Identifying patterns or clusters in streaming time-series data is crucial for decision-making, and underpins applications such as anomaly detection, forecasting, and data quality monitoring. While numerous clustering algorithms have been proposed, many remain unexplored in the time-series domain, and others are unsuitable for streaming scenarios. Moreover, many effective methods require prior knowledge of the number of clusters, a significant limitation when dealing with evolving data streams. To address these challenges, we propose BURST, a principled and general-purpose framework that enables the application of partition-based clustering methods in streaming time-series settings. At its core, BURST integrates AutoKC, a novel, adaptive algorithm for automatically estimating the number of clusters, enhancing robustness to evolving time-series streams. Experimental analyses show that BURST is a robust strategy for real-time time-series clustering, effectively generalizing across different partitioning methods, and achieving state-of-the-art performance compared to existing algorithms.

This study introduces a scalable approach for link prediction in Wikipedia pages, specifically designed to handle the challenges arising from the large volume of data. The proposed solution combines partial reconstruction of the original graph using node descriptions, the generation of node pair vectors based on graph metrics, and the application of a threshold similarity using the TF-IDF method. Our proposed solution achieve a high F1 score of 0.948.

Abstract We deal with the problem of predictive maintenance (PdM) in a vehicle fleet management setting following an unsupervised streaming anomaly detection approach. We investigate a variety of unsupervised methods for anomaly detection, such as proximity-based, hybrid (statistical and proximity-based) and transformers. The proposed methods can properly model the context in which each member of the fleet operates. In our case, the context is both crucial for effective anomaly detection and volatile, which calls for streaming solutions that take into account only the recent values. We propose two novel techniques, a 2-stage proximity-based one and context-aware transformers along with advanced thresholding. In addition, to allow for testing PdM techniques for vehicle fleets in a fair and reproducible manner, we build a new fleet-like benchmarking dataset based on an existing dataset of turbofan simulations. Our evaluation results show that our proposals reduce the maintenance costs compared to existing solutions.