Approaches to processing big spatiotemporal uncertain data in GLONASS+112

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.

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