BDCAA 2019: The 1st IEEE International Workshop on Big Data Computation, Analysis, and Applications

BDCAA 2019 Program

BDCAA 1: The 1st IEEE International Workshop on Big Data Computation, Analysis, and Applications
Friday July 19, 3:00 – 4:15
Location: AMU 157
Session Chair: Shameem Ahmed, Western Washington University, USA

Open Science, Business Analytics, and FAIR Digital Objects
George Strawn

Job-aware Optimization of File Placement in Hadoop
Makoto Nakagami, Jose A. B. Fortes and Saneyasu Yamaguchi

An Emperical Study on Application of Big Data Analytics to Automate Service Desk Business Process
Dan Lo, Karl Kevin Tiba, Sergiu Buciumas, and Frank Ziller

Call for Papers

The workshop aims to solicit cooperation and discussions among researchers specializing in interdisciplinary and complimentary domains that leverage applications that generate and analyze big data on High Performance Computing (HPC) resources.

Workshop theme: Challenge and opportunities in Interdisciplinary Data-Driven Projects

Technological, medical, scientific, and societal advancements are realized via applications that are driven by the expertise of the researcher at the intersection of multiple domains. Disciplines that in the past did not collaborate extensively are more likely to work on joint projects that require the synthesis of ideas and domain-specific skills. Many facets of interdisciplinary projects that aim to deliver novel solutions to address critical problems bring about challenges and opportunities. This workshop will solicit manuscripts for presentation and discussion of the computation and analysis challenges of interdisciplinary projects drawing from the expertise of a variety of disciplines.

Scope of the workshop:

This workshop welcomes any research addressing the big question of “How do applications in various domains leverage HPC to generate and analyze big data for solving societal problems?” Researchers are invited to submit and ultimately present state of the art solutions requiring the analysis or generation of big data using HPC resources, for decision making. Topics of interest include, but are not limited to, the following:

• Big data, data-driven medical diagnosis and analysis
• Real-time decision making for compute in the edge
• Use of large-scale distributed system such as supercomputers, grids, or clouds
• Data dimensionality reduction techniques for large scale analysis of biological data
• Visual data analytics for large-scale and/or high-dimensional data
• Big data, smart sampling and analysis techniques for medical decision making
• Large-scale mobile sensor data for just-in-time health care intervention