MAT-Index: An index for fast multiple aspect trajectory similarity measuring

The semantic enrichment of mobility data with several information sources has led to a
new type of movement data, the so-called multiple aspect trajectories. Comparing multiple
aspect trajectories is crucial for several analysis tasks like querying, clustering, similarity, classification, etc. Multiple aspect trajectory similarity measuring is more complex
and computationally expensive, because of the large number and heterogeneous aspects of
space, time, and semantics that require a different treatment. Only a few works in the
literature focus on optimizing all these dimensions in a single solution, and, to the best
of our knowledge, none of them propose a fast point-to-point comparison. In this paper
we propose the Multiple Aspect Trajectory Index (MAT-Index), an index data structure
for optimizing the point-to-point comparison of multiple aspect trajectories, considering
its three basic dimensions of space, time, and semantics. Quantitative and qualitative
evaluations show a processing time reduction up to 98.1%