arXiv:2607. 29383v1 Announce Type: new Abstract: In recent years, with the development of big data technology, increasingly more companies use HDFS for data processing and storage.
arXiv:2606. 04957v1 Announce Type: cross Abstract: System-generated logs underpin security monitoring, yet their rigid template-based format hinders both automated analysis and human comprehension.
arXiv:2608.29973v1 Announce Type: new Abstract: Cryptocurrency markets generate high-frequency, multi-source data that is expensive to work with unless a team already has commercial-grade streaming a...
arXiv:2610.01168v1 Announce Type: cross Abstract: Time Series Anomaly Detection has received increasing attention, driven by the growing availability of complex time series data. This surge has led t...
arXiv:2609.39215v1 Announce Type: cross Abstract: Time series anomaly detection (TSAD) is increasingly deployed in streaming settings, where data arrive sequentially and may exhibit non-stationarity....
arXiv:2605. 22779v2 Announce Type: replace-cross Abstract: Production systems generate millions of log lines daily, yet most anomaly detectors operate at the session or window-level, flagging groups of lines rather than identifying the specific message responsible.
The paper explores a hybrid approach that combines traditional Journal Entry Tests (JETs) with machine learning techniques to enhance anomaly detection in general ledger data. It presents specialized models designed to improve the accuracy and validity of detected anomalies, thereby aiming to reduce false positives and increase audit efficiency. Experiments are conducted using synthetic data that includes both normal and anomalous journal entries.
arXiv:2509. 06419v2 Announce Type: replace Abstract: Time-series anomaly detection is crucial in AIOps for maintaining large-scale service reliability.
The paper proposes a new framework for automated fault diagnostics in modern vehicles by treating diagnostic trouble codes (DTCs) as a high‑dimensional language. It introduces Transformer‑based models for predictive maintenance, scalable causal discovery methods, and a multi‑agent system that automatically generates Boolean error‑pattern rules. The approach aims to replace costly manual grouping of DTCs with scalable, data‑driven techniques.
arXiv:2511. 22078v2 Announce Type: replace Abstract: Many real-world scenarios involving streaming information can be represented as temporal graphs, where data flows through dynamic changes in edges over time.
arXiv:2609.13940v1 Announce Type: new Abstract: Effective anomaly detection frameworks are a central pillar of the Industry 4.0 paradigm. In this paper, we introduce an Optimal Transport (OT)-based f...
arXiv:2308. 04214v2 Announce Type: replace-cross Abstract: In the domain of knowledge representation and reasoning within AI, datalog engines play an ever-increasingly crucial role.
arXiv:2606. 28923v1 Announce Type: new Abstract: Detecting security threats in an organization's cloud computing environment has become necessary due to the increased reliance on cloud infrastructure.