相关论文: Virtual Data in CMS Production

The Smart Grid (SG) is a Cyber-Physical System (CPS) considered a critical infrastructure divided into cyber (software) and physical (hardware) counterparts that complement each other. It is responsible for timely power provision wrapped by…

Data preprocessing pipelines, which includes data decoding, cleaning, and transforming, are a crucial component of Machine Learning (ML) training. Thy are computationally intensive and often become a major bottleneck, due to the increasing…

The quality of the data in a dataset can have a substantial impact on the performance of a machine learning model that is trained and/or evaluated using the dataset. Effective dataset management, including tasks such as data cleanup,…

The data access patterns of applications running in computing grids are changing due to the recent proliferation of high speed local and wide area networks. The data-intensive jobs are no longer strictly required to run at the computing…

Data plays a fundamental role in consolidating markets, services, and products in the digital financial ecosystem. However, the use of real data, especially in the financial context, can lead to privacy risks and access restrictions,…

Graphics Processing Units (GPUs) leverage massive parallelism and large memory bandwidth to support high-performance computing applications, such as multimedia rendering, crypto-mining, deep learning, and natural language processing. These…

This paper describes PlinyCompute, a system for development of high-performance, data-intensive, distributed computing tools and libraries. In the large, PlinyCompute presents the programmer with a very high-level, declarative interface,…

Multi- and many-core processors are becoming increasingly popular in embedded systems. Many of these processors now feature hardware virtualization capabilities, such as the ARM Cortex A15, and x86 processors with Intel VT-x or AMD-V…

The digital twin has emerged as a technology to predict the undesirables, and ensure desired performance of complex systems. Although digital twins have got attention in the manufacturing research spectrum, yet their industrial application…

The Collaborative Analysis Versioning Environment System (CAVES) project concentrates on the interactions between users performing data and/or computing intensive analyses on large data sets, as encountered in many contemporary scientific…

Machine Learning Workflows (MLWfs) have become essential and a disruptive approach in problem-solving over several industries. However, the development process of MLWfs may be complicated, hard to achieve, time-consuming, and error-prone.…

Process mining involves discovering, monitoring, and improving real processes by extracting knowledge from event logs in information systems. Process mining has become an important topic in recent years, as evidenced by a growing number of…

Effective data visualization is a key part of the discovery process in the era of big data. It is the bridge between the quantitative content of the data and human intuition, and thus an essential component of the scientific path from data…

Structured Cartesian grids are a fundamental component in numerical simulations. Although these grids facilitate straightforward discretization schemes, their na\"{i}ve use in sparse domains leads to excessive memory overhead and…

We introduce a high-performance virtual machine (VM) written in a numerically fast language like Fortran or C to evaluate very large expressions. We discuss the general concept of how to perform computations in terms of a VM and present…

In-memory (transactional) data stores are recognized as a first-class data management technology for cloud platforms, thanks to their ability to match the elasticity requirements imposed by the pay-as-you-go cost model. On the other hand,…

In the field of computational science and engineering, workflows often entail the application of various software, for instance, for simulation or pre- and postprocessing. Typically, these components have to be combined in arbitrarily…

Allocating resources in a distributed environment is a fundamental challenge. In this paper, we analyze the scheduling and placement of virtual machines (VMs) in the cloud platform of SAP, the world's largest enterprise resource planning…

To process a large amount of data sequentially and systematically, proper management of workflow components (i.e., modules, data, configurations, associations among ports and links) in a Scientific Workflow Management System (SWfMS) is…

This paper explains the Visual Milestone Planning (VMP) method using an agile vocabulary to facilitate its adoption by agile practitioners as a front end for a hybrid development process. VMP is a visual and collaborative planning approach…