Practices and Techniques for Prototyping Big Data Applications

Big data analytics has emerged, in the past few years, as a subject of great fascination and intrigue. It has become a vital factor in the decision making process for leaders of various sectors, from government bodies to corporate executives to scientists and researchers. It has gained considerable attention recently due to the exponential growth of data generation by individuals and corporations alike. Numerous research groups around the world are attempting to envision innovative and efficient methods to manage, analyze and visualize big data. Their efforts are generally aimed at the development level or are specific to the use case in hand. However, the early system design procedure, such as low and high-fidelity prototyping, of big data applications has not been studied. In this project, I intend to apply agile methodologies to analyze current techniques and practices and, possibly, to define innovative methods for prototyping big data applications.

Faculty Supervisor:

Frank Maurer

Student:

Partner:

Universidade Federal de São Paulo

Discipline:

Computer science

Sector:

University:

University of Calgary

Program:

Globalink Research Award