TY - GEN
T1 - DimScanner
T2 - 11th IEEE Conference on Visual Analytics Science and Technology, VAST 2016
AU - Xia, Jing
AU - Chen, Wei
AU - Hou, Yumeng
AU - Hu, Wanqi
AU - Xinxin, Huang
AU - Ebertk, David S.
N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].
PY - 2017/3/20
Y1 - 2017/3/20
N2 - Exploring multi-dimensional datasets can be cumbersome if data analysts have little knowledge about the data. Various dimension relation inspection tools and dimension exploration tools have been proposed for efficient data examining and understanding. However, the needed workload varies largely with respect to data complexity and user expertise, which can only be reduced with rich background knowledge over the data. In this paper we address the workload challenge with a data structuring and exploration scheme that affords dimension relation detection and that serves as the background knowledge for further investigation. We contribute a novel data structuring scheme that leverages an information-theoretic view structuring algorithm to uncover information-aware relations among different data views, and thereby discloses redundancy and other relation patterns among dimensions. The integrated system, DimScanner, empowers analysts with rich user controls and assistance widgets to interactively detect the relations of multi-dimensional data. © 2016 IEEE.
AB - Exploring multi-dimensional datasets can be cumbersome if data analysts have little knowledge about the data. Various dimension relation inspection tools and dimension exploration tools have been proposed for efficient data examining and understanding. However, the needed workload varies largely with respect to data complexity and user expertise, which can only be reduced with rich background knowledge over the data. In this paper we address the workload challenge with a data structuring and exploration scheme that affords dimension relation detection and that serves as the background knowledge for further investigation. We contribute a novel data structuring scheme that leverages an information-theoretic view structuring algorithm to uncover information-aware relations among different data views, and thereby discloses redundancy and other relation patterns among dimensions. The integrated system, DimScanner, empowers analysts with rich user controls and assistance widgets to interactively detect the relations of multi-dimensional data. © 2016 IEEE.
KW - data exploration
KW - High-dimensional data visualization
KW - information-aware relation
UR - http://www.scopus.com/inward/record.url?scp=85017279108&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85017279108&origin=recordpage
U2 - 10.1109/VAST.2016.7883514
DO - 10.1109/VAST.2016.7883514
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 9781509056613
T3 - 2016 IEEE Conference on Visual Analytics Science and Technology, VAST 2016 - Proceedings
SP - 81
EP - 90
BT - 2016 IEEE Conference on Visual Analytics Science and Technology, VAST 2016 - Proceedings
PB - IEEE
Y2 - 23 October 2016 through 28 October 2016
ER -