Approximate Processing for Medical Record Linking and Multidatabase Analysis

In this article we investigate how approximate query processing (AQP) can be used in medical multidatabase systems. We identify two areas where this estimation technique will be of use. First, approximate query processing can be used to preprocess medical record linking in the multidatabase. Second, approximate answers can be given for aggregate queries. In the case of multidatabase systems used to link health and health related data sources, preprocessing can be used to find records related to the same patient. This may be the first step in the linking strategy. If the aim is to gather aggregate statistics, then the approximate answers may be enough to provide the required answers. At least they may provide initial answers to encourage further investigation. This estimation may also be used for general query planning and optimization, important in multidatabase systems. In this article we propose two techniques for the estimation. These techniques enable synopses of component local databases to be precalculated and then used for obtaining approximate results for linking records and for aggregate queries. The synopses are constructed with restrictions on the storage space. We report on experiments which show that good approximate results can be obtained in a much shorter time than performing the exact query.

Entity Identification of Fuzzy Multidatabase Systems with Incompatible Keys

Database schema integration is an important discipline for constructing heterogeneous multidatabase systems on one hand. On the other hand, fuzzy information has been introduced into relational databases and studied extensively. However, the issues of integrating local fuzzy relations are rarely addressed. In this chapter, we focus on schema integration of fuzzy multidatabase systems. In particular, we develop a method for identifying the entities in fuzzy multidatabase integration with incompatible keys.

Semantic Integration in Multidatabase Systems

This chapter reviews briefly the semantic integration issues in multidatabase development and provides a standardized representation for classifying semantic conflicts. We begin by summarizing the methods and issues in multidatabase design. From the perspective of database integration, we identify that semantic conflict is the main issue. We explore the idea further by examining semantic conflicts and propose taxonomy to classify semantic conflicts in different groups. This taxonomy is then evaluated by two different methods. Finally, we conclude by discussing the limits of database integration and how this challenge may be addressed.