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Spatiotemporal event sequences (STESs) are the ordered series of event types whose evolving region-based instances frequently follow each other in time and are located closeby. Previous studies on STES mining require significance and prevalence thresholds for the discovery, which is usually unknown to domain experts. As the quality of the discovered STESs is of great importance to the domain experts...
Given a database of spatial trajectories reporting the movement of a set of objects in a time frame, the problem is to discover the groups of objects that stay in close proximity within a geographical area for a significant time. To deal with the problem, techniques for the discovery of collective patterns, e.g. the meeting pattern, have been proposed. Such techniques, however, impose stringent constraints...
We present a novel and configurable synthetic data generator for evolving region trajectories that emulates certain characteristics of a given input dataset, such as the spatial position, velocity, lifespan, and geometry shape and size. This tool aims to facilitate faster prototyping and evaluation of new spatiotemporal data mining algorithms that operate on a specific type of trajectory data, of...
In this paper, we propose a work flow for processing and analysing large-scale tracking data with spatio-temporal marks that uses an infrastructure for machine learning methods based on a meta-data representation of point patterns. The tracking log (IP address) of cyber security devices usually maps to geolocation and timestamp, such data is called spatiotemporal data. Existing spatio-temporal analysis...
Contrast patterns are itemsets that frequently occur in one dataset while not in another. These patterns have been successfully applied to many data mining domains, such as prediction, classification and clustering. However, none of the previous studies has considered extracting contrast patterns from different types of datasets. In this paper, we introduce a new type of contrast pattern, Conditional...
This paper explores recent achievements and novel challenges of the annoying privacy-preserving big data stream mining problem, which consists in applying mining algorithms to big data streams while ensuring the privacy of data. Recently, the emerging big data analytics context has conferred a new light to this exciting research area. This paper follows the so-depicted research trend.
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