Advancing Big Data: Critical Analysis for Harnessing Heterogeneous Information

This cross-disciplinary project aims to investigate underlying philosophical questions in the acquisition, management and production of scientific big data. Big data analytics (BDA) continues to produce an overwhelming amount of information which can be challenging to make use of due to its size, density and complexity. BDA is ubiquitous in forecasting and decision making but diverse external data sources with varying data structures (graphs, tables, arrays etc.) can prevent normal machine learning tasks from answering our queries appropriately. Such a multi-faceted problem necessitates a creative solution, so in addition to using sophisticated computer science, philosophical and socio-technological methods might also be able to help. Using a diversity of approaches to address problems of scale and heterogeneity in big data can serve to help develop new architectures, algorithms, and techniques to harness the information embedded in the big data landscape, making the philosophy of big data both a technical and social enterprise.

Faculty Supervisor:

John Turri

Student:

Partner:

Inria Sophia Antipolis - Méditerranée Research Centre

Discipline:

Computer science

Sector:

Education

University:

University of Waterloo

Program: