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Seminar by Dr Michael L. Brodie

Topic

Efficacy, Efficiency, and Reuse of Big Data

Dr Michael L. Brodie.

Date & Venue 

Date: 10 September 2014,

Time: 12:15pm - 13:15pm

Venue: Univeristy of New South Wales UNSW@ Australian Defence Force Academy (ADFA)

Abstract

Ideally, the Holy Grail of data-intensive discovery in big data is to identify meaningful correlations. However, big data can be used only to identify correlations that suggest What phenomena may have occurred. Correlations do not imply causality – Why a phenomenon occurs. Causality requires empirical verification.

The Holy Grail of data-intensive discovery in big data is identifying correlations that are descriptively and predictively accurate since greater accuracy suggests a greater potential that empirical verification will establish corresponding causal, i.e., meaningful, relationships. The stronger the evidence supporting a correlation in a data set suggest greater potential of empirically verifying that the correlation is casual, i.e., meaningful. Strength of supporting evidence for a correlation is a measure of descriptive accuracy that a correlation is accurate in a given data set. Reliability, a stronger measure of accuracy, that descriptive accuracy of a correlation in one data set implies similar accuracy in other data sets. This talk introduces Data Science concepts, tools, and techniques to improve data-intensive discovery in identifying correlations with estimates of accuracy made symbiotically using human and machine intelligence. The ability to estimate the accuracy of correlations contributes to identifying weak, spurious, and strong correlations thus improves the efficacy and efficiency of data-intensive discovery. The metadata required for accuracy estimation contributes to assessing data for reuse. A far more ambitious goal is for improved efficacy and efficiency of data-intensive discovery (What) to symbiotically accelerate Scientific Discovery (Why).

Bio

Dr. Brodie has over 40 years’ experience in research and industrial practice in databases, distributed systems, integration, artificial intelligence, and multi-disciplinary problem solving. He is concerned with the Big Picture aspects of information ecosystems including business, economic, social, application, and technical. Dr. Brodie is a Research Scientist, MIT Computer Science and Artificial Intelligence Laboratory; advises startups; serves on Advisory Boards of national and international research organizations; and is an adjunct professor at the National University of Ireland, Galway. For over 20 years he served as Chief Scientist of IT, Verizon, a Fortune 20 company, responsible for advanced technologies, architectures, and methodologies for Information Technology strategies and for guiding industrial scale deployments of emergent technologies, most recently Cloud Computing and Big Data and start up Tamr.com. He has served on several National Academy of Science committees. Dr. Brodie holds a PhD in Databases from the University of Toronto and a Doctor of Science (honoris causa) from the National University of Ireland.

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