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Objectives. The aim of the study is to search for effective methods and approaches to the processing of heterogeneous data streams and the management of problems of infinite length, conceptual evolution and conceptual drift. A heterogeneous data stream can have infinite length and contain structured or unstructured data. Processing a heterogeneous and multi-scale data flow is a major challenge for researchers. Most of the research focuses on solving problems of infinite length and concept-drift.Method. New class detection strategies are classified as parametric and non-parametric. This work is based on a non-parametric approach. The classifier works on the ensemble of three models. The separation generates a different number of classes in each fragment. Classes are calculated by applying the K-Medoid clustering method on each fragment. The effectiveness of the K-media clustering method is more suitable for a data set containing anomalies.Result. The developed algorithm is capable of processing heterogeneous and multi-scale data. Each instance that is present in the model belongs to only one class. Experimental work was performed on four samples of stream data of 2000 lines each. After performing the pre-processing, the multi-valued characteristics of the data were found in the data set.Conclusion. This paper presents an effective approach for processing heterogeneous data streams and managing tasks of infinite length, conceptual evolution and conceptual drift. The developed approach is based on the string matching parameter instead of the distance for processing the four tasks of data streams. The level of false positives in the developed algorithm is rather low and can be considered insignificant. The approach does not classify a new instance of the class as an existing class, but can effectively handle the functional evolution.

Currently, there is an increase in information for data mining in transport systems, the main reason is the increase in the number of heterogeneous sources. The relevance of the topic lies in the need to collect, process, aggregate, and model large volumes of unstructured information that cannot be effectively processed by traditional methods. With the increasing flow of vehicles, its diversity, there is a need to optimize the processes of transportation and logistics, increase the system safety of road traffic. The creation of an information knowledge base will help to solve a number of important problems, including: the efficiency of road use, reduction of toxic emissions, control and unloading of traffic flows, reduction in the number of accidents, and prompt notification of services.The idea of developing a unified centralized traffic control system is described. To collect, store and process heterogeneous information, it is proposed to use a cloud infrastructure with split computation. For the purpose of high-quality processing and aggregation of heterogeneous information, it is recommended to investigate hidden dependencies in the data, build and analyze various aggregation options and interpret them in relation to specific tasks.The system should connect all participants in ground traffic, collect dissimilar materials that can be obtained from their devices and a variety of sensors, and also automate the management and decision-making in transport systems. Unstructured information must be correctly interpreted, categorized, and consistently labeled to identify implicit relationships between data.The scientific novelty of the research consists in the formation of the functions of the system being developed, the description of the main aspects, requirements, interfaces, models and methods for aggregating heterogeneous data.The results of the work can be used not only for analyzing big data in the field of transport, but also in other directions when solving problems of processing heterogeneous information.

Objective. The paper proposes a fire detection algorithm for a multisensor system. Due to the difficult conditions in the field, for the first time, watch rescue operations are difficult and often endanger the lives of rescuers. The main scientific goal is for the system to work autonomously on certain segments of the monitoring process, while the three agents, to varying degrees, must interact with each other based on communication and decision-making algorithms.Method. There are many algorithms for image processing, but algorithms that use several sources of information are not sufficiently developed and described. The focus is on fire detection algorithms. The algorithm was developed using NI Vision Assistant, a software tool for rapid prototyping and testing of imaging applications.Result. In addition to the software implementation in C, which NI Vision Assistant generates by default, the paper presents a Python implementation of the algorithm.Conclusion. The results of the work can be used to develop multisensor systems for monitoring hard-to-reach areas.