In-Database Machine Learning for Big Data Entity Resolution 

The data being collected by organizations is increasing relentlessly but it still can give a misleading or fragmented view of the real world. A person, for example, could appear multiple times or have multiple digital entities within the same database, due to typos, name changes, aggregation of different systems and so on. If we try to merge two databases, how do we match entities, when the ID systems might be different or contain errors? 

Entity resolution (ER) helps get to the truth. Entity resolution, which is the disambiguation of real-world entities in a database, is an essential data quality tool.

Graph provides an efficient approach for the entity resolution problem. A native graph database with massive parallel computing capability is the best tool to implement the approach.

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About TigerGraph

TigerGraph is the only scalable graph database for the enterprise. Based on the industry’s first Native and Parallel Graph technology, TigerGraph unleashes the power of interconnected data, offering organizations deeper insights and better outcomes. TigerGraph fulfills the true promise and benefits of the graph platform by tackling the toughest data challenges in real time, no matter how large or complex the dataset. TigerGraph’s proven technology supports applications such as fraud detection, customer 360, MDM, IoT, AI and machine learning to make sense of ever-changing big data, and is used by customers including Amgen, China Mobile, Intuit, Wish and Zillow, along with some of the world’s largest healthcare, entertainment and financial institutions. 

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TigerGraph
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Redwood City, CA 94065

sales@tigergraph.com
www.tigergraph.com 

TigerGraph is the only system today that can help us make real-time care-path recommendations using knowledge of 50 million patients. Your products will have worldwide impact on making everyone’s lives better in more ways than you can imagine.

 

Distinguished Engineer

Fortune 10 Healthcare Company