In this paper, we discuss issues involving temporal data fragmentation, temporal query processing, and query optimization in multiprocessor database machines. We propose parallel processing strategies, which are based on partitioning of temporal relations on timestamp values, for multi-way joins (e

with eight cores and a shared L2 cache. Aggregation is an important aspect of query processing that is seemingly easy to understand and implement. Our research, however, demonstrates that a chip multiprocessor adds new dimensions to understanding hash-based aggregation performance— concurrent sharing

organization, data skew is likely to result during the processing of some user queries. For example, consider the pathological case of a system on which a population database is partitioned according to states: A based horizontal partitioning scheme based on the STATE attribute is use...

Abstract. Several studies have repeatedly demonstrated that both the performance and scalability of a shared-nothing parallel database system depend on the physical layout of data across the processing nodes of the system. Today, data is allocated in these systems using horizontal partitioning

the multithreaded parallel technique to optimize and process queries based on multi-core architecture. A set of algorithms for implementing and optimizing the best query plan, such as the algorithms for scheduling and parallel executing the query plan, the algorithms for allocating thread to sub-query and memory

are comparable to the actual workload. The hierarchical query execution strategy is proved much better than the usual flat strategy of parallel database systems, except some clearly identified extreme cases, where flat processing is better. Furthermore, we propose a declustering scheme for space optimization

The SQL language allows useIs to express queries that have nested subqueries in them. Optimization of nested queries has received considerable attention over the last few years. Most of the previous optimization work has assumed that at most one block is nested within any given block. The solutions

This paper presents a compile-time optimization methodology for complex relational query processing on a multiprocessor machine. A new scheduling algorithm is proposed to allocate the resources of the machine. A control mechanism traces the query processing and a special hierarchy of supervisors

Most of the existing relational database query optimizers generate multi-way join plans only from those linear ones to reduce the optimiza-tion overhead. For multiprocessor computer systems, this strategy seems inadequate since it may reduce the search space too much to gen-erate near-optimal plans

, by solving the difficult prob-lem of automating the migration of databases and the rewrit-ing of queries and updates. In this paper, we present the PRISM/PRISM++ system and the novel technology that made it possible. In particular, we focus on the difficult and previously unsolved problem of supporting