10 citations · 44 across the 14 of their papers we have counts for
16 papers
Investigating Memory Failure Prediction Across CPU Architectures
Qiao Yu, Wengui Zhang, Min Zhou +5
Large-scale datacenters often experience memory failures, where Uncorrectable Errors (UEs) highlight critical malfunction in Dual Inline Memory Modules (DIMMs). Existing approaches…
On Software Ageing Indicators in OpenStack
Yevhen Yazvinskyi, Jasmin Bogatinovski, Jorge Cardoso +1
Distributed systems in general and cloud systems in particular, are susceptible to failures that can lead to substantial economic and data losses, security breaches, and even poten…
FAIRification of MLC data
Ana Kostovska, Jasmin Bogatinovski, Andrej Treven +3
The multi-label classification (MLC) task has increasingly been receiving interest from the machine learning (ML) community, as evidenced by the growing number of papers and method…
First CE Matters: On the Importance of Long Term Properties on Memory Failure Prediction
Jasmin Bogatinovski, Qiao Yu, Jorge Cardoso +1
Dynamic random access memory failures are a threat to the reliability of data centres as they lead to data loss and system crashes. Timely predictions of memory failures allow for…
Leveraging Log Instructions in Log-based Anomaly Detection
Jasmin Bogatinovski, Gjorgji Madjarov, Sasho Nedelkoski +2
Artificial Intelligence for IT Operations (AIOps) describes the process of maintaining and operating large IT systems using diverse AI-enabled methods and tools for, e.g., anomaly…
Failure Identification from Unstable Log Data using Deep Learning
Jasmin Bogatinovski, Sasho Nedelkoski, Li Wu +2
The reliability of cloud platforms is of significant relevance because society increasingly relies on complex software systems running on the cloud. To improve it, cloud providers…
Data-Driven Approach for Log Instruction Quality Assessment
Jasmin Bogatinovski, Sasho Nedelkoski, Alexander Acker +2
In the current IT world, developers write code while system operators run the code mostly as a black box. The connection between both worlds is typically established with log messa…
A2Log: Attentive Augmented Log Anomaly Detection
Thorsten Wittkopp, Alexander Acker, Sasho Nedelkoski +4
Anomaly detection becomes increasingly important for the dependability and serviceability of IT services. As log lines record events during the execution of IT services, they are a…
Explaining the Performance of Multi-label Classification Methods with Data Set Properties
Jasmin Bogatinovski, Ljupčo Todorovski, Sašo Džeroski +1
Meta learning generalizes the empirical experience with different learning tasks and holds promise for providing important empirical insight into the behaviour of machine learning…
Robust and Transferable Anomaly Detection in Log Data using Pre-Trained Language Models
Harold Ott, Jasmin Bogatinovski, Alexander Acker +2
Anomalies or failures in large computer systems, such as the cloud, have an impact on a large number of users that communicate, compute, and store information. Therefore, timely an…
Comprehensive Comparative Study of Multi-Label Classification Methods
Jasmin Bogatinovski, Ljupčo Todorovski, Sašo Džeroski +1
Multi-label classification (MLC) has recently received increasing interest from the machine learning community. Several studies provide reviews of methods and datasets for MLC and…
Artificial Intelligence for IT Operations (AIOPS) Workshop White Paper
Jasmin Bogatinovski, Sasho Nedelkoski, Alexander Acker +5
Artificial Intelligence for IT Operations (AIOps) is an emerging interdisciplinary field arising in the intersection between the research areas of machine learning, big data, strea…
Multi-Source Anomaly Detection in Distributed IT Systems
Jasmin Bogatinovski, Sasho Nedelkoski
The multi-source data generated by distributed systems, provide a holistic description of the system. Harnessing the joint distribution of the different modalities by a learning mo…
Self-Attentive Classification-Based Anomaly Detection in Unstructured Logs
Sasho Nedelkoski, Jasmin Bogatinovski, Alexander Acker +2
The detection of anomalies is essential mining task for the security and reliability in computer systems. Logs are a common and major data source for anomaly detection methods in a…
Superiority of Simplicity: A Lightweight Model for Network Device Workload Prediction
Alexander Acker, Thorsten Wittkopp, Sasho Nedelkoski +2
The rapid growth and distribution of IT systems increases their complexity and aggravates operation and maintenance. To sustain control over large sets of hosts and the connecting…
Self-Supervised Log Parsing
Sasho Nedelkoski, Jasmin Bogatinovski, Alexander Acker +2
Logs are extensively used during the development and maintenance of software systems. They collect runtime events and allow tracking of code execution, which enables a variety of c…