Keywords: information technology, pattern mining, trajectories of motion, points and sequences of interest, cluster analysis, similarity measure.


Introduction. Advanced technologies allow almost continuous tracking and recording the movement of objects in
space and time. Detecting interesting patterns in these data, popular routes, habits, and anomalies in object motion and understanding mobility behaviors are actual tasks in different application areas such as marketing, urban planning, transportation, biology, ecology, etc.
Problem Statement. In order to obtain useful information from trajectories of moving objects, it is important to develop and to improve mathematical methods of spatiotemporal analysis and to implement them in highquality modern software.
Purpose. The purpose of this research is the development of information technology for trajectory data mining.
Materials and Methods. Information technology contains the three main algorithms: revealing key points
and sequences of interest with the use of density-based trajectories clustering of studied objects; detecting patterns of an object movement based on association rules and hierarchical cluster analysis of its motion trajectories in the time interval of observations, similarity measure of the motion trajectories has been proposed to be calculated on the basis of the DTW method with the use of the modified Haversine formula; new algorithm for revealing permanent routes and detecting groups of similar objects has been developed on the basis of clustering ensembles
of all studied trajectories in time. The clustering parameters are selected with multi-criteria quality evaluation.
Results. The modern software that implements the proposed algorithms and provides a convenient interaction
with users and a variety of visualization tools has been created. The developed algorithms and software have been
tested in detail on the artificial trajectories of moving objects and applied to analysis of real open databases.
Conclusions. The experiments have confirmed the efficiency of the proposed information technology that
may have a practicable application to trajectory data mining in various fields.


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How to Cite

Sydorova , M. ., Baybuz, O., Verba, O., & Pidhornyi, P. (2021). INFORMATION TECHNOLOGY FOR TRAJECTORY DATA MINING. Science and Innovation, 17(3), 78–86.



The Scientific Basis of Innovation