TY - GEN
T1 - A Framework for Human-in-the-loop Monitoring of Concept-drift Detection in Event Log Stream
AU - Barbon Junior, Sylvio
AU - Tavares, Gabriel Marques
AU - Da Costa, Victor G.Turrisi
AU - Ceravolo, Paolo
AU - Damiani, Ernesto
N1 - Funding Information:
The authors would like to thank the Information and Communication Technology (ICT) Fund. ABU DHABI for the financial support for this research.
Publisher Copyright:
© 2018 IW3C2 (International World Wide Web Conference Committee), published under Creative Commons CC BY 4.0 License.
PY - 2018/4/23
Y1 - 2018/4/23
N2 - One of the main challenges of Cognitive Computing (CC) is reacting to evolving environments in near-real time. Therefore, it is expected that CC models provide solutions by examining a summary of past history, rather than using full historical data. This strategy has significant benefits in terms of response time and space complexity but poses new challenges in term of concept-drift detection, where both long term and short terms dynamics should be taken into account. In this paper, we introduce the Concept-Drift in Event Stream Framework (CDESF) that addresses some of these challenges for data streams recording the execution of a Web-based business process. Thanks to CDESF support for feature transformation, we perform density clustering in the transformed feature space of the process event stream, observe track concept-drift over time and identify anomalous cases in the form of outliers. We validate our approach using logs of an e-healthcare process.
AB - One of the main challenges of Cognitive Computing (CC) is reacting to evolving environments in near-real time. Therefore, it is expected that CC models provide solutions by examining a summary of past history, rather than using full historical data. This strategy has significant benefits in terms of response time and space complexity but poses new challenges in term of concept-drift detection, where both long term and short terms dynamics should be taken into account. In this paper, we introduce the Concept-Drift in Event Stream Framework (CDESF) that addresses some of these challenges for data streams recording the execution of a Web-based business process. Thanks to CDESF support for feature transformation, we perform density clustering in the transformed feature space of the process event stream, observe track concept-drift over time and identify anomalous cases in the form of outliers. We validate our approach using logs of an e-healthcare process.
KW - clustering
KW - concept-drift
KW - dbscan
KW - process mining
KW - stream mining
UR - http://www.scopus.com/inward/record.url?scp=85085164762&partnerID=8YFLogxK
U2 - 10.1145/3184558.3186343
DO - 10.1145/3184558.3186343
M3 - Conference contribution
AN - SCOPUS:85085164762
T3 - The Web Conference 2018 - Companion of the World Wide Web Conference, WWW 2018
SP - 319
EP - 326
BT - The Web Conference 2018 - Companion of the World Wide Web Conference, WWW 2018
T2 - 27th International World Wide Web, WWW 2018
Y2 - 23 April 2018 through 27 April 2018
ER -