Predictive Maintenance in dynamic environments poses significant challenges, including data and concept drifts, lack of labeled data, and the constraints of streaming environments. To address these, our approach leverages unsupervised anomaly detection techniques to monitor
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SIESTA is an application-agnostic, open-source tool designed to build incremental indices from continuously streaming event data. These indices enable efficient analysis of the data, supporting tasks such as detecting complex patterns, predicting future events, and
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HARVEST has been developed in collaboration with the American Farm School of Thessaloniki. Its goal is to harvest the knowledge that can be generated by the advanced processing of farm sensing data through applying cutting-edge
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The Twitter Monitoring App has been launched by members of the Data and Web Science Lab (DATALAB), the research group of the Department of Informatics, Aristotle University of Thessaloniki, Greece, being specialized in ICT research
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Political polarization refers to the divergence of political opinions away from the center / neutral opinions towards ideological extremes. Political discussions can frequently become polarized, a phenomenon becoming more intense in social media discussions, such
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