Process mining technique for automated simulation model generation using activity log data

Ahn, Sanghyung, Dunston, Phillip S., Kandil, Amr and Martinez, Julio C. (2015). Process mining technique for automated simulation model generation using activity log data. In: William J. O'Brien and Simone Ponticelli, Proceedings of the 2015 International Workshop on Computing in Civil Engineering. 2015 International Workshop on Computing in Civil Engineering, Austin, TX, United States, (636-643). 21-23 June 2015. doi:10.1061/9780784479247.079


Author Ahn, Sanghyung
Dunston, Phillip S.
Kandil, Amr
Martinez, Julio C.
Title of paper Process mining technique for automated simulation model generation using activity log data
Conference name 2015 International Workshop on Computing in Civil Engineering
Conference location Austin, TX, United States
Conference dates 21-23 June 2015
Proceedings title Proceedings of the 2015 International Workshop on Computing in Civil Engineering
Journal name Computing in Civil Engineering
Series Congress on Computing in Civil Engineering, Proceedings
Place of Publication Reston, VA, United States
Publisher American Society of Civil Engineers
Publication Year 2015
Sub-type Fully published paper
DOI 10.1061/9780784479247.079
ISBN 9780784479247
Editor William J. O'Brien
Simone Ponticelli
Volume 2015-January
Issue January
Start page 636
End page 643
Total pages 8
Collection year 2016
Language eng
Abstract/Summary Generating the structure of a simulation network is one of the most important steps in the process of modeling construction operations using discrete-event simulation (DES). It is, however, a complicated and time-consuming task that requires extensive expert knowledge and data pre-processing, which are needed to establish plausible assumptions to build the network. Often such assumptions fail to capture reality, producing a large discrepancy between the generated simulation model and the underlying actual operation, due to an absence of data or incomplete knowledge of the modeler or subject matter expert (SME). As an alternative solution, this paper proposes an approach to learning the simulation model structure from data. We introduce techniques for discovering workflow models from activity log data, which are time-ordered records of all the activities performed by various types of machines during a given construction operation. The latest advancements in data collection and processing techniques such as activity recognition algorithms have made it possible to harvest activity logs based on sensor-based time series data collected from construction equipment. Since activities are fully ordered and recorded sequentially, these activity logs can be used to construct a process specification which adequately models activity cycle diagram (ACD). We introduce a refined α-algorithm to extract a process model from such log data and represent it in terms of an ACDbased DES model. This paper demonstrates the proposed method in the context of earthmoving operations and shows that it can successfully mine the workflow process of the earthmoving operations represented by an ACD.
Q-Index Code E1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Conference Paper
Collections: School of Civil Engineering Publications
Official 2016 Collection
 
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