Hashing Methods for Temporal Data
IEEE Transactions on Knowledge and Data Engineering
Improving Temporal Joins Using Histograms
DEXA '00 Proceedings of the 11th International Conference on Database and Expert Systems Applications
Handbook of massive data sets
Joining interval data in relational databases
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Join operations in temporal databases
The VLDB Journal — The International Journal on Very Large Data Bases
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Data warehouses as well as a wide range of other databases exhibit a strong temporal orientation: it is important to track the temporal variation of data over several months or years. In addition, databases often exhibit append-only characteristics where old data is retained while new data is appended. Performing joins efficiently on large databases such as these is essential to obtain good overall query processing performance. This paper presents a sort-merge-based incremental algorithm for time-oriented data. While incremental computation techniques have proven competitive in many settings, they also introduce a space overhead in the form of differential files. For the temporal data explored here, this overhead is avoided because the differential files are already part of the database. In addition, data is naturally sorted, leaving only merging. The incremental algorithm works in a partitioned storage environment and does not assume the availability of indices, making it a competitor to sort-based and nested-loop joins. The paper presents analytical as well as simulation-based characterizations of the performance of the join.