BigData Analysis and Data Mining

Biography

Biography: Fionn Murtagh

Abstract

The benefits and also the challenges of Big Data analytics can be addressed in innovative ways.  It is known that analytical focus is important.  Considering just as an analogy for our analytics, how a microscope or a telescope bring about observation and measurement at very fine scales and at very gross scales, we can take that analogy as being associated with the resolution scale of our analysis.  Another challenge is the bias in Big Data. But we may calibrate our analytical process with a Big Data framework or infrastructure.  A further challenge of an ethical nature, is how respresentativity replaces the individual.  So we want "to rehabilitate the individual".  Important opportunities arise from contextualization. That can be associated with the resolution scale of our analytics, and it can also be supported by full account taken of appropriate contexts.  The innovation that stems from the different facets of our analytical procedures can be of great benefit.  Here we seek to discuss many such themes that are always in the context of interesting and important case studies.  The main case studies for us here include the following: analytics of mental health and associated well-being; social media analytics based on Twitter; questionnaire and survey analytics with many respondents. Ultimately what is sought is not just scalability alone, but also new and insightful, revealing and rewarding, perspectives, returns and benefits.  A book of ours, to be published in April 2017: Data Science Foundations: Geometry and Topology of Complex Hierarchic Systems and Big Data Analytics.