Proceedings of the 2nd international workshop on Petascal data analytics: challenges and opportunities - PDAC '11

2011 ◽  
Keyword(s):  
Data Analytics ◽  
International Workshop ◽  
Challenges And Opportunities

scholarly journals Challenges and Opportunities in Multimodal Monitoring and Data Analytics in Traumatic Brain Injury

2021 ◽  
Vol 21 (3) ◽  
Author(s):  
Brandon Foreman ◽  
India A Lissak ◽  
Neha Kamireddi ◽  
Dick Moberg ◽  
Eric S Rosenthal
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Data Analytics ◽  
Multimodal Monitoring ◽  
Challenges And Opportunities

Developing and Assessing Data Analytics Courses: A Continuous Proposal for Responding to AACSB Standard A5

2019 ◽  
Vol 17 (1) ◽  
pp. 133-141
Author(s):  
Gregory P. Tapis ◽  
Kanu Priya
Keyword(s):  
Data Analytics ◽  
Holistic Approach ◽  
Course Assessment ◽  
Challenges And Opportunities ◽  
Approach To Teaching ◽  
Psychology Literature

ABSTRACT Data analytics is receiving increasing emphasis in accounting programs. This emphasis has emerged from both practitioners and accrediting bodies. In April 2018, the Association to Advance Collegiate Schools of Business (AACSB) International released Standard A5, which calls for a more holistic approach to teaching and incorporating data analytics into accounting programs. Specifically, accounting programs are required to focus on students' agility and adaptability as they relate to changes and disruptions in technology. Such characteristics present challenges and opportunities for accounting educators when developing and assessing data analytics in accounting programs. In this paper, we propose using a combination of practitioner involvement and measurements from the psychology literature to create a continuous holistic approach to course assessment and improvement. Specifically, utilizing proxies for adaptability and agility, we propose a methodology for measuring changes in students' agility and adaptability throughout a semester.

The Future of High-Performance Computing (HPC)

2018 ◽  
pp. 4004-4017
Author(s):  
Herbert Cornelius
Keyword(s):  
High Performance Computing ◽  
Data Analytics ◽  
High Performance ◽  
Economic Value ◽  
Big Data Analytics ◽  
Science Research ◽  
Ease Of Use ◽  
Challenges And Opportunities ◽  
Development Engineering ◽  
Performance Computing

For decades, HPC has established itself as an essential tool for discoveries, innovations and new insights in science, research and development, engineering and business across a wide range of application areas in academia and industry. Today High-Performance Computing is also well recognized to be of strategic and economic value – HPC matters and is transforming industries. This article will discuss new emerging technologies that are being developed for all areas of HPC: compute/processing, memory and storage, interconnect fabric, I/O and software to address the ongoing challenges in HPC such as balanced architecture, energy efficient high-performance, density, reliability, sustainability, and last but not least ease-of-use. Of specific interest are the challenges and opportunities for the next frontier in HPC envisioned around the 2020 timeframe: ExaFlops computing. We will also outline the new and emerging area of High Performance Data Analytics, Big Data Analytics using HPC, and discuss the emerging new delivery mechanism for HPC - HPC in the Cloud.

scholarly journals Fog Computing: An Overview of Big IoT Data Analytics

2018 ◽  
Vol 2018 ◽  
pp. 1-22 ◽  
Author(s):  
Muhammad Rizwan Anawar ◽  
Shangguang Wang ◽  
Muhammad Azam Zia ◽  
Ahmer Khan Jadoon ◽  
Umair Akram ◽  
...  
Keyword(s):  
Data Analytics ◽  
Smart Cities ◽  
Fog Computing ◽  
Computing Paradigm ◽  
Key Characteristics ◽  
Challenges And Opportunities ◽  
Cloud Framework ◽  
Iot Devices ◽  
Health Care Monitoring ◽  
Computing Structure

A huge amount of data, generated by Internet of Things (IoT), is growing up exponentially based on nonstop operational states. Those IoT devices are generating an avalanche of information that is disruptive for predictable data processing and analytics functionality, which is perfectly handled by the cloud before explosion growth of IoT. Fog computing structure confronts those disruptions, with powerful complement functionality of cloud framework, based on deployment of micro clouds (fog nodes) at proximity edge of data sources. Particularly big IoT data analytics by fog computing structure is on emerging phase and requires extensive research to produce more proficient knowledge and smart decisions. This survey summarizes the fog challenges and opportunities in the context of big IoT data analytics on fog networking. In addition, it emphasizes that the key characteristics in some proposed research works make the fog computing a suitable platform for new proliferating IoT devices, services, and applications. Most significant fog applications (e.g., health care monitoring, smart cities, connected vehicles, and smart grid) will be discussed here to create a well-organized green computing paradigm to support the next generation of IoT applications.

Overview of Big Data and Its Visualization

2022 ◽  
pp. 22-53
Author(s):  
Richard S. Segall ◽  
Gao Niu
Keyword(s):  
Big Data ◽  
Traffic Safety ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Real Data ◽  
Flow Diagram ◽  
Data Sets ◽  
United States Department ◽  
Big Data Visualization ◽  
Challenges And Opportunities

Big Data is data sets that are so voluminous and complex that traditional data processing application software are inadequate to deal with them. This chapter discusses what Big Data is and its characteristics, and how this information revolution of Big Data is transforming our lives and the new technology and methodologies that have been developed to process data of these huge dimensionalities. This chapter discusses the components of the Big Data stack interface, categories of Big Data analytics software and platforms, descriptions of the top 20 Big Data analytics software. Big Data visualization techniques are discussed with real data from fatality analysis reporting system (FARS) managed by National Highway Traffic Safety Administration (NHTSA) of the United States Department of Transportation. Big Data web-based visualization software are discussed that are both JavaScript-based and user-interface-based. This chapter also discusses the challenges and opportunities of using Big Data and presents a flow diagram of the 30 chapters within this handbook.

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