Peopleconnect, Inc.

Hakan Kardes

Xin Wang

Siddharth Agrawal

Ang Sun

Lin Chen

Deepak Konidena

Micah Huff

Andrew Kellberg

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.

Graph-based organization entity resolution

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 &#x2dc;6B nodes and &#x2dc;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.

Fast and scalable connected component computation

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Bellevue, WA, United States of America

Hakan Kardes