8,674 research outputs found
Aggregating causal runs into workflow nets
This paper provides three algorithms for constructing system nets from sets of partially-ordered causal runs. The three aggregation algorithms differ with respect to the assumptions about the information contained in the causal runs. Specifically, we look at the situations where labels of con- ditions (i.e. references to places) or events (i.e. references to transitions) are unknown. Since the paper focusses on aggregation in the context of process mining, we solely look at work ow nets, i.e. the class of Petri nets with unique start and end places. The dierence of the work presented here and most work on process mining is the assumption that events are logged as partial orders instead of linear traces. Although the work is inspired by applications in the process mining and work ow domains, the results are generic and can be applied in other application domains. Keywords: Process mining, Petri net Synthesis, Aggregation, Runs, Process net
A region-based algorithm for discovering Petri nets from event logs
The paper presents a new method for the synthesis of Petri nets from event logs in the area of Process Mining. The method derives a bounded Petri net that over-approximates the behavior of an event log. The most important property is that it produces a net with the smallest behavior that still contains the behavior of the event log. The methods described in this paper have been implemented in a tool and tested on a set of examples.Peer ReviewedPostprint (author's final draft
Mining structured Petri nets for the visualization of process behavior
Visualization is essential for understanding the models obtained by process mining. Clear and efficient visual representations make the embedded information more accessible and analyzable. This work presents a novel approach for generating process models with structural properties that induce visually friendly layouts. Rather than generating a single model that captures all behaviors, a set of Petri net models is delivered, each one covering a subset of traces of the log. The models are mined by extracting slices of labelled transition systems with specific properties from the complete state space produced by the process logs. In most cases, few Petri nets are sufficient to cover a significant part of the behavior produced by the log.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
Process Mining of Programmable Logic Controllers: Input/Output Event Logs
This paper presents an approach to model an unknown Ladder Logic based
Programmable Logic Controller (PLC) program consisting of Boolean logic and
counters using Process Mining techniques. First, we tap the inputs and outputs
of a PLC to create a data flow log. Second, we propose a method to translate
the obtained data flow log to an event log suitable for Process Mining. In a
third step, we propose a hybrid Petri net (PN) and neural network approach to
approximate the logic of the actual underlying PLC program. We demonstrate the
applicability of our proposed approach on a case study with three simulated
scenarios
A Tool for Aligning Event Logs and Prescriptive Process Models through Automated Planning
In Conformance Checking, alignment is the problem of detecting and repairing nonconformity between the actual execution of a business process, as
recorded in an event log, and the model of the same process. Literature proposes solutions for the alignment problem that are implementations of planning algorithms built ad-hoc for the specific problem. Unfortunately, in the era of big data, these ad-hoc implementations do not scale sufficiently compared with well-established planning systems. In this paper, we tackle the above issue by presenting a tool, also available in ProM, to represent instances of the alignment problem as automated planning problems in PDDL (Planning Domain Definition Language) for which state-of-the-art planners can find a correct solution in a finite amount of time. If alignment problems are converted into planning problems, one can seamlessly update to the recent versions of the best performing automated planners, with advantages in term of versatility and customization. Furthermore, by employing several processes and event logs of different sizes, we show how our tool outperforms existing approaches of several order of magnitude and, in certain cases, carries out the task while existing approaches run out of memory
Heuristic Approaches for Generating Local Process Models through Log Projections
Local Process Model (LPM) discovery is focused on the mining of a set of
process models where each model describes the behavior represented in the event
log only partially, i.e. subsets of possible events are taken into account to
create so-called local process models. Often such smaller models provide
valuable insights into the behavior of the process, especially when no adequate
and comprehensible single overall process model exists that is able to describe
the traces of the process from start to end. The practical application of LPM
discovery is however hindered by computational issues in the case of logs with
many activities (problems may already occur when there are more than 17 unique
activities). In this paper, we explore three heuristics to discover subsets of
activities that lead to useful log projections with the goal of speeding up LPM
discovery considerably while still finding high-quality LPMs. We found that a
Markov clustering approach to create projection sets results in the largest
improvement of execution time, with discovered LPMs still being better than
with the use of randomly generated activity sets of the same size. Another
heuristic, based on log entropy, yields a more moderate speedup, but enables
the discovery of higher quality LPMs. The third heuristic, based on the
relative information gain, shows unstable performance: for some data sets the
speedup and LPM quality are higher than with the log entropy based method,
while for other data sets there is no speedup at all.Comment: paper accepted and to appear in the proceedings of the IEEE Symposium
on Computational Intelligence and Data Mining (CIDM), special session on
Process Mining, part of the Symposium Series on Computational Intelligence
(SSCI
Modelling epistasis in genetic disease using Petri nets, evolutionary computation and frequent itemset mining
Petri nets are useful for mathematically modelling disease-causing genetic epistasis. A Petri net model of an interaction has the potential to lead to biological insight into the cause of a genetic disease. However, defining a Petri net by hand for a particular interaction is extremely difficult because of the sheer complexity of the problem and degrees of freedom inherent in a Petri net’s architecture.
We propose therefore a novel method, based on evolutionary computation and data mining, for automatically constructing Petri net models of non-linear gene interactions. The method comprises two main steps. Firstly, an initial partial Petri net is set up with several repeated sub-nets that model individual genes and a set of constraints, comprising relevant common sense and biological knowledge, is also defined. These constraints characterise the class of Petri nets that are desired. Secondly, this initial Petri net structure and the constraints are used as the input to a genetic algorithm. The genetic algorithm searches for a Petri net architecture that is both a superset of the initial net, and also conforms to all of the given constraints. The genetic algorithm evaluation function that we employ gives equal weighting to both the accuracy of the net and also its parsimony.
We demonstrate our method using an epistatic model related to the presence of digital ulcers in systemic sclerosis patients that was recently reported in the literature. Our results show that although individual “perfect” Petri nets can frequently be discovered for this interaction, the true value of this approach lies in generating many different perfect nets, and applying data mining techniques to them in order to elucidate common and statistically significant patterns of interaction
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