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4 Related Work
Several approaches have been proposed for process model abstraction. The work
by Polyvyanyy et al. builds on an algorithm for aggregating activities based on
a slider and specific abstraction criteria [3]. Abstraction criteria are discussed
in [12,13]. A recent paper presents an abstraction approach based on behavioral
profiles [14]. For a set of activities, this approach generates the control flow of
the aggregated model. Both approaches do not generate names for aggregated
activities, such that our work is complementary. A different approach based
on meronymy relations is presented in [15]. This approach inspects meronymy
relations between activity labels to find aggregation candidates. It integrates the
problems of finding aggregation candidates and aggregation names. Our work is
more general in that sense that it is able to derive names for arbitrary process
model fragments, even if they do not share a meronymy relation.
The linguistic analysis of activity labels is also an import task in process model
matching and similarity calculation [16,17,18]. Different approaches of matching
process models are integrated in [19]. This area is also related to research on
semantic annotation of business process models [20]. Recent research has also
started using natural language processing techniques for generating process mod-
els from text. Goncalves et al. generate process models from group stories [21].
The approach by the University of Klagenfurt combines linguistic analysis with
user feedback [22]. The Rapid Business Process Discovery (R-BPD) framework
uses natural language techniques for constructing BPMN models from corporate
documentations or web-content [23]. Anaphora resolution is tackled in a recent
approach to generate BPMN models [24].
5Conluon
In this paper, we have addressed the problem of automatically generating names
for process models. Our work is motivated by the fact that existing works on
process model abstraction require telling names for structurally aggregated pro-
cess fragments. Our overall contribution is an automatic naming approach that
builds on the linguistic analysis of the elements of process models from industry.
The work presented in this paper has significant implications for research and
practice. The automatic generation provides the basis not only for proposing
names of whole processes, but also for process fragments. In this regard, our ap-
proach can be used for instance to dynamically generate abstractions of different
granularity as the user is interacting with the modeling tool.
The main task for future research is the validation of the presented approach.
This may include the comparison of the given with the generated names but also
an applicability assessment by humans. In addition, we aim to further investigate
the usability of different naming strategies. Currently, if a single name for an ab-
stracted fragment is needed, a system can only make a random suggestion from
the set of name proposals. We expect that the strategy itself, but also the length
of the suggested name has a significant impact on the perceived usefulness. Based
on such insight, we will be able to select the best name from a set of suggestions.
