TY - GEN
T1 - Concept of operations for knowledge discovery from "big data" across enterprise data warehouses
AU - Sukumar, Sreenivas R.
AU - Olama, Mohammed M.
AU - McNair, Allen W.
AU - Nutaro, James J.
PY - 2013
Y1 - 2013
N2 - 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.
AB - 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.
KW - "Big Data"
KW - Concept of operations
KW - Data integration
KW - Multi-agency data integration
UR - http://www.scopus.com/inward/record.url?scp=84881190527&partnerID=8YFLogxK
U2 - 10.1117/12.2016321
DO - 10.1117/12.2016321
M3 - Conference contribution
AN - SCOPUS:84881190527
SN - 9780819495495
T3 - Proceedings of SPIE - The International Society for Optical Engineering
BT - Next-Generation Analyst
T2 - Next-Generation Analyst
Y2 - 29 April 2013 through 30 April 2013
ER -