TY - GEN
T1 - Efficient Exploration of Long Data Series
T2 - 22nd International Conference on Human-Computer Interaction, HCII 2020
AU - Wortelen, Bertram
AU - Herdel, Viviane
AU - Pfeiffer, Oliver
AU - Harre, Marie Christin
AU - Saager, Marcel
AU - Lanezki, Mathias
N1 - Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020/1/1
Y1 - 2020/1/1
N2 - Today’s easy access to data, low cost sensors and data transmission infrastructure leads to an abundance of data about complex systems in many domains like industrial process control, network intrusion detection or maritime surveillance. Analyzing this data can take a lot of effort and often cannot be fully automated. As it is hard to fully automate such analysis tasks, we present an HMI framework that supports an analyst in exploring and navigating through multiple time series of data. It is a semi-automatic approach that uses algorithms for automatically labelling low-level events in the data, but leaves the task of evaluation and interpretation to the human operator. These events are highlighted on specific time bars in the HMI framework. It enables the analyst to 1) summarize the main features of the data series, 2) filter it depending on the analysis objective, 3) identify and prioritize relevant section in the data and 4) directly jump to these sections. We present the theoretical concept of the HMI framework and demonstrate it on a process control application for hybrid energy systems.
AB - Today’s easy access to data, low cost sensors and data transmission infrastructure leads to an abundance of data about complex systems in many domains like industrial process control, network intrusion detection or maritime surveillance. Analyzing this data can take a lot of effort and often cannot be fully automated. As it is hard to fully automate such analysis tasks, we present an HMI framework that supports an analyst in exploring and navigating through multiple time series of data. It is a semi-automatic approach that uses algorithms for automatically labelling low-level events in the data, but leaves the task of evaluation and interpretation to the human operator. These events are highlighted on specific time bars in the HMI framework. It enables the analyst to 1) summarize the main features of the data series, 2) filter it depending on the analysis objective, 3) identify and prioritize relevant section in the data and 4) directly jump to these sections. We present the theoretical concept of the HMI framework and demonstrate it on a process control application for hybrid energy systems.
KW - Data exploration
KW - Data visualization
KW - Event detection
KW - System monitoring
KW - Time series
UR - http://www.scopus.com/inward/record.url?scp=85088748370&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-50732-9_64
DO - 10.1007/978-3-030-50732-9_64
M3 - Conference contribution
AN - SCOPUS:85088748370
SN - 9783030507312
T3 - Communications in Computer and Information Science
SP - 495
EP - 503
BT - HCI International 2020 - Posters - 22nd International Conference, HCII 2020, Proceedings
A2 - Stephanidis, Constantine
A2 - Antona, Margherita
PB - Springer
Y2 - 19 July 2020 through 24 July 2020
ER -
