Light-Weight Indexing of General Bitemporal Data

Most data managed by existing, real-world database applications is time referenced. Data warehouses are good examples. Often, two temporal aspects of data are of interest, namely valid time, when data is true in the mini-world, and transaction time, when data is current in the database, resulting in so-called bitemporal data. Like spatial data, bitemporal data thus has associated two-dimensional regions. Such data is in part naturally now-relative: some data is currently true in the mini-world or is part of the current database state. So, unlike for spatial data, the regions of now-relative bitemporal data grow continuously. Existing indices, including commercially available indices such as B+and R-trees, typically do not contend well with even small amounts of now-relative data. This paper proposes a new indexing technique that indexes general bitemporal data efficiently. The technique eliminates the different kinds of growing data regions by means of transformations and then indexes the resulting stationary data regions with four R -trees, and queries on the original data are mapped to corresponding queries on the transformed data. Extensive performance studies are reported that provide insight into the characteristics and behavior of the four trees storing differently-shaped regions, and they indicate that the new technique yields a performance that is competitive with the best existing index; and unlike this existing index, the new technique does not require extension of the kernel of the DBMS.