15,097 research outputs found
Is my event log complete? -A probabilistic approach to process mining
Abstract-Process mining is a technique for extracting process models from event logs recorded by information systems. Process mining approaches normally rely on the assumption that the log to be mined is complete. Checking log completeness is known to be a difficult issue. Except for some trivial cases, checkable criteria for log completeness are not known. We overcome this problem by taking a probabilistic point of view. In this paper, we propose a method to compute the probability that the event log is complete. Our method provides a probabilistic lower bound for log completeness for three subclasses of Petri nets, namely, workflow nets, T-workflow nets, and S-workflow nets. Furthermore, based upon the complete log obtained by our methods, we propose two specialized mining algorithms to discover T-workflow nets and S-workflow nets, respectively. We back up our theoretical work with empirical studies that show that the probabilistic bounds computed by our method are reliable
Towards an Intelligent Workflow Designer based on the Reuse of Workflow Patterns
In order to perform process-aware information systems we need sophisticated methods and concepts for designing and modeling processes. Recently, research on workflow patterns has emerged in order to increase the reuse of recurring workflow structures. However, current workflow modeling tools do not provide functionalities that enable users to define, query, and reuse workflow patterns properly. In this paper we gather a suite for both process modeling and normalization based on workflow patterns reuse. This suite must be used in the extension of some workflow design tool. The suite comprises components for the design of processes
from both legacy systems and process modeling
Learning Heterogeneous Similarity Measures for Hybrid-Recommendations in Meta-Mining
The notion of meta-mining has appeared recently and extends the traditional
meta-learning in two ways. First it does not learn meta-models that provide
support only for the learning algorithm selection task but ones that support
the whole data-mining process. In addition it abandons the so called black-box
approach to algorithm description followed in meta-learning. Now in addition to
the datasets, algorithms also have descriptors, workflows as well. For the
latter two these descriptions are semantic, describing properties of the
algorithms. With the availability of descriptors both for datasets and data
mining workflows the traditional modelling techniques followed in
meta-learning, typically based on classification and regression algorithms, are
no longer appropriate. Instead we are faced with a problem the nature of which
is much more similar to the problems that appear in recommendation systems. The
most important meta-mining requirements are that suggestions should use only
datasets and workflows descriptors and the cold-start problem, e.g. providing
workflow suggestions for new datasets.
In this paper we take a different view on the meta-mining modelling problem
and treat it as a recommender problem. In order to account for the meta-mining
specificities we derive a novel metric-based-learning recommender approach. Our
method learns two homogeneous metrics, one in the dataset and one in the
workflow space, and a heterogeneous one in the dataset-workflow space. All
learned metrics reflect similarities established from the dataset-workflow
preference matrix. We demonstrate our method on meta-mining over biological
(microarray datasets) problems. The application of our method is not limited to
the meta-mining problem, its formulations is general enough so that it can be
applied on problems with similar requirements
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