Temporal join processing with hilbert curve space mapping

ABSTRACT

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.

References

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Index Terms

• Information systems

  • Data management systems

    • Database management system engines

      • Database query processing

    • Information integration

      • Data warehouses

  • Information systems applications

    • Decision support systems

      • Data warehouses

• Theory of computation

  • Theory and algorithms for application domains

    • Database theory

      • Database query processing and optimization (theory)