Abstract
In recent years, there has been observed an “explosion” of trajectory data production due to the proliferation of GPS-enabled devices, such as mobile phones and tablets. This massive-scale data generation has posed new challenges in the data management community in terms of storing, querying, analyzing, and extracting knowledge out of such data. Knowledge discovery out of mobility data is essentially the goal of every mobility data analytics task. Especially in the maritime and aviation domains, this relates to challenging use-case scenarios, such as discovering valuable behavioral patterns of moving objects, identifying different types of activities in a region of interest, environmental fingerprint, etc. In order to be able to support such scenarios, an analyst should be able to apply, at massive scale, several knowledge discovery techniques, such as trajectory clustering, hotspot analysis, and frequent route/network discovery methods.
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Tampakis, P., Sideridis, S., Nikitopoulos, P., Pelekis, N., Doulkeridis, C., Theodoridis, Y. (2020). Offline Trajectory Analytics. In: Vouros, G., et al. Big Data Analytics for Time-Critical Mobility Forecasting. Springer, Cham. https://doi.org/10.1007/978-3-030-45164-6_10
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