R-Tree Based Indexing of Now-Relative Bitemporal Data

The databases of a wide range of applications, e.g., in data warehousing, store multiple states of time-evolving data. These databases contain a substantial part of now-relative data: data that became valid at some past time and remains valid until the current time. More specifically, two temporal aspects of data are frequently of interest, namely valid time, when data is true, and transaction time, when data is current in the database, leading to bitemporal data. Only little work, based mostly on R-trees, has addressed the indexing of bitemporal data. No indices exist that contend well with now-relative data, which leads to temporal data regions that are continuous functions of time. The paper proposes two extended R -trees that permit the indexing of data regions that grow continuously over time, by also letting the internal bounding regions grow. Internal bounding regions may be triangular as well as rectangular. New heuristics for the algorithms that govern the index structure are provided. As a result, dead space and overlap, now also functions of time, are reduced. Performance studies indicate that the best extended index is typically 3‐5 times faster than the existing R-tree based indices.