Infrastructure pattern discovery in configuration management databases via large sparse graph mining

Pranay Anchuri, Mohammed J. Zaki, Omer Barkol, Ruth Bergman, Yifat Felder, Shahar Golan, Arik Sityon

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

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 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 bedefined 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.

Original languageEnglish
Title of host publicationProceedings - IEEE International Conference on Data Mining, ICDM
Pages11-20
Number of pages10
DOIs
Publication statusPublished - 1 Dec 2011
Externally publishedYes
Event11th IEEE International Conference on Data Mining, ICDM 2011 - Vancouver, BC, Canada
Duration: 11 Dec 201114 Dec 2011

Other

Other11th IEEE International Conference on Data Mining, ICDM 2011
CountryCanada
CityVancouver, BC
Period11/12/1114/12/11

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Keywords

  • Configuration management databases
  • Frequent subgraphs
  • Single graph mining

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Anchuri, P., Zaki, M. J., Barkol, O., Bergman, R., Felder, Y., Golan, S., & Sityon, A. (2011). Infrastructure pattern discovery in configuration management databases via large sparse graph mining. In Proceedings - IEEE International Conference on Data Mining, ICDM (pp. 11-20). [6137205] https://doi.org/10.1109/ICDM.2011.81