TY - GEN
T1 - A Visual Analytics Approach to Explore Potential Anomalous Behavior in Corporate Communication
AU - Sun, Bo
AU - Pang, Ce
N1 - Publisher Copyright:
© 2021 ACM.
PY - 2021/2/2
Y1 - 2021/2/2
N2 - In this paper, we present a visual analytics approach to analyze temporal patterns of human communication from a vast corporate communication dataset. Our approach mainly relies on visualization and mapping techniques to discover the patterns, which then support feature model development for a machine learning method. In contrast to previous work, our technique targets communication data presenting only temporal and interaction information, and focuses on the pattern searches of anomaly behaviors. The new visual analytics platform can be effectively used to analyze the differences between normal and suspicious procurement behaviors in corporation using email, phone call, and personal meeting records. By using the platform, we successfully found other potentially illegal activity based on suggested suspicious behaviors.
AB - In this paper, we present a visual analytics approach to analyze temporal patterns of human communication from a vast corporate communication dataset. Our approach mainly relies on visualization and mapping techniques to discover the patterns, which then support feature model development for a machine learning method. In contrast to previous work, our technique targets communication data presenting only temporal and interaction information, and focuses on the pattern searches of anomaly behaviors. The new visual analytics platform can be effectively used to analyze the differences between normal and suspicious procurement behaviors in corporation using email, phone call, and personal meeting records. By using the platform, we successfully found other potentially illegal activity based on suggested suspicious behaviors.
UR - http://www.scopus.com/inward/record.url?scp=85112463850&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85112463850&partnerID=8YFLogxK
U2 - 10.1145/3456529.3456550
DO - 10.1145/3456529.3456550
M3 - Conference contribution
AN - SCOPUS:85112463850
T3 - ACM International Conference Proceeding Series
SP - 119
EP - 128
BT - ICCDA 2021 - Proceedings of the 2021 5th International Conference on Compute and Data Analysis
PB - Association for Computing Machinery
T2 - 5th International Conference on Compute and Data Analysis, ICCDA 2021
Y2 - 2 February 2021 through 4 February 2021
ER -