Distributed Data Management: Web, Clusters and Grid Marta Mattoso Return to Keynotes Abstract Distributed and parallel processing techniques have been responsible for significant performance improvements in large database management systems. However new distributed scenarios, such as the Web, Clusters of PCs and Grids, pose research challenges on the database research community. On the Web scenario, dynamic interoperation of highly distributed and heterogeneous Web services is becoming very popular. Database technology must extend data management to process management and help the automated discovery, composition, enactment, and monitoring of collections of web services. Experience in data modeling and data query can help current research in modeling Web services and their compositions, and service discovery for automatic composition, among others. Parallel database algorithms are being revisited to work on a database cluster, which is a cluster of PC servers, each running an off-the-shelf sequential DBMS. This is cost-effective solution whose challenge is to preserve autonomy. Finally Grid environments bring similar challenges including data and services placement in high performance grids and peer-to-peer scenarios. This talk will cover some of the techniques that are being used by the database community. Some examples will be given on commercial applications such as OLAP and scientific applications such as bioinformatics, which are the main benefiDatabase technology must extend data management to process management and help the automated discovery, composition, enactment, and monitoring of collections of web services. Experience in data modeling and data query can help current research in modeling Web services and their compositions, and service discovery for automatic composition, among others. Parallel database algorithms are being revisited to work on a database cluster, which is a cluster of PC servers, each running an off-the-shelf sequential DBMS. This is cost-effective solution whose challenge is to preserve autonomy. Finally Grid environments bring similar challenges including data and services placement in high performance grids and peer-to-peer scenarios. This talk will cover some of the techniques that are being used by the database community. Some examples will be given on commercial applications such as OLAP and scientific applications such as bioinformatics, which are the main beneficiaries of these new distributed data management research effort. ©2006 Association for Computing Machinery