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 have overlapping regions of interest, such as those arising from an exploratory analysis of the dataset by multiple users. In the second scenario, queries represent adjacent rectilinear sections that capture an irregular subregion in the multi-dimensional space. This scenario corresponds to a case where the user wants to query and retrieve a spatial feature from the dataset. We propose cost models and an algorithm for optimizing such queries. Our results using queries for subsetting and analysis of medical image datasets show that effective use of partial replicas can result in reduction in query execution times.
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