Concept of operations for knowledge discovery from "big data" across enterprise data warehouses

Sreenivas R. Sukumar, Mohammed M. Olama, Allen W. McNair, James J. Nutaro

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

The success of data-driven business in government, science, and private industry is driving the need for seamless integration of intra and inter-enterprise data sources to extract knowledge nuggets in the form of correlations, trends, patterns and behaviors previously not discovered due to physical and logical separation of datasets. Today, as volume, velocity, variety and complexity of enterprise data keeps increasing, the next generation analysts are facing several challenges in the knowledge extraction process. Towards addressing these challenges, data-driven organizations that rely on the success of their analysts have to make investment decisions for sustainable data/information systems and knowledge discovery. Options that organizations are considering are newer storage/analysis architectures, better analysis machines, redesigned analysis algorithms, collaborative knowledge management tools, and query builders amongst many others. In this paper, we present a concept of operations for enabling knowledge discovery that data-driven organizations can leverage towards making their investment decisions. We base our recommendations on the experience gained from integrating multi-agency enterprise data warehouses at the Oak Ridge National Laboratory to design the foundation of future knowledge nurturing data-system architectures.

Original languageEnglish
Title of host publicationNext-Generation Analyst
DOIs
StatePublished - 2013
EventNext-Generation Analyst - Baltimore, MD, United States
Duration: Apr 29 2013Apr 30 2013

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume8758
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

ConferenceNext-Generation Analyst
Country/TerritoryUnited States
CityBaltimore, MD
Period04/29/1304/30/13

Keywords

  • "Big Data"
  • Concept of operations
  • Data integration
  • Multi-agency data integration

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