32 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
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
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
Unfolding-Based Process Discovery
This paper presents a novel technique for process discovery. In contrast to
the current trend, which only considers an event log for discovering a process
model, we assume two additional inputs: an independence relation on the set of
logged activities, and a collection of negative traces. After deriving an
intermediate net unfolding from them, we perform a controlled folding giving
rise to a Petri net which contains both the input log and all
independence-equivalent traces arising from it. Remarkably, the derived Petri
net cannot execute any trace from the negative collection. The entire chain of
transformations is fully automated. A tool has been developed and experimental
results are provided that witness the significance of the contribution of this
paper.Comment: This is the unabridged version of a paper with the same title
appearead at the proceedings of ATVA 201
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
Reducing event variability in logs by clustering of word embeddings
Several business-to-business and business-to-consumer services are provided as a human-to-human conversation in which the provider representative guides the conversation towards its resolution based on her experience, following internal guidelines. Several attempts to automatize these services are becoming popular, but they are currently limited to procedures and objectives set during design step. Process discovery techniques could provide the necessary mechanisms to monitor event logs derived from textual conversations and expand the capabilities of conversational bots. Still, variability of textual messages hinders the utility of process discovery techniques by producing non-understandable unstructured process models. In this paper, we propose the usage of word embedding for combining events that have a semantically similar name.Peer ReviewedPostprint (author's final draft
Computing alignments with constraint programming : the acyclic case
Conformance checking confronts process models with real
process executions to detect and measure deviations between modelled
and observed behaviour. The core technique for conformance checking
is the computation of an alignment. Current approaches for alignment
computation rely on a shortest-path technique over the product of the
state-space of a model and the observed trace, thus suffering from the
well-known state explosion problem. This paper presents a fresh alternative
for alignment computation of acyclic process models, that encodes
the alignment problem as a Constraint Satisfaction Problem. Since modern
solvers for this framework are capable of dealing with large instances,
this contribution has a clear potential. Remarkably, our prototype implementation
can handle instances that represent a real challenge for current
techniques. Main advantages of using Constraint Programming paradigm
lie in the possibility to adapt parameters such as the maximum search
time, or the maximum misalignment allowed. Moreover, using search and
propagation algorithms incorporated in Constraint Programming Solvers
permits to find solutions for problems unsolvable with other techniques.Ministerio de EconomĂa y Competitividad TIN2015-63502-C3-2-RMinisterio de EconomĂa y Competitividad TIN2013-46181-C2-1-
An evolutionary technique to approximate multiple optimal alignments
The alignment of observed and modeled behavior is an essential aid for organizations, since it opens the door for root-cause analysis and enhancement of processes. The state-of-the-art technique for computing alignments has exponential time and space complexity, hindering its applicability for medium and large instances. Moreover, the fact that there may be multiple optimal alignments is perceived as a negative situation, while in reality it may provide a more comprehensive picture of the model’s explanation of observed behavior, from which other techniques may benefit. This paper presents a novel evolutionary technique for approximating multiple optimal alignments. Remarkably, the memory footprint of the proposed technique is bounded, representing an unprecedented guarantee with respect to the state-of-the-art methods for the same task. The technique is implemented into a tool, and experiments on several benchmarks are provided.Peer ReviewedPostprint (author's final draft