Related papers: DQSOps: Data Quality Scoring Operations Framework …
Data Quality Monitoring (DQM) is a crucial component of particle physics experiments and ensures that the recorded data is of the highest quality, and suitable for subsequent physics analysis. Due to the extreme environmental conditions,…
Distributed Stream Processing (DSP) systems enable processing large streams of continuous data to produce results in near to real time. They are an essential part of many data-intensive applications and analytics platforms. The rate at…
Artificial Intelligence for IT Operations (AIOps) is an emerging interdisciplinary field arising in the intersection between the research areas of machine learning, big data, streaming analytics, and the management of IT operations. AIOps,…
In materials science and manufacturing, vast amounts of heterogeneous data (e.g., measurement and simulation logs, process data, publications) serve as the bedrock of valuable knowledge for various engineering applications. However,…
Deep learning has made remarkable progress recently, largely due to the availability of large, well-labeled datasets. However, the training on such datasets elevates costs and computational demands. To address this, various techniques like…
Workflow technology is rapidly evolving and, rather than being limited to modeling the control flow in business processes, is becoming a key mechanism to perform advanced data management, such as big data analytics. This survey focuses on…
In a data stream management system (DSMS), users register continuous queries, and receive result updates as data arrive and expire. We focus on applications with real-time constraints, in which the user must receive each result update…
Data quality is a key element for building and optimizing good learning models. Despite many attempts to characterize data quality, there is still a need for rigorous formalization and an efficient measure of the quality from available…
Data warehousing is continuously gaining importance as organizations are realizing the benefits of decision oriented data bases. However, the stumbling block to this rapid development is data quality issues at various stages of data…
Operating cloud service infrastructures requires high energy efficiency while ensuring a satisfactory service level. Motivated by data centers, we consider a workload routing and server speed control policy applicable to the system…
Artificial Intelligence for IT operations (AIOps) aims to combine the power of AI with the big data generated by IT Operations processes, particularly in cloud infrastructures, to provide actionable insights with the primary goal of…
A significant portion of the effort involved in advanced process control, process analytics, and machine learning involves acquiring and preparing data. Literature often emphasizes increasingly complex modelling techniques with incremental…
Artificial intelligence for IT Operations (AIOps) plays a critical role in operating and managing cloud-native systems and microservice-based applications but is limited by the lack of high-quality datasets with diverse scenarios. Realistic…
DevOps and Artificial Intelligence (AI) are interconnected with each other. DevOps is a business-driven approach to providing quickly delivered quality software, and AI is the technology that can be used in the system to enhance its…
Large code models (LCMs), pre-trained on vast code corpora, have demonstrated remarkable performance across a wide array of code-related tasks. Supervised fine-tuning (SFT) plays a vital role in aligning these models with specific…
Digital business transformation has become increasingly important for organizations. Since transforming business digitally is an ongoing process, it requires an integrated and disciplined approach. Data Operations (DataOps), emerging in…
Machine Learning Operations (MLOps) is becoming a highly crucial part of businesses looking to capitalize on the benefits of AI and ML models. This research presents a detailed review of MLOps, its benefits, difficulties, evolutions, and…
Machine Learning (ML) DevOps, also known as MLOps, has emerged as a critical framework for efficiently operationalizing ML models in various industries. This study investigates the adoption trends, implementation efforts, and benefits of ML…
Artificial intelligence has transformed numerous industries, from healthcare to finance, enhancing decision-making through automated systems. However, the reliability of these systems is mainly dependent on the quality of the underlying…
Machine learning has been proven to be effective in various application areas, such as object and speech recognition on mobile systems. Since a critical key to machine learning success is the availability of large training data, many…