EXPERIENCE MANAGEMENT FOR IT MANAGEMENT SUPPORT
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This thesis focuses on the identification of experience required for solving IT (Information Technology) problems in small to medium sized enterprises. It is aimed to utilize information retrieval and data mining techniques to automatically extract information from publicly available experience data on the internet to automatically generate a knowledgebase for dynamic IT management support. In this thesis, similarity distance measures as Jaccard Index, Cosine Similarity Measure and clustering algorithms as K-Means, EM, DBScan, CES, CES+ are employed on three different datasets to evaluate their performances. CES+ algorithm gives the highest performance results in these evaluations. Moreover, Multi Objective Genetic Algorithm (MOGA) is used and is evaluated on three different data sets to aid the usage of CES+ in real life senarios by automating the selection of necessary parameters. Results show that MOGA support is not only automating the CES+, it also provides higher performance results.