INFORMATION TECHNOLOGY FOR TRAJECTORY DATA MINING

Authors

DOI:

https://doi.org/10.15407/scine17.03.078

Keywords:

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

Abstract

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.

Downloads

Download data is not yet available.

References

Atluri, G., Karpatne, A., Kumar, V. (2018). Spatio-Temporal Data Mining: A Survey of Problems and Methods. ACM Computing Surveys, 51(4), 83:1—83:41. doi: 10.1145/3161602

Andrienko, N., Andrienko, G. (2006). Exploratory Analysis of Spatial and Temporal Data: A Systematic Approach. Springer. Berlin. 703 p.

Venkateswara Rao, K., Govardhan, A., Chalapati Rao, K. V. (2012). Spatiotemporal data mining: issues, tasks and applications. International Journal of Computer Science & Engineering Survey (IJCSES), 3(1), 39—52. doi: 10.5121/ijcses.2012.3104

Mazimpaka, J. D., Timpf, S. (2016). Trajectory data mining: A review of methods and applications. Journal of spatial information science, 13(1), 61—99. doi: 10.5311/JOSIS.2016.13.263

Tanuja, V., Govindarajulu, P. (2016). A Survey on Trajectory Data Mining. International Journal of Computer Science and Security (IJCSS), 10(5), 195—214.

Zheng, Y. (2015). Trajectory Data Mining: An Overview. ACM Transactions on Intelligent Systems and Technology, 6(3), 29:1—29:41. doi: http://dx.doi.org/10.1145/2743025

Suzuki, J., Suhara, Y., Toda, H., Nishida, K. (2019). Personalized Visited-POI Assignment to Individual Raw GPS Trajectories. ACM Transactions on Spatial Algorithms and Systems, 5(3), 16:1—16:31. doi.org/10.1145/3317667

Huang, J., Liu, Y., Chen, Y., Jia, Ch. (2019). Dynamic Recommendation of POI Sequence Responding to Historical Trajectory. International Journal of Geo-Information, 8(10), 433:1—433:15. doi: 10.3390/ijgi8100433

Gonçalves, T., Afonso, A. P., Martins, B. (2015). Cartographic visualization of human trajectory data: overview and analysis. Journal of Location Based Services, 9(2), 138—166. doi: 10.1080/17489725.2015.1074736

Cai, L., Zhou, Y., Liang, Y., He, J. (2018). Research and Application of GPS Trajectory Data Visualization. Annals of Data Science, 5(1), 43—57. doi: 10.1007/s40745-017-0132-1

Sidorova, M., Pidhornyi, P. (2018, February). Software for spatio-temporal trajectory analysis and pattern mining. Proceedings of 14th International Conference on Advanced Trends in Radioelecrtronics, Telecommunications and Computer Engineering (TCSET). Slavske, 958—961. doi: 10.1109/TCSET.2018.8336352

Sidorova, M. (2012, February).Information technology of evaluation and improvement the quality of cluster analysis. Proceedings of International Conference on Modern Problem of Radio Engineering, Telecommunications and Computer Science. Lviv-Slavske, 390.

Baibuz, O. G., Sidorova, M. G. (2014). Information technology of the multivariate time series fuzzy clustering on the example of the samara river hydrochemical monitoring. Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu, 2014(5), 11—18 [in Ukrainian].

Geolife GPS trajectory dataset — User Guide. URL: https://www.microsoft.com/en-us/research/publication/geolifegps-trajectory-dataset-user-guide/ (Last accessed: 24.03.2020).

African Elephant Database. URL: https://www.iucn.org/ssc-groups/mammals/african-elephant-specialist-group/african-elephant-database (Last accessed: 24.03.2020).

TLC Trip Record Data. URL: https://www1.nyc.gov/site/tlc/about/tlc-trip-record-data.page (Last accessed: 24.03.2020).

Downloads

Published

2021-06-17

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. https://doi.org/10.15407/scine17.03.078

Issue

Section

The Scientific Basis of Innovation