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Research on an Active Multi-database Architecture
and its Implementation 
Interview video about this project Name of Researcher:
Yasushi Kiyoki
Professor, the Faculty of Environmental Information, and the Graduate School of Media & Governance, Keio University Outline of Research:
By creating an interconnection mechanism among a lot of legacy databases, the values of those databases gain significantly.
In this cyber-knowledge project, we have studied a data integration method for the meta-level system which interconnects heterogeneous databases by computing spatial and temporal inter-relationships dynamically. This method integrates heterogeneous computational systems in the meta-level system. The application scope of this method can be expanded through hybridization of several systems for computing relationships among databases. The feature of the method is to realize data integration among heterogeneous databases by computing spatial and temporal relationships context-dependently. Our method have been examined through experiments in terms of the system implementation overhead, and our method is observed to reduce the implementation overhead through the experimental study. Details of the Research:
With the rapid progress of global network and database technologies, a large number of legacy databases are connected to the wide-area network. Those databases have been constructed and accessed independently in the wide-% area network environment. By implementing an interconnection mechanism among these legacy databases, the values of the legacy databases gain significantly. Particularly, it is effective to introduce the concept of spatial and temporal database computations to a meta-level of multidatabase environments, because this concept realizes the interconnection among heterogeneous databases according to spatial and temporal contexts. The meta-level of a multidatabase system means an abstracted and higher layer of local databases, and it would be constructed independently to the local systems. The interconnection, according to spatial and temporal contexts, means to join databases by computing spatio-temporal semantics and spatio-temporal relationships which involve contexts defined in specific relationships among local databases. In this study, we present a system architecture and an implementation method of a multidatabase system which realizes the interconnection by computing spatial and temporal relationships according to spatial and temporal contexts.
In a conventional approach for querying and integrating heterogeneous databases, the properties among data values like equality, synonymity, similarity or topology, which are defined as relationships, must be described statically as couples of pattern descriptions. The pattern descriptions are computed by using the pattern-matching technique with pattern descriptions of another database. This process which we call a relationship conversion realizes computational mechanisms for relationships rather than equality in the conventional approach. Currently, a large number of accessible databases are connected to the global network, therefore, an overhead for generating and updating the static descriptions becomes heavy to convert many kinds of relationships among the large number of databases to the single computational mechanism of equality. There are several research activities for converting the relationships to equality among heterogeneous databases automatically or semi-automatically. For example, an evaluation method of schema similarities using neural networks and an evaluation method for equality among data values of heterogeneous databases using ontology have been studied. Our research goals are to provide a system framework for computing the relationships between data values of heterogeneous databases and to realize data integration for heterogeneous databases in the global network environment. In this cyber-knowledge project, we have studied a data integration method for heterogeneous databases. The first feature of our method is that our method interconnects heterogeneous databases dynamically by computing relationships according to user contexts defined in the continuous value domain. This feature contributes to the data integrations among legacy databases. Our method makes it possible to produce more information from legacy databases by the interconnection than the conventional approach. The second feature is to integrate computational systems with the first feature by using the method. Such hybridization of existing computational methods strengthens their functionalities for interconnecting legacy databases, and its implementation overhead is less than the conventional approach. We have designed computational systems for spatial and temporal relationships with the first feature, and those systems are integrated using the second feature. Such implementations of these computational systems realize a query environment with spatial and temporal contexts to users, and legacy databases are interconnected spatio-temporally. In this study, we have shown several experimental results to evaluate that our method reduces the implementation overhead for multidatabase systems in the global network environment. Those experimental results have clarified the feasibility and effectiveness of our multidatabase system. |