TY - GEN
T1 - Semantic trace comparison at multiple levels of abstraction
AU - Montani, Stefania
AU - Striani, Manuel
AU - Quaglini, Silvana
AU - Cavallini, Anna
AU - Leonardi, Giorgio
N1 - Publisher Copyright:
© Springer International Publishing AG 2017.
PY - 2017
Y1 - 2017
N2 - Event logs constitute a rich source of information for several process analysis activities, which can take advantage of similar traces retrieval. The capability of relating semantic structures such as taxonomies to actions in the traces can enable trace comparison to work at different levels of abstraction and, therefore, to mask irrelevant details, and make the identification of similar traces much more flexible. In this paper, we propose a trace abstraction mechanism, which maps actions in the log traces to instances of ground concepts in a taxonomy, and then allows to generalize them up to the desired level. We also show how we have extended a trace similarity metric we defined in our previous work, in order to allow abstracted trace comparison as well. Our framework has been tested in the field of stroke management, where it has allowed us to cluster similar traces, corresponding to correct medical behaviors, abstracting from details, but still preserving the capabilities of identifying outlying situations.
AB - Event logs constitute a rich source of information for several process analysis activities, which can take advantage of similar traces retrieval. The capability of relating semantic structures such as taxonomies to actions in the traces can enable trace comparison to work at different levels of abstraction and, therefore, to mask irrelevant details, and make the identification of similar traces much more flexible. In this paper, we propose a trace abstraction mechanism, which maps actions in the log traces to instances of ground concepts in a taxonomy, and then allows to generalize them up to the desired level. We also show how we have extended a trace similarity metric we defined in our previous work, in order to allow abstracted trace comparison as well. Our framework has been tested in the field of stroke management, where it has allowed us to cluster similar traces, corresponding to correct medical behaviors, abstracting from details, but still preserving the capabilities of identifying outlying situations.
UR - http://www.scopus.com/inward/record.url?scp=85022343868&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-61030-6_15
DO - 10.1007/978-3-319-61030-6_15
M3 - Conference contribution
AN - SCOPUS:85022343868
SN - 9783319610290
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 212
EP - 226
BT - Case-Based Reasoning Research and Development - 25th International Conference, ICCBR 2017, Proceedings
A2 - Aha, David W.
A2 - Lieber, Jean
PB - Springer Verlag
T2 - 25th International Conference on Case-Based Reasoning, ICCBR 2017
Y2 - 26 June 2017 through 28 June 2017
ER -