Exploring Data with Minimal Effort
When: Wednesday, May 2, 2012 - 9:40am - 11:00am
Where: KEC 1007
Speaker Title/Description:
Arash Termehchy
Ph.D. student
Department of Computer Science
University of Illinois at Urbana-Champaign
Speaker Biography:
Arash Termehchy is a Ph.D. student at the Department of Computer Science, University of Illinois at Urbana-Champaign under the supervision of Marianne Winslett. His research interest is in data and information management in a broad sense, including large scale data management, human centric data management, data trustworthiness, social data management, data mining, and semantic Web. He is the recipient of the ICDE'11 best student paper award, the ICDE'11 best papers selection, the Yahoo! Key Scientific Challenges award, and the Feng Chen Memorial award.
Abstract:
Current data search and exploration paradigms fall short of providing usable,
effective, efficient, robust, and economical search and exploration
experiences. For instance, Web search engines do not effectively satisfy
complex information needs. Although database query languages such as SQL are
intended for expressing complex information needs, the languages are too hard
to use, and the underlying databases are too maintenance-intensive, to be
viable bases for an effective Big Data infrastructure. In my research, I have
set forth solid theoretical foundations for a usable, effective, robust, and
economical data search and exploration experience and developed large scale
systems that provide such an experience.
In this talk, I will argue that keyword and natural language query interfaces
should use deep meta-data information to effectively rank the results of
queries. I also postulate that search results from keyword and natural language
query interfaces should not depend on how the underlying data sets are
organized. I show that current query interfaces do not adhere to these
principles. I describe a series of large scale search and exploration systems
that embody and validate these principles through extensive user studies over
real world data.
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