Next Article in Journal
On the Solutions of Second-Order Differential Equations with Polynomial Coefficients: Theory, Algorithm, Application
Next Article in Special Issue
Special Issue on Process Mining and Emerging Applications
Previous Article in Journal
A Boundary Distance-Based Symbolic Aggregate Approximation Method for Time Series Data
Previous Article in Special Issue
Translating Workflow Nets to Process Trees: An Algorithmic Approach

Efficient Time and Space Representation of Uncertain Event Data

Process and Data Science Group (PADS), Department of Computer Science, RWTH Aachen University, 52062 Aachen, Germany
Author to whom correspondence should be addressed.
Algorithms 2020, 13(11), 285;
Received: 30 September 2020 / Revised: 30 October 2020 / Accepted: 6 November 2020 / Published: 9 November 2020
(This article belongs to the Special Issue Process Mining and Emerging Applications)
Process mining is a discipline which concerns the analysis of execution data of operational processes, the extraction of models from event data, the measurement of the conformance between event data and normative models, and the enhancement of all aspects of processes. Most approaches assume that event data is accurately captured behavior. However, this is not realistic in many applications: data can contain uncertainty, generated from errors in recording, imprecise measurements, and other factors. Recently, new methods have been developed to analyze event data containing uncertainty; these techniques prominently rely on representing uncertain event data by means of graph-based models explicitly capturing uncertainty. In this paper, we introduce a new approach to efficiently calculate a graph representation of the behavior contained in an uncertain process trace. We present our novel algorithm, prove its asymptotic time complexity, and show experimental results that highlight order-of-magnitude performance improvements for the behavior graph construction. View Full-Text
Keywords: process mining; uncertain data; partial order process mining; uncertain data; partial order
Show Figures

Figure 1

MDPI and ACS Style

Pegoraro, M.; Uysal, M.S.; van der Aalst, W.M.P. Efficient Time and Space Representation of Uncertain Event Data. Algorithms 2020, 13, 285.

AMA Style

Pegoraro M, Uysal MS, van der Aalst WMP. Efficient Time and Space Representation of Uncertain Event Data. Algorithms. 2020; 13(11):285.

Chicago/Turabian Style

Pegoraro, Marco, Merih S. Uysal, and Wil M.P. van der Aalst 2020. "Efficient Time and Space Representation of Uncertain Event Data" Algorithms 13, no. 11: 285.

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Back to TopTop