WSEAS Transactions on Information Science and Applications
Print ISSN: 1790-0832, E-ISSN: 2224-3402
Volume 22, 2025
Stream Data Analysis and Processing Frameworks for Detecting Outliers in Human Activities: A Review
Authors: ,
Abstract: Every day, we generate enormous amounts of data from a wide range of personal devices. The rapid increase in data amount and velocity is pushing our limits to process and analyze them. Traditional machine learning and data analytics methods and algorithms use all historical data in the dataset to build their analyses and models. This may lead to processing and analyzing large amounts of historical data being computationally expensive and time-consuming, especially in real-time applications where speed is crucial. Furthermore, using all historical data may not account for changes in the models and dynamics underlying the data over time. This could lead to inaccurate forecasts or insights. Streaming analytics, on the other hand, processes each point of continuous data as it is received. It is more efficient than batch processing in certain cases. Real-time data processing using stream analytics allows organizations to make immediate and proactive decisions based on up-to-date information. This can be especially beneficial in time-sensitive industries, such as finance or logistics, where even a slight delay in data analysis can result in missed opportunities or costly errors. Additionally, stream analytics enables businesses to detect and respond to anomalies in real time, leading to enhanced operational efficiency and customer experiences. Statistically significant outliers are instances that don’t follow the general trend of the data. Datasets may contain outliers for several reasons, such as mistakes made during data collection or the presence of extremely high or low values. Because of the potential impact of outliers on analysis, it is worthwhile to carefully consider whether or not they should be included. This is useful for spotting inconsistencies or discrepancies, as well as determining which parts of the data need more in-depth analysis. This study discusses topics related to stream data analysis. These topics include a variety of frameworks for processing and analyzing streaming data, methods for detecting outliers, and human activity detection.
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Pages: 104-117
DOI: 10.37394/23209.2025.22.11