Modeling and Analytics for Big Data Applications

It has always been challenging to manage data at large-scale as there are no standards and best practices currently available for modeling and analysis of data. Currently available solutions rely on limited information and are confined to manual processing which makes the process of data handling and management slow, inefficient and hard. This makes modeling and analysis crucial to be able to track, monitor and manage applications that are complex, critical in nature and produce intensive data. In this research, the applicant will develop techniques, algorithms and tools for effective modeling and analysis of Big Data applications. These outcomes will help in automating the process of handling and management of large-scale data and make best use of formalism of data with analytics for the purposes of fault detection and prediction. Impact and usefulness of the proposed research will be demonstrated by applying it on real-life application from the industrial partner.

Faculty Supervisor:

Hans-Arno Jacobsen

Student:

Omair Shafiq

Partner:

Digital Science Canada

Discipline:

Engineering - computer / electrical

Sector:

Information and communications technologies

University:

University of Toronto