@inproceedings{d72d22e1b2034eef861d3cff0709905d,
title = "Activity matching with human intelligence",
abstract = "Effective matching of activities is the first step toward successful process model matching and search. The problem is nontrivial and has led to a variety of computational similarity metrics and matching approaches, however all still with low performance in terms of precision and recall. In this paper, instead, we study how to leverage on human intelligence to identify matches among activities and show that the problem is not as straightforward as most computational approaches assume. We access human intelligence (i) by crowdsourcing the activity matching problem to generic workers and (ii) by eliciting ground truth matches from experts. The precision and recall we achieve and the qualitative analysis of the results testify huge potential for a human-based activity matching that contemplates disagreement and interpretation.",
keywords = "Activity matching, Crowdsourcing, Label matching",
author = "Carlos Rodr{\'i}Guez and Christopher Klinkm{\"u}ller and Ingo Weber and Florian Daniel and Fabio Casati",
year = "2016",
doi = "10.1007/978-3-319-45468-9_8",
language = "English",
isbn = "9783319454672",
series = "Lecture Notes in Business Information Processing",
publisher = "Springer, Springer Nature",
pages = "124--140",
editor = "{La Rosa}, Marcello and Peter Loos and Oscar Pastor",
booktitle = "Business Process Management Forum",
address = "United States",
note = "International Conference on Business Process Management, BPM 2016 ; Conference date: 18-09-2016 Through 22-09-2016",
}