Exploiting Parallelism to Accelerate Keyword Search on Deep-Web Sources
DILS '09 Proceedings of the 6th International Workshop on Data Integration in the Life Sciences
Optimizing joins in a map-reduce environment
Proceedings of the 13th International Conference on Extending Database Technology
Decentralized execution of linear workflows over web services
Future Generation Computer Systems
Adaptive parallelization of queries to data providing web service operations
Transactions on Large-Scale Data- and Knowledge-Centered Systems V
Parallel pipelined filter ordering with precedence constraints
ACM Transactions on Algorithms (TALG)
MobiS: a distributed paradigm of mobile sensor data analytics for evaluating environmental exposures
Proceedings of the First ACM SIGSPATIAL International Workshop on Mobile Geographic Information Systems
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We address the problem of minimizing the response time of a multi-way join query using pipelined (inter-operator) parallelism, in a parallel or a distributed environment. We observe that in order to fully exploit the parallelism in the system, we must consider a new class of "interleaving" plans, where multiple query plans are used simultaneously to minimize the response time of a query (or to maximize the tuple-throughput of the system). We cast the query planning problem in this environment as a "flow maximization problem", and present polynomial-time algorithms that (statically) find the optimal set of plans to use for a given query, for a large class of multi-way join queries. Our proposed algorithms also naturally extend to query optimization over web services. Finally we present an extensive experimental evaluation that demonstrates both the need to consider such plans in parallel query processing and the effectiveness of our algorithms.