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
Efficient Algorithms for Large-Scale Temporal Aggregation
IEEE Transactions on Knowledge and Data Engineering
Incremental computation and maintenance of temporal aggregates
The VLDB Journal — The International Journal on Very Large Data Bases
Spatiotemporal Aggregate Computation: A Survey
IEEE Transactions on Knowledge and Data Engineering
Historical spatio-temporal aggregation
ACM Transactions on Information Systems (TOIS)
An efficient method for temporal aggregation with range-condition attributes
Information Sciences—Informatics and Computer Science: An International Journal
On computing temporal aggregates with range predicates
ACM Transactions on Database Systems (TODS)
Efficient temporal counting with bounded error
The VLDB Journal — The International Journal on Very Large Data Bases
Aggregates in Generalized Temporally Indeterminate Databases
SUM '07 Proceedings of the 1st international conference on Scalable Uncertainty Management
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The ability to model the temporal dimension is essential to many applications. Furthermore, the rate of increase in database size and response time requirements has out-paced advancements in processor and mass storage technology, leading to the need for parallel temporal database management systems. In this paper, we introduce a variety of parallel temporal aggregation algorithms for a shared-nothing architecture based on the sequential Aggregation Tree algorithm. Via an empirical study, we found that the number of processing nodes, the partitioning of the data, the placement of results, and the degree of data reduction effected by the aggregation impacted the performance of the algorithms. For distributed results placement, we discovered that Time Division Merge was the obvious choice. For centralized results and high data reduction, Pairwise Merge was preferred regardless of the number of processing nodes, but for low data reduction, it only performed well up to 32 nodes. This led us to a centralized variant of Time Division Merge which was best for larger configurations having low data reduction.