Mining spatio-temporal data

TL;DR: Despite much formalization of space and time relations available in spatio-temporal reasoning, the extraction of spatial/ temporal relations implicitly defined in the data introduces some degree of fuzziness that may have a large impact on the results of the data mining process.

Abstract: Both the temporal and spatial dimensions add substantial complexity to data mining tasks. First of all, the spatial relations, both metric (such as distance) and non-metric (such as topology, direction, shape, etc.) and the temporal relations (such as before and after) are information bearing and therefore need to be considered in the data mining methods. Secondly, some spatial and temporal relations are implicitly defined, that is, they are not explicitly encoded in a database. These relations must be extracted from the data and there is a trade-off between precomputing them before the actual mining process starts (eager approach) and computing them on-the-fly when they are actually needed (lazy approach). Moreover, despite much formalization of space and time relations available in spatio-temporal reasoning, the extraction of spatial/ temporal relations implicitly defined in the data introduces some degree of fuzziness that may have a large impact on the results of the data mining process. J Intell Inf Syst (2006) 27: 187–190 DOI 10.1007/s10844-006-9949-3