TY - GEN
T1 - Timeradar
AU - Nguyen, Ngan V.T.
AU - Hass, Jon
AU - Dang, Tommy
N1 - Funding Information:
The authors acknowledge the High-Performance Computing Center (HPCC) at Texas Tech University [27] in Lubbock for providing HPC resources and data that have contributed to the research results reported within this paper. This research is supported in part by the National Science Foundation under grant CNS-1362134, OAC-1835892, and through the IUCRC-CAC (Cloud and Autonomic Computing) Dell Inc. membership contribution.
Publisher Copyright:
© 2021 IEEE.
PY - 2021/7
Y1 - 2021/7
N2 - Analyzing temporal event sequences play an important role in many application domains, such as workload behavior analysis, hardware fault diagnosis, and natural disaster resilience. As data volume keeps growing, real-world temporal event sequences are often noisy, missing, and complex, thus making it a daunting task to convey much of the information from a comprehensive overview for analysts. This work proposes a visual approach based on clustering and superimposing to construct a high-level overview of sequential event data while balancing the amount of information and its cardinality. We also implement an interactive prototype, called TimeRadar, that allows domain analysts to simultaneously analyze sequence clustering, extract distribution patterns, drill multiple levels of detail to accelerate the analysis. This work aims to provide an abstracted view of temporal event sequences where significant events are highlighted. The TimeRadar is demonstrated through case studies with real-world temporal datasets of various sizes.
AB - Analyzing temporal event sequences play an important role in many application domains, such as workload behavior analysis, hardware fault diagnosis, and natural disaster resilience. As data volume keeps growing, real-world temporal event sequences are often noisy, missing, and complex, thus making it a daunting task to convey much of the information from a comprehensive overview for analysts. This work proposes a visual approach based on clustering and superimposing to construct a high-level overview of sequential event data while balancing the amount of information and its cardinality. We also implement an interactive prototype, called TimeRadar, that allows domain analysts to simultaneously analyze sequence clustering, extract distribution patterns, drill multiple levels of detail to accelerate the analysis. This work aims to provide an abstracted view of temporal event sequences where significant events are highlighted. The TimeRadar is demonstrated through case studies with real-world temporal datasets of various sizes.
UR - http://www.scopus.com/inward/record.url?scp=85115856875&partnerID=8YFLogxK
U2 - 10.1109/COMPSAC51774.2021.00057
DO - 10.1109/COMPSAC51774.2021.00057
M3 - Conference contribution
AN - SCOPUS:85115856875
T3 - Proceedings - 2021 IEEE 45th Annual Computers, Software, and Applications Conference, COMPSAC 2021
SP - 350
EP - 356
BT - Proceedings - 2021 IEEE 45th Annual Computers, Software, and Applications Conference, COMPSAC 2021
A2 - Chan, W. K.
A2 - Claycomb, Bill
A2 - Takakura, Hiroki
A2 - Yang, Ji-Jiang
A2 - Teranishi, Yuuichi
A2 - Towey, Dave
A2 - Segura, Sergio
A2 - Shahriar, Hossain
A2 - Reisman, Sorel
A2 - Ahamed, Sheikh Iqbal
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 12 July 2021 through 16 July 2021
ER -