Congratulations to A. Tsiakalos for receiving the 2024 Outstanding Teaching Award from the School of Informatics at Aristotle University of Thessaloniki! This prestigious honor, presented during the 2024 School of Sciences Excellence Awards ceremony for Aristotle University of Thessaloniki, recognizes his exceptional dedication to teaching.
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 asset states, eliminating the dependency on labeled datasets. Auto-configurable solutions ensure robustness against data drifts and enhance usability for end-users. Additionally, context-aware methods are employed to provide explainability and improve decision-making processes. All solutions are designed for online deployment, enabling real-time analytics and swift responses.
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 uncovering constraints that describe both the sequence order and temporal aspects of the events.
SIESTA – A scalable infrastructure for sequential event analysis Read More »
Stream DaQ is an open-source framework developed by members of the Data and Web Science Lab (DATALAB) with a strong expertise in data streaming, anomaly detection and time series analytics. Stream DaQ is developed to keep an eye on your data stream, letting you know the moment when travelling data do not meet the expected quality in real time, so that you can take timely, informed actions. Acknowledging that every data-centric application is different, Stream DaQ comes with a comprehensive built-in suite of 60+ state-of-the-art data quality measures, so that
