Mining abstract XML data-types

Schema integration has been a long-standing challenge for the data-engineering community that has received steady attention over the past three decades. General-purpose integration approaches construct unified schemas that encompass all schema elements. Schema integration has been revisited in the past decade in service-oriented computing since the input/output data-types of service interfaces are heterogeneous XML schemas. However, service integration differs from the traditional integration problem, since it should generalize schemas (mining abstract data-types) instead of unifying all schema elements. To mine well-formed abstract data-types, the fundamental Liskov Substitution Principle (LSP), which generally holds between abstract data-types and their subtypes, should be followed. However, due to the heterogeneity of service data-types, the strict employment of LSP is not usually feasible. On top of that, XML offers a rich type system, based on which data-types are defined via combining type patterns (e.g., composition, aggregation). The existing integration approaches have not dealt with the challenges of a defining subtyping relation between XML type patterns. To address these challenges, we propose a relaxed version of LSP between XML type patterns and an automated generalization process for mining abstract XML data-types. We evaluate the effectiveness and the efficiency of the process on the schemas of two datasets against two representative state-of-the-art approaches.