Using Elasticsearch to Help Generate New Insights from Census Data

The Census Bureau has large amounts of rich and complex data sets that are retrieved and used each day by the public. This data reveals insights into our economy, demographic characteristics of states, and helps communities make infrastructural decisions such as where and when to plan public transportation systems and the location of new housing. Given the criticality of Census data, a team at the Bureau has built a prototype that leverages Elasticsearch to make data more accessible and relevant to users. This talk explores our key challenges, successes, and how we used Elasticsearch to build the prototype

Jesus Jackson

Jesus Jackson is a Chief Data Scientist with Booz Allen Hamilton. His expertise is in large-scale data platforms, distributed search, and advanced analytics. In addition to his technical work, Jesus is a senior leader in Booz Allen’s data science practice and leads the data science business for the federal financial services sector

Daewoo Chong

Daewoo Chong is a software engineer/data scientist at Booz Allen Hamilton. His current role as a search engineer at the US Census has him exploring ways to combine the powerful relevancy models of Elastic with machine learning. His interests include rapid prototyping, information retrieval, natural language processing, and deep learning.