Multiscale Frequent Co-movement Pattern Mining

Helmi, Shahab; Banaei-Kashani, Farnoush

doi:10.1109/icde48307.2020.00077

Cited by 3 publications

(1 citation statement)

References 23 publications

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“…Informally, a co-movement pattern can be defined as a group of objects moving together for at least some time duration. While there exists a plethora of works in this area, such as [3,[5][6][7]15], we choose the algorithm proposed in [13], where the authors propose a novel co-movement pattern definition, called evolving clusters, that unifies the definitions of flocks and convoys and reduces them to Maximal Cliques (MC), and Maximal Connected Subgraphs (MCS), respectively; see the difference between the two in Figure 2. For the purposes of our demonstration, we choose to run EvolvingClusters in two modes:…”

Section: Co-movement Patternsmentioning

confidence: 99%

MaSEC: Discovering Anchorages and Co-movement Patterns on Streaming Vessel Trajectories

Tritsarolis

Kontoulis

Pelekis

et al. 2021

17th International Symposium on Spatial and Temporal Databases

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The massive-scale data generation of positioning (tracking) messages, collected by various surveillance means, has posed new challenges in the field of mobility data analytics in terms of extracting valuable knowledge out of this data. One of these challenges is online cluster analysis, where the goal is to unveil hidden patterns of collective behaviour from streaming trajectories, such as comovement and co-stationary (aka anchorage) patterns. Towards this direction, in this paper, we demonstrate MaSEC (Moving and Stationary Evolving Clusters), a system that discovers valuable behavioural patterns as above. In particular, our system provides a unified solution that discovers both moving and stationary evolving clusters on streaming vessel position data in an online mode. The functionality of our system is evaluated over two real-world datasets from the maritime domain. CCS CONCEPTS• Information systems → Data mining; Spatial-temporal systems.

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Section: Co-movement Patternsmentioning

confidence: 99%

MaSEC: Discovering Anchorages and Co-movement Patterns on Streaming Vessel Trajectories

Tritsarolis

Kontoulis

Pelekis

et al. 2021

17th International Symposium on Spatial and Temporal Databases

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Predicting Co-movement patterns in mobility data

et al. 2022

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Predictive analytics over mobility data is of great importance since it can assist an analyst to predict events, such as collisions, encounters, traffic jams, etc. A typical example is anticipated location prediction, where the goal is to predict the future location of a moving object, given a look-ahead time. What is even more challenging is to be able to accurately predict collective behavioural patterns of movement, such as co-movement patterns as well as their course over time. In this paper, we address the problem of Online Prediction of Co-movement Patterns. Furthermore, in order to be able to calculate the accuracy of our solution, we propose a co-movement pattern similarity measure, which facilitates the comparison between the predicted clusters and the actual ones. Finally, we calculate the clusters’ evolution through time (survive, split, etc.) and compare the cluster evolution predicted by our framework with the actual one. Our experimental study uses two real-world mobility datasets from the maritime and urban domain, respectively, and demonstrates the effectiveness of the proposed framework.

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