Discovering spatiotemporal event sequences

Berkay Aydin, Rafal Angryk
<span title="">2016</span> <i title="ACM Press"> Proceedings of the 5th ACM SIGSPATIAL International Workshop on Mobile Geographic Information Systems - MobiGIS &#39;16 </i> &nbsp;
Finding frequent patterns plays a vital role in many analytics tasks such as finding itemsets, associations, correlations, and sequences. In recent decades, spatiotemporal frequent pattern mining has emerged with the main goal focused on developing datadriven analysis frameworks for understanding underlying spatial and temporal characteristics in massive datasets. In this thesis, we will focus on discovering spatiotemporal event sequences from large-scale region trajectory datasetes with event
more &raquo; ... nnotations. Spatiotemporal event sequences are the series of event types whose trajectory-based instances follow each other in spatiotemporal context. We introduce new data models for storing and processing evolving region trajectories, provide a novel framework for modeling spatiotemporal follow relationships, and present novel spatiotemporal event sequence mining algorithms.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="">doi:10.1145/3004725.3004735</a> <a target="_blank" rel="external noopener" href="">fatcat:3m7tehj3yjawdg2htdvuwn6guu</a> </span>
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