Declarative analysis of noisy information networks

—There is a growing interest in methods for analyzing data describing networks of all types, including information, biological, physical, and social networks. Typically the data describing these networks is observational, and thus noisy and te; it is often at the wrong level of fidelity and abstraction for meaningful data analysis. This has resulted in a growing body of work on extracting, cleaning, and annotating network data. Unfortunately, much of this work is ad hoc and domain-specific. In this paper, we present the architecture of a data management system that enables efficient, declarative analysis of large-scale information networks. We identify a set of primitives to support the extraction and inference of a network from observational data, and describe a framework that enables a network analyst to easily implement and combine new extraction and analysis techniques, and efficiently apply them to large observation networks. The key insight behind our approach is to decoupl...

• Type: Journal
• Year: 2011
• Where: ICDE
• Authors: Walaa Eldin Moustafa, Galileo Namata, Amol Deshpande, Lise Getoor
• Keywords: Analysis, Database, ICDE 2011, Meaningful Data Analysis, Network Data