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Speaker: Fethi Rabhi, UNSW

Seminar Date: Tuesday July 12 2016, 12.00pm

Abstract: Most big data analytics research is scattered across multiple domains such as applied statistics, machine learning, language technology or databases. Little attention has been paid to aligning big data solutions with end-user’s mental models for conducting exploratory and predictive data analysis. We are particularly interested in the way domain experts perform real-time data analysis by applying statistical techniques involving time-series variables. In this seminar, we compare and contrast the different views about time-series data between the fields of statistics and computer science. We review popular analysis techniques and tools within a defined time-series analytics stack. We then propose a model-driven architecture that uses semantic and event processing technologies to achieve a separation of concerns between expressing the mathematical model and the computational requirements.  This seminar also describes an ongoing effort towards implementation of a case study in funds management.