Mining, indexing, and querying historical spatiotemporal data

By Nikos Mamoulis, H. Cao, George Kollios, Marios Hadjieleftheriou, Yufei Tao, David Wai-Lok Cheung. Knowledge Discovery and Data Mining, 2004

329 citations recorded by the source corpus.

Subfields: Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/4548b411a40c767e3e08b08deccd23878d810d97

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Citations in the corpus

Discovering popular routes from trajectories (2011, background): “The search of popular routes in light of past movements is highly relevant to trajectory processing/querying issues, including pattern mining [7], [2], [1], [8], trajectory clustering [9], [10], hot route discovery [11], [12], trajectory prediction [3], [4], [13], etc.”
Mining interesting locations and travel sequences from GPS trajectories (2009, background): “[16] proposed a framework for retrieving maximum periodic patterns in spatio-temporal data.”
Spatio-Temporal Data Mining (2017, background): “, for trajectory pattern mining (Giannotti et al. 2007; Mamoulis et al. 2004) and time-series data mining (Ding et al.”
Mining frequent spatio-temporal sequential patterns (2005, background): “The contribution of [9] is that it does not trea t spatio-temporal series as event sequences, by merely repla cing each location by a predefined region enclosing it, but automatically discovers the regions that form the patterns .”
Trajectory pattern mining (2007, background): “Finally, a similar goal, but focused on cyclic patterns, is pursued in [8]: the authors propose an effective and fast mining algorithm for retrieving maximal periodic patterns, treating time as discrete, yet dealing with continuous spatial locations that are discretized dynamically through densityba”
A probabilistic approach to spatiotemporal theme pattern mining on weblogs (2006, background): “Spatiotemporal data mining on numerical data and moving objects has been well studied [5, 20].”
A Hybrid Prediction Model for Moving Objects (2008, methodology): “For the first component, we adopt the periodical pattern mining methods [10] which discover objects' patterns that follow the same routes over regular time intervals.”
Mining periodic behaviors for moving objects (2010, background): “Periodic behaviors are also useful for compressing movement data [12, 17, 4].”