相关论文: How Can Subgroup Discovery Help AIOps?

The modern industrial environment is equipping myriads of smart manufacturing machines where the state of each device can be monitored continuously. Such monitoring can help identify possible future failures and develop a cost-effective…

Intrusion detection is a long standing and crucial problem in security. A system capable of detecting intrusions automatically is on great demand in enterprise security solutions. Existing solutions rely heavily on hand-crafted rules…

In this work, we provide an industry research view for approaching the design, deployment, and operation of trustworthy Artificial Intelligence (AI) inference systems. Such systems provide customers with timely, informed, and customized…

AIOps algorithms play a crucial role in the maintenance of microservice systems. Many previous benchmarks' performance leaderboard provides valuable guidance for selecting appropriate algorithms. However, existing AIOps benchmarks mainly…

Analyzing sequence data usually leads to the discovery of interesting patterns and then anomaly detection. In recent years, numerous frameworks and methods have been proposed to discover interesting patterns in sequence data as well as…

We outline a comprehensive framework for artificial intelligence (AI) Application Operations (AIAppOps), based on real-world experiences from diverse organizations. Data-driven projects pose additional challenges to organizations due to…

Subgroup selection in clinical trials is essential for identifying patient groups that react differently to a treatment, thereby enabling personalised medicine. In particular, subgroup selection can identify patient groups that respond…

As Artificial Intelligence (AI) technologies continue to gain traction in the modern-day world, they ultimately pose an immediate threat to current cybersecurity systems via exploitative methods. Prompt engineering is a relatively new field…

Industrial monitoring systems, especially when deployed in Industry 4.0 environments, are experiencing a shift in paradigm from traditional rule-based architectures to data-driven approaches leveraging machine learning and artificial…

The ability to detect and adapt to changes in data distributions is crucial to maintain the accuracy and reliability of machine learning models. Detection is generally approached by observing the drift of model performance from a global…

The increasing connectivity of data and cyber-physical systems has resulted in a growing number of cyber-attacks. Real-time detection of such attacks, through the identification of anomalous activity, is required so that mitigation and…

Streaming data analysis is increasingly required in applications, e.g., IoT, cybersecurity, robotics, mechatronics or cyber-physical systems. Despite its relevance, it is still an emerging field with open challenges. SDO is a recent anomaly…

Information technology (IT) systems are vital for modern businesses, handling data storage, communication, and process automation. Monitoring these systems is crucial for their proper functioning and efficiency, as it allows collecting…

Despite the increasing development of Artificial Intelligence (AI) systems, Requirements Engineering (RE) activities face challenges in this new data-intensive paradigm. We identified a lack of support for problem discovery within AI…

An Intrusion Detection System (IDS) is a software that monitors a single or a network of computers for malicious activities (attacks) that are aimed at stealing or censoring information or corrupting network protocols. Most techniques used…

Machine Learning algorithms are good tools for both classification and prediction purposes. These algorithms can further be used for scientific discoveries from the enormous data being collected in our era. We present ways of discovering…

Data extracted from software repositories is used intensively in Software Engineering research, for example, to predict defects in source code. In our research in this area, with data from open source projects as well as an industrial…

Tipping points occur in many real-world systems, at which the system shifts suddenly from one state to another. The ability to predict the occurrence of tipping points from time series data remains an outstanding challenge and a major…

The efficient and effective monitoring of mobile networks is vital given the number of users who rely on such networks and the importance of those networks. The purpose of this paper is to present a monitoring scheme for mobile networks…

Cloud computing is ubiquitous: more and more companies are moving the workloads into the Cloud. However, this rise in popularity challenges Cloud service providers, as they need to monitor the quality of their ever-growing offerings…