Data schemes integration with algorithms for data summarization via archetypal analysis

Anton Zvonko Gazvoda (2014) Data schemes integration with algorithms for data summarization via archetypal analysis. MSc thesis.

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Abstract

Schema mapping discovery is key activity while performing data-level integration process and represents the basis for proper data transformation. For this purpose, we introduce novel instance-based schema matching method by using archetypal analysis in order to generate data summary for each schema element. Summary approximations are represented by convex hulls. We define several approaches for data transformation to vector space, as well as summary-similarity metrics. Two algorithms were developed in order to determine simple and complex matches. Our method was evaluated on the test data including proper mappings between schemas and compared with COMA CE schema matcher. Efficiency of our method was evaluated with sensitivity (91%), specificity (75%), accuracy (87%) and precision (91%). Compared with COMA CE, our method performs on average 20% better.

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