When: Monday, March 19, 2012 - 11:45am - 1:00pm
Where: KEC 1005
Speaker Information
Speaker Name: Alexandra Meliou
Speaker Title/Description:
Postdoctoral Research Associate
University of Washington
Speaker Biography:
Alexandra Meliou is a postdoctoral research associate with Dan Suciu in the database group of the University of Washington. She received her Ph.D degree in 2009 from the University of California, Berkeley, and is a 2008 Siebel Scholar. Her interests are in data and information management with a focus on issues of data provenance. Currently, she is working on extending the capabilities of database systems to support business decisions and strategy planning queries.
Abstract:
Current trends have seen data grow larger, more intertwined, and more diverse,
as more and more users contribute to and use it. This trend has given rise to
the need to support richer data analysis tasks. Such tasks involve determining
the causes of observations, finding and correcting the sources of error in
query results, as well as modifying the data in order to make it conform to
complex desirable properties.
In this talk I will discuss three challenges: (a) providing explanations through support for causal
queries ("Why"), (b) tracing and correcting errors at their source (post-factum data
cleaning), and (c) integrating database systems with constrained optimization capabilities
("How"). First, I will show how to apply causal reasoning to tuple provenance in order to
determine the causes of query results, and their responsibility. I will present extensive analysis
of the data complexity for the case of conjunctive queries, and focus on a complete dichotomy
between NP-hard and PTIME cases for the problem of computing responsibility. This concrete
characterization of PTIME cases is crucial in scaling up to the challenges of Big Data. Second, I
will demonstrate the applicability of the causality framework in a practical setting. I will use a
mobile sensing application to show that ranking provenance tuples by their degrees of
responsibility identifies errors more effectively than o!
ther schemes. Finally, I will present the Tiresias system, the first how-to
query engine, which seamlessly integrates database systems with constrained
problem solving capabilities. The contributions of the system are threefold:
(a) a declarative interface for defining how-to queries over a database, (b)
translation rules from the declarative statements to the constrained problem
specification, and (c) a suite of data-specific optimizations that allow
scaling to large data sizes. Initial results of our prototype system
implementation show order-of-magnitude speedups to state-of-the-art solver
runtimes, which indicates that there are significant gains in pushing this
functionality within the database engine.
I will conclude with a summary of my contributions, and discuss my future steps
with the Tiresias system, and the bigger vision of reverse data management.
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