Efficient discovery of emerging patternsin heterogeneous spatiotemporal data from mobile sensors
Francisco Neves, Anna Carolina Finamore, Rui Henriques · 2020
Heterogeneous sensor networks, including traffic monitoring systems and telemetry systems, produce massive spatiotemporal data. Geolocated time series data and timestamped trajectory data are generally produced from fixed and mobile sensors in these systems, offering the possibility to detect events of interest. Events of interest generally comprise emerging and gradual changes in the behavior of those systems, including patterns of congestion in road, utility and communication networks. However, the comprehensive discovery of these actionable events is challenged by the: i) inherently spatiotemporal and heterogeneous nature of data produced by different sensors; ii) difficulty of detecting emerging patterns not yet markedly noticeable at early stages; and iii) massive data size.