Programa

Keynote Talk

Big Data Analytics: Are we doing the right thing and Are we doing it right?

 This talk will discuss general concepts of Big Data Analytics including critical questions about this emerging technology. What are realistic and unrealistic expectations of Big Data? Are there any remaining challenges? The talk will motivate questions whether state-of-the-art techniques and research trends are approaching Big Data Analytics in the right way.

Speaker:  Dr. Rattikorn Hewett

Short Bio. Rattikorn Hewett received a Ph.D. in Computer Science from Iowa State University (1986). Her M. Eng. Sc. in Computer Science from the University of New South Wales (Australia) (1979), and a B.A. (Hons) in Pure Mathematics and Statistics from Flinders University (Australia) (1977) were sponsored by full scholarships from the Australian government. Hewett was a postdoctoral fellow at Stanford University (1987-1990), and a recipient of the National Science Foundation Research Initiation Award (1993-1997). She was a research scientist at the Institute for Human and Machine Cognition and a faculty member at Washington State University Vancouver, and Florida Atlantic University, where she received recognition as an Exceptional Professor. She joined the department of Computer Science at Texas Tech University (TTU) in 2004, where she is presently a full professor. Dr. Hewett has established a research program on Capability Engineering at TTU to apply and enhance Artificial Intelligence (AI) techniques for emerging problems in software automation and security in large software intensive systems.

Invited Talks


From Data Mining/Machine Learning to Big Data Analytics

Speaker:  Dr. Rattikorn Hewett

This talk investigates the problem of how we can design Big Data analytic algorithms that aim to maximally exploit distributed and parallel infrastructures to gain efficiency and effectiveness.  The talk will look at some existing approaches that transform a well-known data-mining algorithm into its corresponding Big Data analytic algorithm. It will show experimental results and identify issues of these approaches. The talk uncovers a fundamental property that can hinder the optimal exploitability of current Big Data frameworks.