Do LLMs Understand Visual Anomalies? Uncovering LLM's Capabilities in Zero-shot Anomaly Detection
Large vision-language models (LVLMs) are markedly proficient in deriving visual
representations guided by natural language. Recent explorations have utilized LVLMs to …
representations guided by natural language. Recent explorations have utilized LVLMs to …
NeurDB: an AI-powered autonomous data system
In the wake of rapid advancements in artificial intelligence (AI), we stand on the brink of a
transformative leap in data systems. The imminent fusion of AI and DB (AI× DB) promises a …
transformative leap in data systems. The imminent fusion of AI and DB (AI× DB) promises a …
Database native model selection: Harnessing deep neural networks in database systems
The growing demand for advanced analytics beyond statistical aggregation calls for
database systems that support effective model selection of deep neural networks (DNNs) …
database systems that support effective model selection of deep neural networks (DNNs) …
Impact of log parsing on deep learning-based anomaly detection
Software systems log massive amounts of data, recording important runtime information.
Such logs are used, for example, for log-based anomaly detection, which aims to …
Such logs are used, for example, for log-based anomaly detection, which aims to …
Impact of log parsing on log-based anomaly detection
Software systems log massive amounts of data, recording important runtime information.
Such logs are used, for example, for log-based anomaly detection, which aims to …
Such logs are used, for example, for log-based anomaly detection, which aims to …
Powering in-database dynamic model slicing for structured data analytics
Relational database management systems (RDBMS) are widely used for the storage and
retrieval of structured data. To derive insights beyond statistical aggregation, we typically …
retrieval of structured data. To derive insights beyond statistical aggregation, we typically …
Pluto: Sample Selection for Robust Anomaly Detection on Polluted Log Data
Log anomaly detection, critical in identifying system failures and preempting security
breaches, finds irregular patterns within large volumes of log data. Modern log anomaly …
breaches, finds irregular patterns within large volumes of log data. Modern log anomaly …
Contrastive Learning for Fraud Detection from Noisy Labels
Detecting frauds in computing platforms involves identifying malicious user activity sessions.
Recently, deep learning models have been employed to design fraud detection approaches …
Recently, deep learning models have been employed to design fraud detection approaches …
PreLog: A Pre-trained Model for Log Analytics
Large-scale software-intensive systems often produce a large volume of logs to record
runtime status and events for troubleshooting purposes. The rich information in log data …
runtime status and events for troubleshooting purposes. The rich information in log data …
LBSC: A Cost-Aware Caching Framework for Cloud Databases
Caching is a crucial solution to alleviate the high latency and low bandwidth of cloud
databases. However, existing caching algorithms are not suitable for cloud databases as 1) …
databases. However, existing caching algorithms are not suitable for cloud databases as 1) …