15,570 research outputs found
Measuring the Stability of Process Outcome Predictions in Online Settings
Predictive Process Monitoring aims to forecast the future progress of process
instances using historical event data. As predictive process monitoring is
increasingly applied in online settings to enable timely interventions,
evaluating the performance of the underlying models becomes crucial for
ensuring their consistency and reliability over time. This is especially
important in high risk business scenarios where incorrect predictions may have
severe consequences. However, predictive models are currently usually evaluated
using a single, aggregated value or a time-series visualization, which makes it
challenging to assess their performance and, specifically, their stability over
time. This paper proposes an evaluation framework for assessing the stability
of models for online predictive process monitoring. The framework introduces
four performance meta-measures: the frequency of significant performance drops,
the magnitude of such drops, the recovery rate, and the volatility of
performance. To validate this framework, we applied it to two artificial and
two real-world event logs. The results demonstrate that these meta-measures
facilitate the comparison and selection of predictive models for different
risk-taking scenarios. Such insights are of particular value to enhance
decision-making in dynamic business environments.Comment: 8 pages, 3 figures, Proceedings of the 5th International Conference
on Process Mining (ICPM 2023
Integrating E-Commerce and Data Mining: Architecture and Challenges
We show that the e-commerce domain can provide all the right ingredients for
successful data mining and claim that it is a killer domain for data mining. We
describe an integrated architecture, based on our expe-rience at Blue Martini
Software, for supporting this integration. The architecture can dramatically
reduce the pre-processing, cleaning, and data understanding effort often
documented to take 80% of the time in knowledge discovery projects. We
emphasize the need for data collection at the application server layer (not the
web server) in order to support logging of data and metadata that is essential
to the discovery process. We describe the data transformation bridges required
from the transaction processing systems and customer event streams (e.g.,
clickstreams) to the data warehouse. We detail the mining workbench, which
needs to provide multiple views of the data through reporting, data mining
algorithms, visualization, and OLAP. We con-clude with a set of challenges.Comment: KDD workshop: WebKDD 200
BINet: Multi-perspective Business Process Anomaly Classification
In this paper, we introduce BINet, a neural network architecture for
real-time multi-perspective anomaly detection in business process event logs.
BINet is designed to handle both the control flow and the data perspective of a
business process. Additionally, we propose a set of heuristics for setting the
threshold of an anomaly detection algorithm automatically. We demonstrate that
BINet can be used to detect anomalies in event logs not only on a case level
but also on event attribute level. Finally, we demonstrate that a simple set of
rules can be used to utilize the output of BINet for anomaly classification. We
compare BINet to eight other state-of-the-art anomaly detection algorithms and
evaluate their performance on an elaborate data corpus of 29 synthetic and 15
real-life event logs. BINet outperforms all other methods both on the synthetic
as well as on the real-life datasets
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
Log Skeletons: A Classification Approach to Process Discovery
To test the effectiveness of process discovery algorithms, a Process
Discovery Contest (PDC) has been set up. This PDC uses a classification
approach to measure this effectiveness: The better the discovered model can
classify whether or not a new trace conforms to the event log, the better the
discovery algorithm is supposed to be. Unfortunately, even the state-of-the-art
fully-automated discovery algorithms score poorly on this classification. Even
the best of these algorithms, the Inductive Miner, scored only 147 correct
classified traces out of 200 traces on the PDC of 2017. This paper introduces
the rule-based log skeleton model, which is closely related to the Declare
constraint model, together with a way to classify traces using this model. This
classification using log skeletons is shown to score better on the PDC of 2017
than state-of-the-art discovery algorithms: 194 out of 200. As a result, one
can argue that the fully-automated algorithm to construct (or: discover) a log
skeleton from an event log outperforms existing state-of-the-art
fully-automated discovery algorithms.Comment: 16 pages with 9 figures, followed by an appendix of 14 pages with 17
figure
Recursion Aware Modeling and Discovery For Hierarchical Software Event Log Analysis (Extended)
This extended paper presents 1) a novel hierarchy and recursion extension to
the process tree model; and 2) the first, recursion aware process model
discovery technique that leverages hierarchical information in event logs,
typically available for software systems. This technique allows us to analyze
the operational processes of software systems under real-life conditions at
multiple levels of granularity. The work can be positioned in-between reverse
engineering and process mining. An implementation of the proposed approach is
available as a ProM plugin. Experimental results based on real-life (software)
event logs demonstrate the feasibility and usefulness of the approach and show
the huge potential to speed up discovery by exploiting the available hierarchy.Comment: Extended version (14 pages total) of the paper Recursion Aware
Modeling and Discovery For Hierarchical Software Event Log Analysis. This
Technical Report version includes the guarantee proofs for the proposed
discovery algorithm
Discovering learning processes using inductive miner: A case study with learning management systems (LMSs)
Resumen tomado de la publicaciónDescubriendo procesos de aprendizaje aplicando Inductive Miner: un estudio de caso en Learning Management Systems (LMSs). Antecedentes: en la minerÃa de procesos con datos educativos se utilizan diferentes algoritmos para descubrir modelos, sobremanera el Alpha Miner, el Heuristic Miner y el Evolutionary Tree Miner. En este trabajo proponemos la implementación de un nuevo algoritmo en datos educativos, el denominado Inductive Miner. Método: hemos utilizado datos de interacción de 101 estudiantes universitarios en una asignatura de grado desarrollada en la plataforma Moodle 2.0. Una vez prepocesados se ha realizado la minerÃa de procesos sobre 21.629 eventos para descubrir los modelos que generan los diferentes algoritmos y comparar sus medidas de ajuste, precisión, simplicidad y generalización. Resultados: en las pruebas realizadas en nuestro conjunto de datos el algoritmo Inductive Miner es el que obtiene mejores resultados, especialmente para el valor de ajuste, criterio de mayor relevancia en lo que respecta al descubrimiento de modelos. Además, cuando ponderamos con pesos las diferentes métricas seguimos obteniendo la mejor medida general con el Inductive Miner. Conclusiones: la implementación de Inductive Miner en datos educativos es una nueva aplicación que, además de obtener mejores resultados que otros algoritmos con nuestro conjunto de datos, proporciona modelos válidos e interpretables en términos educativos.Universidad de Oviedo. Biblioteca de PsicologÃa; Plaza Feijoo, s/n.; 33003 Oviedo; Tel. +34985104146; Fax +34985104126; [email protected]
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