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Infrastructure Pattern Discovery in Configuration Management Databases via Large Sparse Graph Mining

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7 Author(s)
Pranay Anchuri ; Dept. of Comput. Sci., Rensselaer Polytech. Inst., Troy, NY, USA ; Mohammed J. Zaki ; Omer Barkol ; Ruth Bergman
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A configuration management database (CMDB) can be considered to be a large graph representing the IT infrastructure entities and their inter-relationships. Mining such graphs is challenging because they are large, complex, and multi-attributed, and have many repeated labels. These characteristics pose challenges for graph mining algorithms, due to the increased cost of sub graph isomorphism (for support counting), and graph isomorphism (for eliminating duplicate patterns). The notion of pattern frequency or support is also more challenging in a single graph, since it has to be defined in terms of the number of its (potentially, exponentially many) embeddings. We present CMDB-Miner, a novel two-step method for mining infrastructure patterns from CMDB graphs. It first samples the set of maximal frequent patterns, and then clusters them to extract the representative infrastructure patterns. We demonstrate the effectiveness of CMDB-Miner on real-world CMDB graphs.

Published in:

2011 IEEE 11th International Conference on Data Mining

Date of Conference:

11-14 Dec. 2011