Alexandra Meliou

Alexandra Meliou


Assistant Professor

School of Computer Science

140 Governors Drive

University of Massachusetts

Amherst, MA 01003-9264 USA


Email:
Office: 330
Phone: +1-413-545-3788
Fax: +1-413-545-1249

Research

My research interests are in the area of data and information management, with an emphasis on provenance, causality, and reverse data management. I am currently working on the multiple challenges that emerge at the intersection of database systems and business intelligence applications. One major goal of my research is to extend the capabilities of modern database systems to support business decisions and strategy planning queries, which commonly involve optimization problems over large data.

Prospective Students
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Current Projects

Causality Fusion of Probabilistic Data
Cleaning the data extracted from the web is not straightforward: extracted data is typically uncertain, and different extraction systems often provide duplicate or conflicting results. In this project, we study the problem of integrating data from multiple extraction systems, targeting three main challenges: (a) limited knowledge of the inner workings of each extraction method, (b) uncertainty in the source data and the extraction results, and (c) unknown correlations between extraction methods.
Collaboration: AT&T Labs Research, Google Research

Tiresias The Tiresias System
The goal of this project is to seamlessly integrate databases with constrained problem solving in a fully-fledged system. We are building a system that allows the user to specify an optimization problem over their data declaratively. The system then translates the declarative input into a mixed integer program that is sent to a dedicated solver.

Causality Causality in Databases
When queries return unexpected results, users require explanations for their observations. In this project we explore what constitutes a cause for a query answer, or non-answer, and augment databases with support for causal queries. We demonstrate how causality can be used to provide explanations, as well as identify and correct data errors in a process called post-factum data cleaning.

RDM Reverse Data Management
Reverse Data Management encompasses an array of problems in database research where an action needs to be performed on the input data, on behalf of desired outcomes in the output data. Some examples include updates through views, data generation, data cleaning and repair. Today, as increasingly more of the available data is derived from other data, there is an increased need to be able to modify the input in order to achieve a desired effect on the output, motivating a systematic study of RDM.