6 Citations (Scopus)

Abstract

Although having structural system models which determine system behaviours is critical to plan, control and manage many complex systems (e.g. manufacturing and production systems), we often do not have pre-defined structural system models. We need to perform reverse engineering which is to collect and mine observable system data in order to discover structural system models. This paper presents a reverse engineering algorithm that can be used to discover a causal system model which is one kind of structural system model and represents causal relations of system factors. In a causal relation, the presence of one system factor causes the presence of another system factor. The paper also shows the computational complexity of the algorithm. The paper presents the application and performance of the reverse engineering algorithms to data in two application fields.

Original languageEnglish (US)
Pages (from-to)1-17
Number of pages17
JournalInternational Journal of Production Research
DOIs
StateAccepted/In press - Jul 28 2016

Fingerprint

Reverse engineering
Large scale systems
Computational complexity
System model
Factors

Keywords

  • causal relations
  • data mining
  • manufacturing information systems
  • reverse engineering
  • structural system models

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering
  • Management Science and Operations Research
  • Strategy and Management

Cite this

@article{990cd69ea5bd43d48bae9957a0a14eda,
title = "A reverse engineering algorithm for mining a causal system model from system data",
abstract = "Although having structural system models which determine system behaviours is critical to plan, control and manage many complex systems (e.g. manufacturing and production systems), we often do not have pre-defined structural system models. We need to perform reverse engineering which is to collect and mine observable system data in order to discover structural system models. This paper presents a reverse engineering algorithm that can be used to discover a causal system model which is one kind of structural system model and represents causal relations of system factors. In a causal relation, the presence of one system factor causes the presence of another system factor. The paper also shows the computational complexity of the algorithm. The paper presents the application and performance of the reverse engineering algorithms to data in two application fields.",
keywords = "causal relations, data mining, manufacturing information systems, reverse engineering, structural system models",
author = "Nong Ye",
year = "2016",
month = "7",
day = "28",
doi = "10.1080/00207543.2016.1213913",
language = "English (US)",
pages = "1--17",
journal = "International Journal of Production Research",
issn = "0020-7543",
publisher = "Taylor and Francis Ltd.",

}

TY - JOUR

T1 - A reverse engineering algorithm for mining a causal system model from system data

AU - Ye, Nong

PY - 2016/7/28

Y1 - 2016/7/28

N2 - Although having structural system models which determine system behaviours is critical to plan, control and manage many complex systems (e.g. manufacturing and production systems), we often do not have pre-defined structural system models. We need to perform reverse engineering which is to collect and mine observable system data in order to discover structural system models. This paper presents a reverse engineering algorithm that can be used to discover a causal system model which is one kind of structural system model and represents causal relations of system factors. In a causal relation, the presence of one system factor causes the presence of another system factor. The paper also shows the computational complexity of the algorithm. The paper presents the application and performance of the reverse engineering algorithms to data in two application fields.

AB - Although having structural system models which determine system behaviours is critical to plan, control and manage many complex systems (e.g. manufacturing and production systems), we often do not have pre-defined structural system models. We need to perform reverse engineering which is to collect and mine observable system data in order to discover structural system models. This paper presents a reverse engineering algorithm that can be used to discover a causal system model which is one kind of structural system model and represents causal relations of system factors. In a causal relation, the presence of one system factor causes the presence of another system factor. The paper also shows the computational complexity of the algorithm. The paper presents the application and performance of the reverse engineering algorithms to data in two application fields.

KW - causal relations

KW - data mining

KW - manufacturing information systems

KW - reverse engineering

KW - structural system models

UR - http://www.scopus.com/inward/record.url?scp=84980022885&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84980022885&partnerID=8YFLogxK

U2 - 10.1080/00207543.2016.1213913

DO - 10.1080/00207543.2016.1213913

M3 - Article

AN - SCOPUS:84980022885

SP - 1

EP - 17

JO - International Journal of Production Research

JF - International Journal of Production Research

SN - 0020-7543

ER -