Usage-Based Schema Matching

TLDR

Schema matching techniques are traditionally classified as schema‑based, instance‑based, or hybrid. This paper introduces usage‑based schema matching as a new class of techniques. The approach exploits query log information to identify attribute correspondences via co‑occurrence patterns, join usage, and aggregate functions, scoring similarities with several functions and using a genetic algorithm to select the best mappings. The method works even with opaque attribute names, can be combined with existing techniques for higher accuracy, and experimental results confirm its effectiveness.

Abstract

Existing techniques for schema matching are classified as either schema-based, instance-based, or a combination of both. In this paper, we define a new class of techniques, called usage-based schema matching. The idea is to exploit information extracted from the query logs to find correspondences between attributes in the schemas to be matched. We propose methods to identify co-occurrence patterns between attributes in addition to other features such as their use in joins and with aggregate functions. Several scoring functions are considered to measure the similarity of the extracted features, and a genetic algorithm is employed to find the highest- score mappings between the two schemas. Our technique is suitable for matching schemas even when their attribute names are opaque. It can further be combined with existing techniques to obtain more accurate results. Our experimental study demonstrates the effectiveness of the proposed approach and the benefit of combining it with other existing approaches.