Improved histograms for selectivity estimation of range predicates
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Histogram-based estimation techniques in database systems
Histogram-based estimation techniques in database systems
Query Processing Algorithms for Temporal Intersection Joins
Proceedings of the Seventh International Conference on Data Engineering
Efficient Evaluation of the Valid-Time Natural Join
Proceedings of the Tenth International Conference on Data Engineering
Temporal Query Processing and Optimization in Multiprocessor Database Machines
VLDB '92 Proceedings of the 18th International Conference on Very Large Data Bases
The Representation of a Temporal Data Model in the Relational Environment
Proceedings of the 4th International Working Conference SSDBM on Statistical and Scientific Database Management
Efficient Temporal Join Processing Using Time Index
SSDBM '96 Proceedings of the Eighth International Conference on Scientific and Statistical Database Management
Efficient Processing of Time-Joins in Temporal Data Bases
Proceedings of the 3rd International Conference on Database Systems for Advanced Applications (DASFAA)
Join algorithm costs revisited
The VLDB Journal — The International Journal on Very Large Data Bases
Incremental Join of Time-Oriented Data
SSDBM '99 Proceedings of the 11th International Conference on Scientific and Statistical Database Management
Binary-Tree Histograms with Tree Indices
DEXA '02 Proceedings of the 13th International Conference on Database and Expert Systems Applications
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Histograms are used in most commercial database systems to estimate query result sizes and evaluation plan costs. They can also be used to optimize join algorithms. In this paper, we consider how to use histograms to improve the join processing in temporal databases. We define histograms for temporal data and a temporal join algorithm that makes use of this histogram information. The join algorithm is a temporal partition-join with dynamic buffer allocation. Histogram information is used to determine partition boundaries that maximize overall buffer usage. We compare the performance of this join algorithm to temporal join evaluation strategies that do not use histograms, such as a patition-based algorithm based on sampling and a partition-join using the Time Index, an index structure for temporal data. The results demonstrate that the temporal partition-join is substantially improved through the incorporation of histogram information, showing significantly better performance than the sampling-based algorithm and achieving equivalent performance to the Time Index join without requiring an index.