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Graph mining for discovering infrastructure patterns in configuration management databases

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Abstract

A configuration management database (CMDB) can be considered to be a large graph representing the IT infrastructure entities and their interrelationships. 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 subgraph 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, as well as synthetic graphs.

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Authors and Affiliations

  1. Department of Computer Science, Rensselaer Polytechnic Institute, Troy, NY, 12180, USA

    Pranay Anchuri & Mohammed J. Zaki

  2. HP Labs, 32000 , Technion City, Haifa, Israel

    Omer Barkol, Ruth Bergman, Yifat Felder, Shahar Golan & Arik Sityon

Authors
  1. Pranay Anchuri
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  2. Mohammed J. Zaki
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  3. Omer Barkol
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  4. Ruth Bergman
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  5. Yifat Felder
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  6. Shahar Golan
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  7. Arik Sityon
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Corresponding author

Correspondence to Mohammed J. Zaki.

Additional information

This work was supported by the HP Labs Innovation Research Program Award, and in part by NSF Grant EMT-0829835.

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Anchuri, P., Zaki, M.J., Barkol, O. et al. Graph mining for discovering infrastructure patterns in configuration management databases. Knowl Inf Syst 33, 491–522 (2012). https://doi.org/10.1007/s10115-012-0528-3

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  • DOI: https://doi.org/10.1007/s10115-012-0528-3

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