The boolean basis problem and how to cover some polygons by rectangles
SIAM Journal on Discrete Mathematics
On the hardness of approximating minimization problems
Journal of the ACM (JACM)
Continuously adaptive continuous queries over streams
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
TAG: a Tiny AGgregation service for ad-hoc sensor networks
ACM SIGOPS Operating Systems Review - OSDI '02: Proceedings of the 5th symposium on Operating systems design and implementation
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Processing set expressions over continuous update streams
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
The design of an acquisitional query processor for sensor networks
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Gigascope: a stream database for network applications
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Power-conserving computation of order-statistics over sensor networks
PODS '04 Proceedings of the twenty-third ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Holistic aggregates in a networked world: distributed tracking of approximate quantiles
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Multiple aggregations over data streams
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Sketching streams through the net: distributed approximate query tracking
VLDB '05 Proceedings of the 31st international conference on Very large data bases
On-the-fly sharing for streamed aggregation
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Multi-query optimization for sensor networks
DCOSS'05 Proceedings of the First IEEE international conference on Distributed Computing in Sensor Systems
Moara: flexible and scalable group-based querying system
Proceedings of the 9th ACM/IFIP/USENIX International Conference on Middleware
Optimized union of non-disjoint distributed data sets
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Efficient on-demand operations in dynamic distributed infrastructures
LADIS '08 Proceedings of the 2nd Workshop on Large-Scale Distributed Systems and Middleware
Ranking distributed probabilistic data
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Dynamic Query Processing for P2P Data Services in the Cloud
DEXA '09 Proceedings of the 20th International Conference on Database and Expert Systems Applications
Opportunistic sampling in wireless sensor networks
Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
On-line sensing task optimization for shared sensors
Proceedings of the 9th ACM/IEEE International Conference on Information Processing in Sensor Networks
Optimized processing of multiple aggregate continuous queries
Proceedings of the 20th ACM international conference on Information and knowledge management
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An emerging challenge in modern distributed querying is to efficiently process multiple continuous aggregation queries simultaneously. Processing each query independently may be infeasible, so multi-query optimizations are critical for sharing work across queries. The challenge is to identify overlapping computations that may not be obvious in the queries themselves. In this paper, we reveal new opportunities for sharing work in the context of distributed aggregation queries that vary in their selection predicates. We identify settings in which a large set of q such queries can be answered by executing k . The k queries are revealed by analyzing a boolean matrix capturing the connection between data and the queries that they satisfy, in a manner akin to familiar techniques like Gaussian elimination. Indeed, we identify a class of linear aggregate functions (including SUM, COUNT and AVERAGE), and show that the sharing potential for such queries can be optimally recovered using standard matrix decompositions from computational linear algebra. For some other typical aggregation functions (including MIN and MAX) we find that optimal sharing maps to the NP-hard set basis problem. However, for those scenarios, we present a family of heuristic algorithms and demonstrate that they perform well for moderate-sized matrices. We also present a dynamic distributed system architecture to exploit sharing opportunities, and experimentally evaluate the benefits of our techniques via a novel, flexible random workload generator we develop for this setting.