Efficient Index Structures for String Databases
Proceedings of the 27th International Conference on Very Large Data Bases
Complex spatio-temporal pattern queries
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Email Surveillance Using Non-negative Matrix Factorization
Computational & Mathematical Organization Theory
Mixed-Drove Spatio-Temporal Co-occurence Pattern Mining: A Summary of Results
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Pattern Mining in Frequent Dynamic Subgraphs
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
OASIS: an online and accurate technique for local-alignment searches on biological sequences
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Automated social hierarchy detection through email network analysis
Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis
Graphs-at-a-time: query language and access methods for graph databases
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Discovering correlated spatio-temporal changes in evolving graphs
Knowledge and Information Systems
Trend Motif: A Graph Mining Approach for Analysis of Dynamic Complex Networks
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Mining Periodic Behavior in Dynamic Social Networks
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
GADDI: distance index based subgraph matching in biological networks
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
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An evolving graph is a graph that can change over time. Such graphs can be applied in modelling a wide range of real-world phenomena, like computer networks, social networks and protein interaction networks. This paper addresses the novel problem of querying evolving graphs using spatio-temporal patterns. In particular, we focus on answering selection queries, which can discover evolving subgraphs that satisfy both a temporal and a spatial predicate. We investigate the efficient implementation of such queries and experimentally evaluate our techniques using real-world evolving graph datasets --- Internet connectivity logs and the Enron email corpus. We show that is possible to use queries to discover meaningful events hidden in this data and demonstrate that our implementation is scalable for very large evolving graphs.