Assessment of the real-time pattern recognition capability of machine learning algorithms

Nowadays data streams from different sources, like blockchain-based and traditional financial transactions, social networks, and interconnected Internet of Things (IoT) devices, are becoming increasingly large in volume and the need to recognize patterns in real time from these streams, while adapting to their velocity and veracity, is emerging. Established machine learning algorithms used for pattern recognition methods have not been designed taking under account the volume, velocity, diversity, and accuracy of the data streams. This research contributes with an approach for assessing the pattern recognition capabilities of established machine learning algorithms when handling volatile data in real time and proposes a system that adapts the algorithms to the requirements of data streams, as well as assesses their pattern recognition capabilities based on established criteria. The system was applied for assessing five machine learning algorithms with input from a data stream from Bluetooth beacons tracking consumers in a retail store. This research can support future data scientists and analysts who need to reveal data patterns in big, volatile data streams in real time in order to support effective decision-making in the respective application domain. Copyright (c) 2024 John Wiley & Sons, Ltd.