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Overview - MONGOOSE (MONitoring Global Online Opinions via Semantic Extraction)

Data analytics technology is becoming increasingly important as people try to extract as much value as possible from their most valuable resource - the information around them, whether in their organizations or freely and publicly available. While many data analytics efforts are focused on deriving information from a particularly interesting, and often difficult, question, these projects tend to spend most of their cycles acquiring and ingesting data. Thus, the focus of these efforts is more directed towards data ingestion.

System Overview


  1. A suite of technologies that one can plug domain knowledge cartridges into and that outputs data suitable for OLAP or BI consumption. One plugs in small amounts of domain knowledge that involves pulling in unstructured, semi-structured and structured data, and MONGOOSE converts it all into structured form.
  2. A Platform for Worst-Case Scenario Workflow Management. MONGOOSE is built on the assumption that failure happens and it must be handled quickly and seamlessly, such that it does not stop or hinder information ingest.
  3. A Platform for Community-Based Information Extraction around specific phenomenon that can be fed into statistical analysis tools.

Team: Alfredo Alba, Varun Bhagwan, Tyrone Grandison, Daniel Gruhl, Jan Pieper



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