Dynamic database integration for knowledge sharing

点击翻译按钮获取中文摘要

Organizations are drowning in data. The basic question is how can we manage all this data and turn it into valuable information?  Data is scattered around different systems, in different formats, and controlled by various organizations. It is like a giant jigsaw puzzle.  In order to answer questions, you need pieces from various places, and only when the pieces are put together can meaningful results be produced.  The key to turning data into information are the tools that allow you to construct the whole picture from the individual pieces.   This proposal aims to develop the algorithms and associated tools to help all users find, query, and integrate data from many data sources.  The approaches are applicable to the integration of medical information, business data, and the dissemination and integration of the vast amounts of scientific data.   Database integration is the process of combining data from multiple data sources into a single unified information system.  Although advances have been made including integration architectures and schema matching algorithms, database integration remains a challenging and human-intensive problem.  One of the major challenges is the construction of an integrated view combining the information in all data sources.    The proposed research is to develop techniques for supporting rapid, dynamic integration of database systems.  The research is in two areas: query processing and optimization for integration systems and ontology-based schema matching and merging.  Evaluating and ordering joins in distributed query processing is critical to achieving high performance.  Research will be performed on how to improve the hash-based early join algorithms and utilize them with optimizers specific for the integration environment. The second area of study is using ontologies (domain representations suitable for computer processing) to improve the accuracy of matching schemas.  The goal is to support iterative schema merging and refinement to produce an integrated view.  The research will use the previously constructed Unity integration architecture for experimentation and evaluation of join algorithms, query optimization heuristics, and schema merging approaches.