The rapid accumulation of multi-modal data (e.g., text, images, and geo-locations) presents significant opportunities for data mining applications in healthcare and e-commerce. However, effectively retrieving such data remains challenging due to the difficulty in capturing diverse and dynamic user retrieval intents. Existing solutions, such as vector databases, typically rely on static embeddings or fixed weights, failing to adapt to users' varying preferences across different modalities. To address this, we present OneDB, a distributed framework for multi-metric similarity search that integrates data management with learning-based techniques. Unlike traditional systems, OneDB features three key algorithmic innovations: (i) an adaptive metric weight learning model based on lightweight contrastive learning, which infers implicit user preferences from limited query examples; (ii) a dual-layer indexing strategy that combines global partitioning with modality-aware local indexing to handle heterogeneous data distributions efficiently; and (iii) an end-to-end parameter tuning module leveraging deep reinforcement learning to optimize system performance in dynamic environments. Extensive experiments on real-world datasets demonstrate that OneDB captures user intent effectively, achieving 12.63%--30.75% higher accuracy and 2.5--5.75× faster retrieval speeds compared to state-of-the-art vector search systems.
Tang Qian, Yifan Zhu, Lu Chen et al.· Proceedings of the 32nd ACM...· 0 citations
Multivariate time series anomaly detection is critical in safety-critical domains such as industrial monitoring and financial systems. However, real-world time series are inherently non-stationary, with evolving data distributions driven by changing operational regimes and system dynamics. As a result, most existing methods, which assume static data distributions, exhibit severe performance degradation over time and are prone to catastrophic forgetting when incrementally updated. To address these challenges, we propose ReCATS, a replay-free framework for continual anomaly detection in non-stationary multivariate time series. ReCATS combines multi-regime modeling with a dual-phase generative alignment mechanism, enabling effective adaptation to distribution shifts while preserving knowledge acquired from past tasks. Furthermore, we introduce a dynamic dual-scale thresholding strategy to mitigate decision boundary drift under evolving data distributions. Extensive experiments on six real-world benchmark datasets demonstrate that ReCATS consistently outperforms state-of-the-art methods in terms of detection accuracy, knowledge retention, and transferability, as evaluated by standard continual learning metrics, including Backward Transfer and Forward Transfer. The code is available at https://github.com/Li-Qiuyang/ReCATS.
Qiuyang Li, Q. Ma, Zhongming Yao et al.· Proceedings of the 32nd ACM...· 0 citations
Multivariate time series anomaly detection is critical in safety-critical domains such as industrial monitoring and financial systems. However, real-world time series are inherently non-stationary, with evolving data distributions driven by changing operational regimes and system dynamics. As a result, most existing methods, which assume static data distributions, exhibit severe performance degradation over time and are prone to catastrophic forgetting when incrementally updated. To address these challenges, we propose ReCATS, a replay-free framework for continual anomaly detection in non-stationary multivariate time series. ReCATS combines multi-regime modeling with a dual-phase generative alignment mechanism, enabling effective adaptation to distribution shifts while preserving knowledge acquired from past tasks. Furthermore, we introduce a dynamic dual-scale thresholding strategy to mitigate decision boundary drift under evolving data distributions. Extensive experiments on six real-world benchmark datasets demonstrate that ReCATS consistently outperforms state-of-the-art methods in terms of detection accuracy, knowledge retention, and transferability, as evaluated by standard continual learning metrics, including Backward Transfer and Forward Transfer. The code is available at https://github.com/Li-Qiuyang/ReCATS.
Qiuyang Li, Q. Ma, Zhongming Yao et al.· Proceedings of the 32nd ACM...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
