The choices for mining of decentralized data are numerous, and we have developed techniques to enumerate and optimize decentralized frequent itemset counting. We introduce our heuristic approach to improve the performance of such techniques developed in ways similar to query processing in database systems. We also describe empirical results that validate our heuristic techniques.

Powerful solutions enabling interoperability must allow applications to evolve and the requirements of shared databases to change, while minimizing such changes on other integrated applications. Several approaches have been proposed to make interoperability possible by using object-oriented techniques. These approaches may generate a large number of schema versions over time, resulting in an excessive build-up of classes and underlying object instances, not all being necessarily still in use. This results in degradation of system performance due to the view maintenance and the storage overhead costs. In this paper, we address the problem of removing obsolete view schemas. We characterize four potential problems of schema consistency that could be caused by the removal of a single derived class. We demonstrate that schema version removal is sensitive to the order in which individual classes are processed, and present a formal dependency model that captures all dependencies between classes as logic clauses and manipulates them to make decisions on class deletions and non-deletions while guaranteeing the consistency of the schema. We have also developed and proven consistent a dependency graph (DG) representation of the formal model. Lastly, we present a cost model for evaluating alternative removal patterns on a DG to assure selection of the optimal solution. The proposed techniques have been implemented in our Schema View Removal (SVR) tool. Lastly, we report experimental findings for applying our techniques for consistent schema version removal on the MultiView/TSE (Transparent Schema Evolution) system.