Abstract: A novel entity resolution approach for the organization entity domain can be implemented in the MapReduce framework with low memory requirements so that it may scale to large scale datasets. A new clustering approach, sClust, significantly improves the recall of the pairwise classifier.

Abstract: Finding connected components in a graph is a well-known problem in a wide variety of application areas such as social network analysis, data mining, image processing, and etc. We present an efficient and scalable approach to find all the connected components in a given graph. We compare our approach with the state-of-the-art on a real-world graph. We also demonstrate the viability of our approach on a massive graph with ˜6B nodes and ˜92B edges on an 80-node Hadoop cluster. To the best of our knowledge, this is the largest graph publicly used in such an experiment.