TY - GEN
T1 - Mining the log-tree of process traces
T2 - 27th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2015
AU - Luca, Canensi
AU - Giorgio, Leonardi
AU - Stefania, Montani
AU - Paolo, Terenziani
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2016/1/4
Y1 - 2016/1/4
N2 - Logs recording the traces of execution of previous process instances can be exploited for different process management tasks, such as prediction and recommendation in operational support. Efficient retrieval of past traces can be very important to achieve such tasks, while building a model of the process from the log can support problem/anomaly detection and, more generally, process analysis. Trace retrieval is gaining attention in the Case Based Reasoning research community, but so far it has been faced in a completely separate way from the construction of a process model from the log, instead, we propose an approach aiming at integrating these two goals. The core notion of our proposal is the log-tree, which constitutes a bridge between the notions of (log) index and process model. We propose a mining algorithm to build it, and an algorithm exploiting it for trace retrieval. Future extensions of our initial contribution are also widely discussed.
AB - Logs recording the traces of execution of previous process instances can be exploited for different process management tasks, such as prediction and recommendation in operational support. Efficient retrieval of past traces can be very important to achieve such tasks, while building a model of the process from the log can support problem/anomaly detection and, more generally, process analysis. Trace retrieval is gaining attention in the Case Based Reasoning research community, but so far it has been faced in a completely separate way from the construction of a process model from the log, instead, we propose an approach aiming at integrating these two goals. The core notion of our proposal is the log-tree, which constitutes a bridge between the notions of (log) index and process model. We propose a mining algorithm to build it, and an algorithm exploiting it for trace retrieval. Future extensions of our initial contribution are also widely discussed.
UR - https://www.scopus.com/pages/publications/84963625918
U2 - 10.1109/ICTAI.2015.55
DO - 10.1109/ICTAI.2015.55
M3 - Conference contribution
AN - SCOPUS:84963625918
T3 - Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI
SP - 310
EP - 316
BT - Proceedings - 2015 IEEE 27th International Conference on Tools with Artificial Intelligence, ICTAI 2015
PB - IEEE Computer Society
Y2 - 9 November 2015 through 11 November 2015
ER -