"Mining Healthcare Big Data - Concepts, Techniques and Practice"

Abstract: Big Data is different from the so called big data set in that it is a collection of data so large, so complex, so distributed, and growing so fast (or so called 5Vs). Big Data are not usable until they can be aggregated and integrated into a manner that computer can process to generate knowledge. Extracting useful knowledge from Big Data can be considered as a processing pipeline that involves multiple distinct configuration stages to achieve full utilization. Each stage faces several specific challenges. The objective of this presentation is to discuss the opportunities of mining health Big Data to improve healthcare as well as the challenges and solutions for health Big Data Analytics (BDA) – the process of extracting knowledge from sets of Health Big Data.

 

Short bio: Dr. Alex Kuo holds a PhD from the Department of Computer Science, University of Nottingham, UK. He is a Professor at the School of Health Information Science, University of Victoria, BC, Canada. He was a visiting scholar at the Electronic Commerce Resource Centre (ECRC), Georgia Tech. (1999-2000), and the Center for Expanded Data Annotation and Retrieval (CEDAR), School of Medicine, Stanford University (2015-2016). Now, he is the chair of the IEEE Big Data Education Track and the study group leader of Metadata Standard for Big Data Management at the IEEE Big Data Initiative (BDI).