Dynamic Similarity Search on Semistractured Multimedia Databases Using Multiple Similarity Measures

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Due to recent development of internet and computer technologies, importance of advanced information use has been increasing. In particular, for semistructured multimedia data such as multimedia XML data, we need technology to realize flexible information retrieval using multiple similarity measures like those based on data contents similarity, structural similarity, and relative and dynamic similarity incorporating context information. In this project, we have studied technical issues related to implementation of dynamic and adaptive retrieval, integration, and transformation of semistructured multimedia data using such different similarity measures. The main research items are as follows.1. Development of an extensible XML query processing system named X-QL which enables the use of different similarity measures in retrieval, summary generation, and transformation on XML data sets. A web information and application integration system lias also been developed on top of it.2. Study on processing of queries including foreign functions on XML views built on relational databases.3. Development of an example-based XML manipulation framework named AQUA and its experimental evaluation.4. Development of an XML query learning system XLearner integrating machine learning techniques and XML processing and its experimental evaluation.5. Study on web search methods using taxonomy information as contexts for web search. The study includes experimental evaluation using real web sites such as ODP and Google.6. Development of a continuous query processing system which provides neighborhood information for moving objects. Position and movement information of moving objects are used as contexts for information extraction.7. Study on an architecture and query processing schemes for integrating multiple streams as a basis for flexible manipulation of semistructured data streams.