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Effective Keyword Search over (Semi)-Structured Big Data

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  • Fri, 02/10/2017 - 11:00am - 12:30pm




Effective Keyword Search over (Semi)-Structured Big Data

Computer Science Colloquium Series
Dr. Mehdi Kargar

Date:  Friday, February 10th, 2017
Time: 11:00 am
Location: Chrysler Hall North, G100

Abstract: Huge amount of data is generated every day. In fact, the digital universe is doubling in size every two years. The effect of big data to improve our capabilities and lives is limited to our ability to use the data. Having a strong system that enables users to access big data is of paramount importance. Much of the world’s high-quality big data are stored as (semi)-structured data. This includes enterprise’s RDBMSs, XML repositories and social networks. These (semi)-structured databases could be modeled as graphs. In order to use graph-like databases, one should be familiar with its structure (i.e., schema) and a query language (e.g., SQL/SPARQL). However, the complexity of query languages and database structures has largely restricted its use to experts and skilled developers. In contrast, a non-technical end-user is effectively locked out. Such an end-user is limited to use pre-defined forms to use the data.

Keyword search over graph-like databases offers an alternative way to access and use (semi)-structured data that neither requires mastery of a query language, nor deep knowledge of the database’s potentially quite complex schema. I will talk about the problems, challenges and opportunities for improving the exploration of graph-like databases. More specifically, my work revolved around building systems to resolve these two problems: keyword search in big graphs and team formation in social networks. I will also briefly talk about my experience with the industry and how theory can be implemented in the real world systems.
 

Bio:
Mehdi Kargar is an Assistant Professor of Computer Science at the University of Windsor. Prior to this, he was a Postdoctoral Fellow at Lassonde School of Engineering at York University and Dapasoft Inc. (Microsoft Gold Certified Partner). His research concerns big data, databases, data mining, data analytics, social network analysis and software engineering. He won the prestigious Mitacs Elevate postdoctoral fellowship in 2014. He received his Ph.D. in Computer Science from York University (2009-2013). He is specifically interested in designing effective methods and algorithms for the problem of keyword search in big graphs, relational database and social networks. His research is published in database and data mining top-tier venues like PVLDB, ACM SIGMOD, IEEE ICDE, IEEE TKDE, EDBT, ACM CIKM and SIAM SDM. He holds a M.Sc. and a B.Sc. in Software Engineering from Sharif University of Technology in Iran and was ranked 7th in Iranian National Scientific Olympiad for university students in Computer Engineering in 2006.

 

 



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