Traditionally, data has been processed in tables or lists. Almost every tool used today (such as R, Excel, or Databases) is based on these kinds of data structures. Many practical domains, however, are more suitably modeled as graphs. Social Networks, monetary transactions, citation of papers, or even disease spreading is oftentimes better modeled as a graph, where the vertices represent entities and the edges relationships or interactions.
Large graphs, however, can be tedious to process. The Web of Data, for example, has grown one dataset in 2007 to a graph with over 31 billion edges (in 2011) usually shown in the diagrams and a plethora of open data sets published by individuals, organizations and governments all over the world usually not counted. Given this immense growth the question arises how to process these kinds of data. Even if you can process 10'000 edges per second it will still take more than 861 hours to process the whole Web of Data, so conventional approaches are going to be slow.
In this talk I will introduce the distributed graph-processing framework Signal/Collect, which allows to process billions of edges in seconds. I will highlight the usefulness of the framework in 4 application scenarios — Graph Databases, Fraud Detection, Constraint Optimization, and Probabilistic Reasoning.
Abraham is a Full Professor at the Department of Informatics at the University of Zurich, Switzerland. His current research focuses on various aspects of data on the web, knowledge discovery, and large-scale coordination such as crowdsourcing. His work draws on both social science (organizational psychology/sociology/economics) and technical (computer science, artificial intelligence) foundations.
Before coming to Zurich he was an Assistant Professor, at the Information Systems Department in New York University's Stern School of Business, and received a Ph.D. at MIT's Sloan School of Management.
School of IT, Lecture Theatre (Room 123)
School of IT Building
The University of Sydney
School of IT Reception
T: +61 2 9351 3423