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SUMMARY:Brown bag: Immanuel Trummer
DESCRIPTION:Title: Database Group Research Overview\nSpeaker: Immanuel
	 Trummer\nAbstract: In this talk\, I will give a broad overview of recent
	 and ongoing work in my group:\n\nData Vocalization: most prior research
	 on how to optimally present data to users focuses on data visualization.
	 The communication between user and computer is however more and more
	 shifting towards voice-based interfaces\, evidenced by devices and
	 services such as Google Home\, Amazon Echo\, or Apple's Siri. This
	 motivates research on \"data vocalization\"\, i.e. how to optimally
	 transmit data via voice output. I will describe results from a recent
	 publication\, in which we introduce the problem field of data
	 vocalization\, as well as several ongoing projects.\n\nQuery
	 Optimization: the goal of query optimization is to translate declarative
	 queries into optimal executable query plans. Query optimization is an
	 NP-hard optimization problem which makes it difficult to find optimal
	 plans for large queries. I will give an overview of our recent results
	 on leveraging integer programming solvers to solve query optimization
	 instances within seconds where traditional optimizers would need weeks
	 of optimization time. I will also describe ongoing work on leveraging
	 reinforcement learning to replace traditional cost models in query
	 optimization.\n\nFact Checking: relational data sets are often published
	 together with text articles\, summarizing key statistics. The majority
	 of the population never accesses raw data but relies on summaries alone.
	 This raises the question of how we can trust such summaries to be
	 accurate. I will describe our ongoing work on the \"FactChecker\"\, a
	 novel tool\, similar in spirit to a spell checker\, that supports
	 authors in creating accurate data summaries. The FactChecker translates
	 textual claims into SQL queries and displays\, via markup\, whether
	 evaluation results match the values claimed in text. Using the current
	 version\, we were already able to identify erroneous claims in articles
	 from several major newspapers.
LOCATION:Gates 122
UID:2017-11-07
STATUS:CONFIRMED
DTSTART:20171107T170000Z
DTEND:20171107T180000Z
LAST-MODIFIED:20171106T150904Z
ORGANIZER;CN=Jonathan Shi:http://www.cs.cornell.edu/~jshi/brownbag/
DTSTAMP:20260408T172641Z
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