相关论文: Data processing model for the CDF experiment
Background: Metabolomics datasets are becoming increasingly large and complex, with multiple types of algorithms and workflows needed to process and analyse the data. A cloud infrastructure with portable software tools can provide much…
The simulation of high-energy physics collision events is a key element for data analysis at present and future particle accelerators. The comparison of simulation predictions to data allows looking for rare deviations that can be due to…
Increased adoption and deployment of phasor measurement units (PMU) has provided valuable fine-grained data over the grid. Analysis over these data can provide insight into the health of the grid, thereby improving control over operations.…
With the explosive growth of big data, workloads tend to get more complex and computationally demanding. Such applications are processed on distributed interconnected resources that are becoming larger in scale and computational capacity.…
Scientific applications are complex, large, and often exhibit irregular and stochastic behavior. The use of efficient loop scheduling techniques in computationally-intensive applications is crucial for improving their performance on…
The BaBar experiment at SLAC is in its fourth year of running. The data processing system has been continuously evolving to meet the challenges of higher luminosity running and the increasing bulk of data to re-process each year. To meet…
In this article we describe the migration of event data collected by the COMPASS and HARP experiments at CERN. Together these experiments have over 300TB of physics data stored in Objectivity/DB that had to be transferred to a new data…
The Data Science domain has expanded monumentally in both research and industry communities during the past decade, predominantly owing to the Big Data revolution. Artificial Intelligence (AI) and Machine Learning (ML) are bringing more…
Supercomputers are complex systems producing vast quantities of performance data from multiple sources and of varying types. Performance data from each of the thousands of nodes in a supercomputer tracks multiple forms of storage, memory,…
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…
Computer-based assessments routinely generate detailed interaction logs -- commonly referred to as process data -- that record every action a respondent performs during task completion, yet systematic preprocessing guidance, integrated…
As multimodal and AI-driven services exchange hundreds of megabytes per request, existing IPC runtimes spend a growing share of CPU cycles on memory copies. Although both hardware and software mechanisms are exploring memory offloading,…
The Cognitive Data Model (CDM) is proposed. A novel approach to database design, inspired by the belief that the human brain operates with a logical data model independent of its anatomical structure. The study aims to identify and…
The design of microfluidic devices is a cumbersome and tedious process that can be significantly improved by simulation. Methods based on Computational Fluid Dynamics (CFD) are considered state-of-the-art, but require extensive compute time…
With today's increasing demand for digital devices in Substation Automation Systems (SAS) based on the IEC61850 standard, the measured data error due to the synchronization problem should be considered as a significant problem in…
Hospitals around the world collect massive amounts of physiological data from their patients every day. Recently, there has been an increase in research interest to subject this data to statistical analysis to gain more insights and provide…
Distributed stream processing systems are widely deployed to process real-time data generated by various devices, such as sensors and software systems. A key challenge in the system is overloading, which leads to an unstable system status…
In 2002 the ATLAS experiment started a series of Data Challenges (DC) of which the goals are the validation of the Computing Model, of the complete software suite, of the data model, and to ensure the correctness of the technical choices to…
The data science community today has embraced the concept of Dataframes as the de facto standard for data representation and manipulation. Ease of use, massive operator coverage, and popularization of R and Python languages have heavily…
Data-flow testing (DFT) aims to detect potential data interaction anomalies by focusing on the points at which variables receive values and the points at which these values are used. Such test objectives are referred as \emph{def-use…