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Building Smart Software Systems for Focused Pattern Discovery - Morteza Zihayat

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  • Wed, 05/03/2017 - 11:00am - 12:00pm




Abstract

With rapid advances in the cloud computing and dramatic expansion of data collection systems, nowadays, software systems, such as web-based and various phenomenal systems, have collected vast amounts of data about their processes and users. These data are modern-day treasure stores that can be mined to glean insights into a software system development life cycle (SDLC), business’s products, services and customers. Extracting useful knowledge from such massive data requires smart and scalable analytics systems and programming tools. Despite great efforts that have been made in the past, there is a large gap between academic deliverables and business expectations and thus, many questions still remain to be answered. How can we build smart software systems to discover actionable knowledge from dynamically changing data produced by different platforms? How can we effectively combine human and machine intelligence to gain more useful and effective insights from massive data? How can we guarantee the high performance of systems by taking the power of cloud computing into account?

One major objective in building such systems is to discover patterns that can represent intrinsic and important properties of massive datasets in different domains. Finding patterns has been studied extensively in last two decades. However, most of the techniques fail to incorporate the user preference into the process, and thus, lack the ability to steer systems to more interesting parts of data. In this talk, I will describe how we overcome this limitation by developing user-oriented systems for discovering patterns. In this new problem setting, patterns are then simultaneously extracted according to the user preference. I will talk about the problems, challenges and opportunities for building systems to resolve two pattern analytics problems: 1) high utility pattern discovery in massive sequential data and 2) team formation in a network of experts. I will also talk about my research projects with the industry and how theory can be implemented in real world systems.

Biography

Morteza Zihayat is a Postdoctoral Fellow at Faculty of Information at University of Toronto and Dapasoft Inc. (Microsoft Gold Certified Partner). His research concerns software engineering, Big Data systems for search and mining, parallel and distributed computing, data management in the cloud and health informatics. He won the prestigious Mitacs Elevate postdoctoral fellowship in 2016. Since 2012, he has been involved in designing and implementing several big industrial projects as a solution architect and data scientist in IBM Canada, Dapasoft Inc., and The Globe and Mail Inc. He received his Ph.D. in Computer Science from York University where he did research on designing scalable systems to discover actionable knowledge from big sequential data and social networks. Recently, he has received the outstanding Ph.D. Dissertation award from the Department of Electrical Engineering and Computer Science at York University. His research is published in data mining, machine learning and Big Data premium-tier venues like Machine Learning, Information Sciences, SIAM SDM, ECML/PKDD, EDBT and IEEE Big Data. He holds a B. Sc. and a M. Sc. in IT Engineering from University of Isfahan and University of Tehran, Iran.



Margaret Garabon
garabon@uwindsor.ca
(519)253-3000 ext.3714