About the Event
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 other 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.
Alexandra Meliou is a postdoctoral research associate with Dan Suciu in the database group of 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.