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.

The appropriate deployment of web service operations at the service provider site plays a critical role in the efficient provision of services to clients. In this paper, the authors assume that a service provider has several servers over which web service operations can be deployed. Given a workflow of web services and the topology of the servers, the most efficient mapping of operations to servers must then be discovered. Efficiency is measured in terms of two cost functions that concern the execution time of the workflow and the fairness of the load distribution among the servers. The authors study different topologies for the workflow structure and the server connectivity and propose a suite of greedy algorithms for each combination.