Frequent subgraph mining from streams of linked graph structured data
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Date
2015
Authors
Cuzzocrea, Alfredo
Jiang, Fan
Leung, Carson K.
Journal Title
Journal ISSN
Volume Title
Publisher
CEUR Workshop Proceedings
Abstract
Nowadays, high volumes of high-value data (e.g., semantic web data) can be generated and published at a high velocity. A collection of these data can be viewed as a big, interlinked, dynamic graph structure of linked resources. Embedded in them are implicit, previously unknown, and potentially useful knowledge. Hence, efficient knowledge discovery algorithms for mining frequent subgraphs from these dynamic, streaming graph structured data are in demand. Some existing algorithms require very large memory space to discover frequent subgraphs; some others discover collections of frequently co-occurring edges (which may be disjoint). In contrast, we propose---in this paper---algorithms that use limited memory space for discovering collections of frequently co-occurring connected edges. Evaluation results show the effectiveness of our algorithms in frequent subgraph mining from streams of linked graph structured data.
Description
A. Cuzzocrea, F. Jiang, & C.K. Leung. Frequent subgraph mining from streams of linked graph structured data. In Proc. EDBT/ICDT Workshops 2015, pp. 237-244. This paper is published in the Workshop Proceedings of the EDBT/ICDT 2015 Joint Conference (March 27, 2015, Brussels, Belgium) on CEUR-WS.org (ISSN 1613-0073) under the terms of the Creative Commons license CC-by-nc-nd 4.0 (http://creativecommons.org/licenses/by-nc-nd/4.0).
Keywords
data mining, frequent patterns, graph structured data, linked data, extending database technology, database theory
Citation
A. Cuzzocrea, F. Jiang, & C.K. Leung. Frequent subgraph mining from streams of linked graph structured data. In Proc. EDBT/ICDT Workshops 2015, pp. 237-244.