R-Tree Based Indexing of General Spatio-Temporal Data

Real-world objects are inherently spatially and temporally referenced, and many database applications rely on databases that record the past, present, and anticipated future locations of, e.g., people or land parcels. As a result, indices that efficiently support queries on the spatio-temporal extents of objects are needed. In contrast, past indexing research has progressed in largely separate spatial and temporal streams. In the former, focus has been on one-, two-, or three-dimensional space; and in the latter, focus has been on one or both of the temporal aspects, or dimensions, of data known as transaction time and valid time. Adding time dimensions to spatial indices, as if time was a spatial dimension, neither supports nor exploits the special properties of time. On the other hand, temporal indices are generally not amenable to extension with spatial dimensions. This paper proposes an efficient and versatile technique for the indexing of spatio-temporal data with discretely changing spatial extents: the spatial aspect of an object may be a point or may have an extent; both the transaction time and valid time are supported; and a generalized notion of the current time, now, is accommodated for the temporal dimensions. The technique extends the previously proposed R -tree and borrows from the GR-tree, and it provides means of prioritizing space versus time, enabling it to adapt to spatially and temporally restrictive queries. Performance experiments were performed to evaluate different aspects of the proposed indexing technique, and are included in the paper.