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Raffaele Conforti

Researcher at University of Melbourne

Publications -  52
Citations -  2074

Raffaele Conforti is an academic researcher from University of Melbourne. The author has contributed to research in topics: Process mining & Business process. The author has an hindex of 23, co-authored 52 publications receiving 1705 citations. Previous affiliations of Raffaele Conforti include Queensland University of Technology.

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Journal ArticleDOI

Automated Discovery of Process Models from Event Logs: Review and Benchmark

Adriano Augusto, +7 more
- 01 Apr 2019 - 
TL;DR: The results highlight gaps and unexplored tradeoffs in the field, including the lack of scalability of some methods and a strong divergence in their performance with respect to the different quality metrics used.
Journal ArticleDOI

Filtering Out Infrequent Behavior from Business Process Event Logs

Raffaele Conforti, +2 more
- 01 Feb 2017 - 
TL;DR: The proposed technique is evaluated in detail and it is shown that its application in conjunction with certain existing process discovery algorithms significantly improves the quality of the discovered process models and that it scales well to large datasets.
Book ChapterDOI

Complex Symbolic Sequence Encodings for Predictive Monitoring of Business Processes

Anna Leontjeva, +4 more
TL;DR: In this paper, the authors address the problem of predicting the outcome of an ongoing case of a business process based on event logs by treating traces as complex symbolic sequences, that is, sequences of events each carrying a data payload.
Posted Content

Automated Discovery of Process Models from Event Logs: Review and Benchmark

Adriano Augusto, +7 more
- 05 May 2017 - 
TL;DR: In this paper, a systematic review and comparative evaluation of automated process discovery methods, using an open-source benchmark and covering twelve publicly-available real-life event logs, twelve proprietary real life event logs and nine quality metrics, is presented.
Journal ArticleDOI

Split miner: automated discovery of accurate and simple business process models from event logs

Adriano Augusto, +5 more
- 01 May 2019 - 
TL;DR: Split Miner as discussed by the authors combines a novel approach to filter the directly-follows graph induced by an event log, with an approach to identify combinations of split gateways that accurately capture the concurrency, conflict and causal relations between neighbors in the graph.