In distributed database systems, tables are frequently fragmented and replicated over a number of sites in order to reduce network communication costs. How to fragment, when to replicate and how to allocate the fragments to the sites are challenging problems that has previously been solved either by static fragmentation, replication and allocation, or based on a priori query analysis. Many emerging applications of distributed database systems generate very dynamic workloads with frequent changes in access patterns from different sites. In such contexts, continuous refragmentation and reallocation can significantly improve performance. In this paper we present DYFRAM, a decentralized approach for dynamic table fragmentation and allocation in distributed database systems based on observation of the access patterns of sites to tables. The approach performs fragmentation, replication, and reallocation based on recent access history, aiming at maximizing the number of local accesses compared to accesses from remote sites. We show through simulations and experiments on the DASCOSA distributed database system that the approach significantly reduces communication costs for typical access patterns, thus demonstrating the feasibility of our approach.

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Semantic caching augments cached data with a semantic description of the data. These semantic descriptions can be used to improve execution time for similar queries by retrieving some data from cache and issuing a remainder query for the rest. This is an improvement over traditional page caching, since caches are no longer limited to only base tables but are extended to contain intermediate results. In large-scale distributed database systems, using a central server with complete knowledge of the system will be a serious bottleneck and single point of failure. In this paper, we propose a distributed semantic caching method where sites make autonomous caching decisions based on locally available information, thereby reducing the need for centralized control. We implement the method in the DASCOSA-DB distributed database system prototype and use this implementation to do experiments that show the applicability and efficiency of our approach. Our evaluation shows that execution times for queries with similar subqueries are significantly reduced and that overhead caused by cache management is marginal.

This paper presents a novel approach for database searching where the user is assisted in locating relevant objects in query results. This is made possible by an active user interface which asks the user dynamically generated questions based on collected information of the properties of the objects in the result. The answers to these questions are used to retain only objects with the indicated properties, thereby improving precision. SESAM, a World Wide Web-based prototype based on this approach, is presented. It has been successfully tested using a large, real-life database.

Computational science applications performing distributed computations using grid networks are now emerging. These applications have new and demanding requirements for efficient query processing. In order to meet these requirements, we have developed the DASCOSA-DB distributed database system. In this chapter, a detailed overview of the architecture and implementation of DASCOSA-DB is given, as well as a description of novel features developed in order to better support typical data-intensive applications running on a grid system: fault-tolerant query processing, dynamic refragmentation, allocation and replication of data fragments, and distributed semantic caching.

In a number of application areas, distributed database systems can be used to provide persistent storage of data while providing efficient access for both local and remote data. With an increasing number of sites (computers) involved in a query, the probability of failure at query time increases. Recovery has previously only focused on database updates while query failures have been handled by complete restart of the query. This technique is not always applicable in the context of large queries and queries with deadlines. In this paper we present an approach for partial restart of queries that incurs minimal extra network traffic during query recovery. Based on results from experiments on an implementation of the partial restart technique in a distributed database system, we demonstrate its applicability and significant reduction of query cost in the presence of failures.

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