Подходы к обработке больших пространственно-временных данных ГЛОНАСС+112 в условиях неопределенности
Работая с нашим сайтом, вы даете свое согласие на использование файлов cookie. Это необходимо для нормального функционирования сайта, показа целевой рекламы и анализа трафика. Статистика использования сайта отправляется в «Яндекс» и «Google»
Научный журнал Моделирование, оптимизация и информационные технологииThe scientific journal Modeling, Optimization and Information Technology
Online media
issn 2310-6018

Approaches to processing big spatiotemporal uncertain data in GLONASS+112

idAnikin I.V. Petrov G.E.  

UDC 004.8
DOI: 10.26102/2310-6018/2023.40.1.026

  • Abstract
  • List of references
  • About authors

The paper examines some approaches to processing big spatiotemporal uncertain data in GLONASS+112. This system is used for managing interaction between operational services in the Republic of Tatarstan and collecting and processing data characterizing various incidents, based on calls received by a common emergency number "112". The performance and scalability of several basic operations for managing big data (query with threshold, JOIN, k-nearest neighbors algorithm) were studied; they were adapted for operating data under spatial and temporal uncertainty. New approaches to clustering and associative rules mining for uncertain data are suggested. Modernization of ST-DBSCAN algorithm for clustering spatiotemporal data is proposed. This algorithm is integrated into the association rules mining process. The program complex for forming the associative rules for spatiotemporal data under uncertainty has been developed. The complex is applied to analyze GLONASS+112 data as well as the information about weather conditions obtained from external sources. The associative rules being formed can be used by various units in operating services for decision-making and resource-planning. This would help to increase the efficiency of managing the emergencies and undesired incidents.

1. Dagaeva M., Garaeva A., Anikin I., Makhmutova A., Minnikhanov R. Big spatio-temporal data mining for emergency management information systems. IET Intelligent Transport Systems. 2019;13(11):1649–1657.

2. Anikin I.V., Minnikhanov R.N., Dagaeva M.V., Makhmutova A.Z., Chokoev A.N. The framework for associative rules mining in GLONASS+112 with using external source data. Proceedings of Kazan Digital Week. 2021:34–41. (In Russ.).

3. Minnikhanov R.N., Dagaeva M.V., Anikin I.V., Sabitov A.A., Garaeva A.R. Application of data mining techniques in information systems in Republic of Tatarstan. Vestnik NCBJD = Proceedings of RCSO. 2021;2(48):159–167. (In Russ.).

4. Aggarwal C.C. Data mining. The textbook. Springer Cham; 2014. 661 p.

5. Zheleznov A.N., Anikin I.V., Dagaeva M.V. Basic operations of analyzis uncertain spatio-temporal data and their application to data processing in GLONASS+112. Proceedings of V International conference «Modern problems of life safety: intelligent transport systems and situation centers». 2018;2:240–245.

6. Thai M.T., Wu W., Xiong H. Big Data in Complex and Social Networks. Chapman & Hall. 2020:252.

7. Kromer P. Jurney R. Big Data for Chimps: A Guide to Massive-Scale Data Processing in Practice. O'Reilly Media. 2015:220.

8. Parsian M. Data Algorithms. Recipes for scaling up with Hadoop and Spark. O'Reilly Media, Inc. 2015:686.

9. Manocha S., Girolami M.A. An empirical analysis of the probabilistic K-nearest neighbour classifier. Pattern Recognition Letters.2007;28(13):1818–1824.

10. Cheng R., Chen L., Chen J. Evaluating Probability Threshold k-Nearest-Neighbor Queries over Uncertain Data. Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology. 2009:672–683.

11. Lim H.-S., Lee J.-G., Lee M.-J., Whang K.-Y., Song I.-Y. Continuous query processing in data streams using duality of data and queries. Proceedings of the ACM SIGMOD International Conference on Management of Data. 2006:313–324.

12. Papadias D., Theodoridis Y. Spatial relations, minimum bounding rectangles, and spatial data structures. International Journal of Geographical Information Science. 1997;11(2):111–138.

13. Brisaboa N.R., Luaces M.R., Navarro G., Seco D. Range queries over a compact representation of minimum bounding rectangles. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2010;6413:33–42.

14. Agrawal R., Imieliński T., Swami A. Mining association rules between sets of items in large databases. Proceedings of the 1993 ACM SIGMOD international conference on Management of data – SIGMOD '93. 1993.

15. Kuok C.M., Fu A., Wong M.H. Mining Fuzzy Association Rules in Databases. SIGMOD Record (ACM Special Interest Group on Management of Data). 1998;27(1):41–46.

16. Seda Unal Calargun, Adnan Yazici. Fuzzy association rule mining from spatio-temporal data. Proceedings of Computational Science and Its Applications – ICCSA 2008 – International Conference. 2008

17. Kanani Sadat Y., Nikaein T., Kar F. Fuzzy spatial association rule mining to analyze the effect of environmental variables on the risk of allergic asthma prevalence. Geodesy and cartography. 2015;41(2):101–112.

18. Zadeh L.A. Fuzzy Sets. Information and Control. 1965;8:338–363.

19. Birant D. ST-DBSCAN: An algorithm for clustering spatial-temporal data. Data & Knowledge Engineering. 2007;60(1):208–221.

20. Frequent Pattern Mining. Ed. Charu Aggarwal and Jiawei Han. Springer. 2014.

Anikin Igor Vyacheslavovich
Doctor of Technical Sciences Professor
Email: anikinigor777@mail.ru

WoS | Scopus | ORCID | eLibrary |

Kazan National Research Technical University named after A.N. Tupolev-KAI

Kazan, Russian Federation

Petrov Gleb Evgenievich

Kazan National Research Technical University named after A.N. Tupolev-KAI

Kazan, Russian Federation

Keywords: data mining, spatiotemporal data, uncertainty, clustering, associative rules, emergency management

For citation: Anikin I.V. Petrov G.E. Approaches to processing big spatiotemporal uncertain data in GLONASS+112. Modeling, Optimization and Information Technology. 2023;11(1). Available from: https://moitvivt.ru/ru/journal/pdf?id=1307 DOI: 10.26102/2310-6018/2023.40.1.026 (In Russ).

195

Full text in PDF

Received 17.01.2023

Revised 21.02.2023

Accepted 16.03.2023

Published 17.03.2023