ScholarDigital Library
- M. Levene and A. Poulovassilis. The hypernode model and its associated query language. In JCIT, pages 520--530. IEEE Computer Society Press, 1990. Google ScholarDigital Library
- M. Levene and A. Poulovassilis. An object-oriented data model formalised through hypergraphs. Data and Knowledge Engineering, 6(3):205--224, 1991. Google ScholarDigital Library
- R. Meo, G. Psaila, and S. Ceri. An extension to SQL for mining association rules. Data Mining and Knowledge Discovery, 2(2):195--224, 1998. Google ScholarDigital Library
- E. Prud'hommeaux and A. Seaborne. SPARQL query language for RDF. http://www.w3.org/TR/rdf-sparql-query/, 2008.Google Scholar
- P. Sen, G. Namata, M. Bilgic, L. Getoor, B. Gallagher, and T. Eliassi-Rad. Collective classification in network data. AI Magazine, 29(3):93--107, 2008.Google ScholarDigital Library
- L. Sheng, Z. Ozsoyoglu, and G. Ozsoyogly. A graph query language and its query processing. In ICDE, pages 572--581. IEEE Computer Society Press, 1999. Google ScholarDigital Library
- J. Wicker, L. Richter, K. Kessler, and S. Kramer. SINDBAD and SiQL: An inductive database and query language in the relational model. In ECML/PKDD (2), volume 5212 of Lecture Notes in Computer Science, pages 690--694. Springer, 2008.Google Scholar
- Z. Zeng, J. Wang, L. Zhou, and G. Karypis. Coherent closed quasi-clique discovery from large dense graph databases. In KDD, pages 797--802. ACM, 2006. Google ScholarDigital Library
Index Terms
- A query language for analyzing networks
Recommendations
Foundations of Modern Query Languages for Graph Databases
We survey foundational features underlying modern graph query languages. We first discuss two popular graph data models: edge-labelled graphs, where nodes are connected by directed, labelled edges, and property graphs, where nodes and edges can further ...
Performance of graph query languages: comparison of cypher, gremlin and native access in Neo4j
EDBT '13: Proceedings of the Joint EDBT/ICDT 2013 WorkshopsNoSQL and especially graph databases are constantly gaining popularity among developers of Web 2.0 applications as they promise to deliver superior performance when handling highly interconnected data compared to traditional relational databases. Apache ...
Analyzing graph databases by aggregate queries
MLG '10: Proceedings of the Eighth Workshop on Mining and Learning with GraphsAn important step in data analysis is the exploration of data. For traditional relational databases one of the most powerful tools for performing such analysis is the relational database and the aggregates and rankings that they can compute: for ...
Comments