GRDB: a system for declarative and interactive analysis of noisy information networks

Authors:
Walaa Eldin Moustafa;Hui Miao;Amol Deshpande;Lise Getoor
Affiliations:
University of Maryland, College Park, MD, USA;University of Maryland, College Park, MD, USA;University of Maryland, College Park, MD, USA;University of Maryland, College Park, MD, USA
Venue:
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
Year:
2013

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Abstract

There is a growing interest in methods for analyzing data describing networks of all types, including biological, physical, social, and scientific collaboration networks. Typically the data describing these networks is observational, and thus noisy and incomplete; it is often at the wrong level of fidelity and abstraction for meaningful data analysis. This demonstration presents GrDB, a system that enables data analysts to write declarative programs to specify and combine different network data cleaning tasks, visualize the output, and engage in the process of decision review and correction if necessary. The declarative interface of GrDB makes it very easy to quickly write analysis tasks and execute them over data, while the visual component facilitates debugging the program and performing fine grained corrections.