Temporal join processing with hilbert curve space mapping
Event Location / Date(s)
Gyeongju, Korea / March 24-28, 2014
Conference Name / Publication Title
Proceedings of the 29th ACM Symposium on Applied Computing
ISSN or ISBN
Management of data with a time dimension increases the overhead of storage and query processing in large database applications especially with the join operation, which is a commonly used and expensive relational operator. The join evaluation is difficult because temporal data are intrinsically multidimensional. The problem is harder since tuples with longer life spans tend to overlap a greater number of joining tuples thus; they are likely to be accessed more often. The proposed index-based Hilbert-Temporal Join (Hilbert-TJ) join algorithm maps temporal data into Hilbert curve space that is inherently clustered, thus allowing for fast retrieval and storage.
An evaluation and comparison study of the proposed Hilbert-TJ algorithm determined the relative performance with respect to a nested-loop join, a sort-merge, and a partition-based join algorithm that use a multiversion B+ tree (MVBT) index. The metrics include the processing time (disk I/O time plus CPU time) and index storage size. Under the given conditions, the expected outcome was that by reducing index redundancy better performance was achieved. Additionally, the Hilbert-TJ algorithm offers support to both valid-time and transaction-time data.
Raigoza, Jaime and Sun, Junping, "Temporal join processing with hilbert curve space mapping" (2014). CEC Faculty Proceedings, Presentations, Speeches and Lectures. 66.