TY - GEN
T1 - Balancing interactive data management of massive data with situational awareness through smart aggregation
AU - Tesone, Daniel R.
AU - Goodall, John R.
PY - 2007
Y1 - 2007
N2 - Designing a visualization system capable of processing, managing, and presenting massive data sets while maximizing the user's situational awareness (SA) is a challenging, but important, research question in visual analytics. Traditional data management and interactive retrieval approaches have often focused on solving the data overload problem at the expense of the user's SA. This paper discusses various data management strategies and the strengths and limitations of each approach in providing the user with SA. A new data management strategy, coined Smart Aggregation, is presented as a powerful approach to overcome the challenges of both massive data sets and maintaining SA. By combining automatic data aggregation with user-defined controls on what, how, and when data should be aggregated, we present a visualization system that can handle massive amounts of data while affording the user with the best possible SA. This approach ensures that a system is always usable in terms of both system resources and human perceptual resources. We have implemented our Smart Aggregation approach in a visual analytics system called VIAssist (Visual Assistant for Information Assurance Analysis) to facilitate exploration, discovery, and SA in the domain of Information Assurance.
AB - Designing a visualization system capable of processing, managing, and presenting massive data sets while maximizing the user's situational awareness (SA) is a challenging, but important, research question in visual analytics. Traditional data management and interactive retrieval approaches have often focused on solving the data overload problem at the expense of the user's SA. This paper discusses various data management strategies and the strengths and limitations of each approach in providing the user with SA. A new data management strategy, coined Smart Aggregation, is presented as a powerful approach to overcome the challenges of both massive data sets and maintaining SA. By combining automatic data aggregation with user-defined controls on what, how, and when data should be aggregated, we present a visualization system that can handle massive amounts of data while affording the user with the best possible SA. This approach ensures that a system is always usable in terms of both system resources and human perceptual resources. We have implemented our Smart Aggregation approach in a visual analytics system called VIAssist (Visual Assistant for Information Assurance Analysis) to facilitate exploration, discovery, and SA in the domain of Information Assurance.
KW - Data management
KW - Data retrieval
KW - Information visualization
KW - Situational awareness
KW - Smart aggregation
KW - Visual analytics
UR - http://www.scopus.com/inward/record.url?scp=47349092629&partnerID=8YFLogxK
U2 - 10.1109/VAST.2007.4388998
DO - 10.1109/VAST.2007.4388998
M3 - Conference contribution
AN - SCOPUS:47349092629
SN - 9781424416592
T3 - VAST IEEE Symposium on Visual Analytics Science and Technology 2007, Proceedings
SP - 67
EP - 74
BT - VAST IEEE Symposium on Visual Analytics Science and Technology 2007, Proceedings
T2 - VAST IEEE Symposium on Visual Analytics Science and Technology 2007
Y2 - 30 October 2007 through 1 November 2007
ER -