MADden: query-driven statistical text analytics

Authors:
Christan Earl Grant;Joir-dan Gumbs;Kun Li;Daisy Zhe Wang;George Chitouras
Affiliations:
University of Florida, Gainesville, Florida, USA;University of Florida, Gainesville, FL, USA;University of Florida, Gainesville, FL, USA;University of Florida, Gainesville, FL, USA;Greenplum/EMC, San Mateo, CA, USA
Venue:
Proceedings of the 21st ACM international conference on Information and knowledge management
Year:
2012

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Abstract

In many domains, structured data and unstructured text are both important natural resources to fuel data analysis. Statistical text analysis needs to be performed over text data to extract structured information for further query processing. Typically, developers will need to connect multiple tools to build off-line batch processes to perform text analytic tasks. MADden is an integrated system developed for relational database systems such as PostgreSQL and Greenplum for real-time ad hoc query processing over structured and unstructured data. MADden implements four important text analytic functions that we have contributed to the MADlib open source library for textual analytics. In this demonstration, we will show the capability of the MADden text analytic library using computational journalism as the driving application. We show real-time declarative query processing over multiple data sources with both structured and text information.