657 research outputs found
Discovering duplicate tasks in transition systems for the simplification of process models
This work presents a set of methods to improve the understandability of process models. Traditionally, simplification methods trade off quality metrics, such as fitness or precision. Conversely, the methods proposed in this paper produce simplified models while preserving or even increasing fidelity metrics. The first problem addressed in the
paper is the discovery of duplicate tasks. A new method is proposed that avoids overfitting by working on the transition system generated by the log. The method is able to discover duplicate tasks even in the presence of concurrency and choice. The second problem is the structural simplification of the model by identifying optional and repetitive tasks. The tasks are substituted by annotated events that allow the removal of silent tasks and reduce the complexity of the
model. An important feature of the methods proposed in this paper is that they are independent from the actual miner used for process discovery.Peer ReviewedPostprint (author's final draft
Learning Hybrid Process Models From Events: Process Discovery Without Faking Confidence
Process discovery techniques return process models that are either formal
(precisely describing the possible behaviors) or informal (merely a "picture"
not allowing for any form of formal reasoning). Formal models are able to
classify traces (i.e., sequences of events) as fitting or non-fitting. Most
process mining approaches described in the literature produce such models. This
is in stark contrast with the over 25 available commercial process mining tools
that only discover informal process models that remain deliberately vague on
the precise set of possible traces. There are two main reasons why vendors
resort to such models: scalability and simplicity. In this paper, we propose to
combine the best of both worlds: discovering hybrid process models that have
formal and informal elements. As a proof of concept we present a discovery
technique based on hybrid Petri nets. These models allow for formal reasoning,
but also reveal information that cannot be captured in mainstream formal
models. A novel discovery algorithm returning hybrid Petri nets has been
implemented in ProM and has been applied to several real-life event logs. The
results clearly demonstrate the advantages of remaining "vague" when there is
not enough "evidence" in the data or standard modeling constructs do not "fit".
Moreover, the approach is scalable enough to be incorporated in
industrial-strength process mining tools.Comment: 25 pages, 12 figure
Anti-alignments in conformance checking: the dark side of process models
Conformance checking techniques asses the suitability of a process model in representing an underlying process, observed through a collection of real executions. These techniques suffer from the wellknown state space explosion problem, hence handling process models exhibiting large or even infinite state spaces remains a challenge. One important metric in conformance checking is to asses the precision of the model with respect to the observed executions, i.e., characterize the ability of the model to produce behavior unrelated to the one observed. By avoiding the computation of the full state space of a model, current techniques only provide estimations of the precision metric, which in some situations tend to be very optimistic, thus hiding real problems a process model may have. In this paper we present the notion of antialignment as a concept to help unveiling traces in the model that may deviate significantly from the observed behavior. Using anti-alignments, current estimations can be improved, e.g., in precision checking. We show how to express the problem of finding anti-alignments as the satisfiability of a Boolean formula, and provide a tool which can deal with large models efficiently.Peer ReviewedPostprint (author's final draft
A recursive paradigm for aligning observed behavior of large structured process models
The alignment of observed and modeled behavior is a crucial problem in process mining, since it opens the door for conformance checking and enhancement of process models. The state of the art techniques for the computation of alignments rely on a full exploration of the combination of the model state space and the observed behavior (an event log), which hampers their applicability for large instances. This paper presents a fresh view to the alignment problem: the computation of alignments is casted as the resolution of Integer Linear Programming models, where the user can decide the granularity of the alignment steps. Moreover, a novel recursive strategy is used to split
the problem into small pieces, exponentially reducing the complexity of the ILP models to be solved. The contributions of this paper represent a promising alternative to fight the inherent complexity of computing alignments for large instances.Peer ReviewedPostprint (author's final draft
Multi-level Service Approach for Flexible Support of Design Processes
Part III: Sustainable ServicesInternational audienceThe need to answer quickly to new market opportunities and the high variability of consumer demands tend industrial companies to review their adopted organisation, so to improve their reactivity and to facilitate the coupling with the business enactment. Therefore, these companies require agility in their information systems to allow business needs scalability and design process flexibility. We propose in this paper, the business activities as a service based on the service paradigm and whereas a design process is made of agile services orchestrations. We discuss the interest to use a service-oriented approach and propose a layered architecture for design process enactment
Enhancing workflow-nets with data for trace completion
The growing adoption of IT-systems for modeling and executing (business)
processes or services has thrust the scientific investigation towards
techniques and tools which support more complex forms of process analysis. Many
of them, such as conformance checking, process alignment, mining and
enhancement, rely on complete observation of past (tracked and logged)
executions. In many real cases, however, the lack of human or IT-support on all
the steps of process execution, as well as information hiding and abstraction
of model and data, result in incomplete log information of both data and
activities. This paper tackles the issue of automatically repairing traces with
missing information by notably considering not only activities but also data
manipulated by them. Our technique recasts such a problem in a reachability
problem and provides an encoding in an action language which allows to
virtually use any state-of-the-art planning to return solutions
Managing Process Variants as an Information Resource
Many business solutions provide best practice process templates, both generic as well as for specific industry sectors. However, it is often the variance from template solutions that provide organizations with intellectual capital and competitive differentiation. In this paper, we present a modeling framework that is conducive to constrained variance, by supporting user driven process adaptations. The focus of the paper is on providing a means of utilizing the adaptations effectively for process improvement through effective management of the process variants repository (PVR). In particular, we will provide deliberations towards a facility to provide query functionality for PVR that is specifically targeted for effective search and retrieval of process variants
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