Random sampling with a reservoir
ACM Transactions on Mathematical Software (TOMS)
Practical selectivity estimation through adaptive sampling
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
New sampling-based summary statistics for improving approximate query answers
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Analysis and application of adaptive sampling
PODS '00 Proceedings of the nineteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Indexing the positions of continuously moving objects
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Congressional samples for approximate answering of group-by queries
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
A robust, optimization-based approach for approximate answering of aggregate queries
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Applying the golden rule of sampling for query estimation
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Selectivity estimation for spatio-temporal queries to moving objects
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Overcoming Limitations of Sampling for Aggregation Queries
Proceedings of the 17th International Conference on Data Engineering
ICICLES: Self-Tuning Samples for Approximate Query Answering
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Fast Incremental Maintenance of Approximate Histograms
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
On nearest neighbor indexing of nonlinear trajectories
Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Approximate join processing over data streams
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Extended wavelets for multiple measures
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Analysis of predictive spatio-temporal queries
ACM Transactions on Database Systems (TODS)
Performance evaluation of spatio-temporal selectivity estimation techniques
SSDBM '03 Proceedings of the 15th International Conference on Scientific and Statistical Database Management
The TPR*-tree: an optimized spatio-temporal access method for predictive queries
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Challenges in spatiotemporal stream query optimization
MobiDE '06 Proceedings of the 5th ACM international workshop on Data engineering for wireless and mobile access
Location-dependent query processing: Where we are and where we are heading
ACM Computing Surveys (CSUR)
An adaptive updating protocol for reducing moving object database workload
Proceedings of the VLDB Endowment
SSTD'05 Proceedings of the 9th international conference on Advances in Spatial and Temporal Databases
An adaptive updating protocol for reducing moving object database workload
The VLDB Journal — The International Journal on Very Large Data Bases
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Given a region q_R and a future timestamp q_T, a "range aggregate" query estimates the number of objects expected to appear in q_R at time q_T. Currently the only methods for processing such queries are based on spatio-temporal histograms, which have several serious problems. First, they consume considerable space in order to provide accurate estimation. Second, they incur high evaluation cost. Third, their efficiency continuously deteriorates with time. Fourth, their maintenance requires significant update overhead. Motivated by this, we develop Venn sampling (VS), a novel estimation method optimized for a set of "pivot queries" that reflect the distribution of actual ones. In particular, given m pivot queries, VS achieves perfect estimation with only O(m) samples, as opposed to O(2^m) required by the current state of the art in workload-aware sampling. Compared with histograms, our technique is much more accurate (given the same space), produces estimates with negligible cost, and does not deteriorate with time. Furthermore, it permits the development of a novel "query-driven" update policy, which reduces the update cost of conventional policies significantly.