Recently, a fast increment of spatio-temporal data volume has been achieved and more importantly the data might distribute everywhere. So, there is a need for spatio-temporal data mining systems that are able to support such distributed spatio-temporal query and analysis operations. Distributed spatiotemporal data mining technologies were discussed in this paper. After discussing the process of...

Large volumes of spatio-temporal data are increasingly collected and studied in diverse domains including, climate science, social sciences, neuroscience, epidemiology, transportation, mobile health, and Earth sciences. Spatio-temporal data differs from relational data for which computational approaches are developed in the data mining community for multiple decades, in that both spatial and te...

Today, multielectrode arrays (MEAs) capture neuronal spike streams in real time, thus providing dynamic perspectives into brain function. Mining such spike streams from these MEAs is critical toward understanding the firing patterns of neurons and gaining insight into the underlying cellular activity. However, the acquisition rate of neuronal data places a tremendous computational burden on the...

One of the main unresolved problems that arise during the data mining process is treating data that contains temporal information. In this case, a complete understanding of the entire phenomenon requires that the data should be viewed as a sequence of events. Temporal sequences appear in a vast range of domains, from engineering, to medicine and finance, and the ability to model and extract inf...

In this paper we describe our approach to data mining in temporal databases by introducing Easy Miner, a data mining system developed at UMIST. This system implements a wide spectrum of data mining functions, including generalisation, relevance analysis, classification and discovery of association rules. By integrating these interesting data mining techniques, the system provides a user friendl...

Provenance of digital scientific data is an important piece of the metadata of a data object. It can however grow voluminous quickly because the granularity level of capture can be high. It can also be quite feature rich. We propose a representation of the provenance data based on logical time that reduces the feature space. Creating time and frequency domain representations of the provenance, ...