Optimizing multiple queries on scientific datasets with partial replicas

We propose strategies to efficiently execute a query workload, which consists of multiple related queries submitted against a scientific dataset, on a distributed-memory system in the presence of partial dataset replicas. Partial replication re-organizes and re-distributes one or more subsets of a dataset across the storage system to reduce I/O overheads and increase I/O parallelism. Our work targets a class of queries, called range queries, in which the query predicate specifies lower and upper bounds on the values of all or a subset of attributes of a dataset. Data elements whose attribute values fall into the specified bounds are retrieved from the dataset. If we think of the attributes of a dataset forming multi-dimensional space, where each attribute corresponds to one of the dimensions, a range query defines a bounding box in this multi-dimensional space. We evaluate our strategies in two scenarios involving range queries. The first scenario represents the case in which queries ...

• Type: Conference
• Year: 2007
• Where: GRID
• Authors: Li Weng, Ümit V. Çatalyürek, Tahsin M. Kurç, Gagan Agrawal, Joel H. Saltz
• Keywords: Dataset, Distributed And Parallel Computing, GRID 2007, Partial Dataset Replicas, Range Queries