Efficient computation of temporal aggregates with range predicates
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Efficient aggregation over objects with extent
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Efficient integration and aggregation of historical information
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Temporal and spatio-temporal aggregations over data streams using multiple time granularities
Information Systems - Special issue: Best papers from EDBT 2002
Temporal Aggregation over Data Streams Using Multiple Granularities
EDBT '02 Proceedings of the 8th International Conference on Extending Database Technology: Advances in Database Technology
Incremental computation and maintenance of temporal aggregates
The VLDB Journal — The International Journal on Very Large Data Bases
Main Memory-Based Algorithms for Efficient Parallel Aggregation for Temporal Databases
Distributed and Parallel Databases
Spatiotemporal Aggregate Computation: A Survey
IEEE Transactions on Knowledge and Data Engineering
Historical spatio-temporal aggregation
ACM Transactions on Information Systems (TOIS)
On computing temporal aggregates with range predicates
ACM Transactions on Database Systems (TODS)
Using preaggregation to speed up scaling operations on massive spatio-temporal data
ER'10 Proceedings of the 29th international conference on Conceptual modeling
Processing count queries over event streams at multiple time granularities
Information Sciences: an International Journal
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The ability to model time-varying natures is essential to many database applications such as data warehousing and mining. However, the temporal aspects provide many unique characteristics and challenges for query processing and optimization. Among the challenges is computing temporal aggregates, which is complicated by having to compute temporal grouping.In this paper, we introduce a variety of temporal aggregation algorithms that overcome major drawbacks of previous work. First, for small-scale aggregations, both the worst-case and average-case processing time have been improved significantly. Second, for large-scale aggregations, the proposed algorithms can deal with a database that is substantially larger than the size of available memory.