| Title |
Matching events and activities by integrating behavioral aspects and label analysis
|
|---|---|
| Published in |
Software and Systems Modeling, May 2017
|
| DOI | 10.1007/s10270-017-0603-z |
| Pubmed ID | |
| Authors |
Thomas Baier, Claudio Di Ciccio, Jan Mendling, Mathias Weske |
| Abstract |
Nowadays, business processes are increasingly supported by IT services that produce massive amounts of event data during the execution of a process. These event data can be used to analyze the process using process mining techniques to discover the real process, measure conformance to a given process model, or to enhance existing models with performance information. Mapping the produced events to activities of a given process model is essential for conformance checking, annotation and understanding of process mining results. In order to accomplish this mapping with low manual effort, we developed a semi-automatic approach that maps events to activities using insights from behavioral analysis and label analysis. The approach extracts Declare constraints from both the log and the model to build matching constraints to efficiently reduce the number of possible mappings. These mappings are further reduced using techniques from natural language processing, which allow for a matching based on labels and external knowledge sources. The evaluation with synthetic and real-life data demonstrates the effectiveness of the approach and its robustness toward non-conforming execution logs. |
Mendeley readers
The data shown below were compiled from readership statistics for 80 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 80 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 13 | 16% |
| Student > Ph. D. Student | 11 | 14% |
| Student > Bachelor | 11 | 14% |
| Researcher | 7 | 9% |
| Student > Doctoral Student | 5 | 6% |
| Other | 15 | 19% |
| Unknown | 18 | 23% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 43 | 54% |
| Business, Management and Accounting | 4 | 5% |
| Engineering | 3 | 4% |
| Mathematics | 1 | 1% |
| Nursing and Health Professions | 1 | 1% |
| Other | 7 | 9% |
| Unknown | 21 | 26% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 05 December 2023.
All research outputs
#8,725,813
of 25,838,141 outputs
Outputs from Software and Systems Modeling
#148
of 773 outputs
Outputs of similar age
#127,355
of 328,980 outputs
Outputs of similar age from Software and Systems Modeling
#3
of 8 outputs
Altmetric has tracked 25,838,141 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 773 research outputs from this source. They receive a mean Attention Score of 2.2. This one has gotten more attention than average, scoring higher than 74% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 328,980 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 52% of its contemporaries.
We're also able to compare this research output to 8 others from the same source and published within six weeks on either side of this one. This one has scored higher than 5 of them.