An Ontology and Multi-Agent Based Decision Support Framework for Prefabricated Component Supply Chain

Du, Juan; Jing, Hengqing; Choo, Kim‐Kwang Raymond; Sugumaran, Vijayan; Castro‐Lacouture, Daniel

doi:10.1007/s10796-019-09941-x

Cited by 29 publications

(11 citation statements)

References 31 publications

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“…To pursue the rationality of educational decision-making and promote educational decision-making from experience to science, scholars at home and abroad have done a lot of research and discussion on educational decision support from different perspectives. Du et al started the theoretical research in this area by proposing a data mining-based decision support system for educational management [ 12 ]. Shen et al used the B/S model and analyzed the process dynamics of learners in the Moodle learning platform using network analysis techniques.…”

Section: Related Workmentioning

confidence: 99%

Model Construction and Research on Decision Support System for Education Management Based on Data Mining

Wang

2021

Computational Intelligence and Neuroscience

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Based on data mining technology, this paper applies a combination of theoretical and practical approaches to systematically describe the background and basic concepts related to the generation of data mining-related technologies. The classical data mining process is analyzed in depth and in detail, and the method of building a decision support system for education management based on the B/S model is studied. Not only are the data mining techniques applied to this system, but also the decision tree model with the improved ID3 algorithm is implemented in this thesis, which is further applied to the educational management decision support system of this topic. The load of the client computer is reduced, and the client computer only needs to run a small part of the program. This paper focuses on the following aspects: the overall planning of the educational management decision support system based on data mining technology. From the actual educational management work, we analyze the requirements and design each functional module of this system in detail, applying the system functional structure diagram and functional use case diagram to represent the functional structure of the system and using flow charts to illustrate the workflow of the system as a whole and in parts. The logical structure design, entity-relationship design, and physical model design of the database have been carried out. To improve the efficiency of the system, the ID3 algorithm was improved on this basis to reduce the time complexity of its operation, improve the efficiency of the system operation, and achieve the goal of assessing and predicting the teaching quality of teachers. The development and design of this system provide an efficient, convenient, scientific, and reliable system tool to reduce the workload of education administrators and, more importantly, to make reasonable and effective use of the large amount of data generated in the management, and data mining techniques are used to extract valuable and potential information from these data, which can be more scientific and efficient for the teaching of teachers and students. It can provide reliable, referenceable, and valuable information for managers to make assessments and decisions.

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Section: Related Workmentioning

confidence: 99%

Model Construction and Research on Decision Support System for Education Management Based on Data Mining

Wang

2021

Computational Intelligence and Neuroscience

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“…This system helps manufacturing engineers to determine appropriate processes to design products with a competitive matching between features, material characteristics, and process capabilities. In [21], the authors proposed a decision support framework for the prefabricated component supply chain. This framework uses ontologies for providing unified support for the simulation process as well as for integrating heterogeneous data sources.…”

Section: Related Workmentioning

confidence: 99%

“…On the other hand, despite all works analyzed make use of ontologies as the main mechanism to represent expert's knowledge, they differ in the techniques used for developing the rule-based inference engine. Research efforts such as the presented in [17,19,21,27] implemented rule-based inference engines that rely on Jena, a Java-based application framework for developing semantic web applications. Jena provides development tools and predefined reasoners including an RDFS (Resource Description Framework Schema) reasoner, and OWL-Lite reasoner, and a generic rule reasoner that supports user-defined rules written in the Jena rules language.…”

Section: Related Workmentioning

confidence: 99%

AgriEnt: A Knowledge-Based Web Platform for Managing Insect Pests of Field Crops

et al. 2020

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In the agricultural context, there is a great diversity of insects and diseases that affect crops. Moreover, the amount of data available on data sources such as the Web regarding these topics increase every day. This fact can represent a problem when farmers want to make decisions based on this large and dynamic amount of information. This work presents AgriEnt, a knowledge-based Web platform focused on supporting farmers in the decision-making process concerning crop insect pest diagnosis and management. AgriEnt relies on a layered functional architecture comprising four layers: the data layer, the semantic layer, the web services layer, and the presentation layer. This platform takes advantage of ontologies to formally and explicitly describe agricultural entomology experts’ knowledge and to perform insect pest diagnosis. Finally, to validate the AgriEnt platform, we describe a case study on diagnosing the insect pest affecting a crop. The results show that AgriEnt, through the use of the ontology, has proven to produce similar answers as the professional advice given by the entomology experts involved in the evaluation process. Therefore, this platform can guide farmers to make better decisions concerning crop insect pest diagnosis and management.

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“…External data source to facilitate easier production simulation start: These papers pursue the integration of different external data sources to create a uniform simulation data model through an ontology. It is noticeable for this type of concept that multiple ontologies are used for modeling [42][43][44]. The simulation models are either converted into executable models, as in Silver et al [42], or the ontology serves as a knowledge database adjunct to the simulation model [43,44].…”

mentioning

confidence: 99%

“…It is noticeable for this type of concept that multiple ontologies are used for modeling [42][43][44]. The simulation models are either converted into executable models, as in Silver et al [42], or the ontology serves as a knowledge database adjunct to the simulation model [43,44]. Benjamin et al [43] introduce an ontology-driven framework based on scheduling, simulation and optimization ontology.…”

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confidence: 99%

Ontology-Based Production Simulation with OntologySim

et al. 2022

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Imagine the possibility to save a simulation at any time, modify or analyze it, and restart again with exactly the same state. The conceptualization and its concrete manifestation in the implementation OntologySim is demonstrated in this paper. The presented approach of a fully ontology-based simulation can solve current challenges in modeling and simulation in production science. Due to the individualization and customization of products and the resulting increase in complexity of production, a need for flexibly adaptable simulations arises. This need is exemplified in the trend towards Digital Twins and Digital Shadows. Their application to production systems, against the background of an ever increasing speed of change in such systems, is arduous. Moreover, missing understandability and human interpretability of current approaches hinders successful, goal oriented applications. The OntologySim can help solving this challenge by providing the ability to generate truly cyber physical systems, both interlocked with reality and providing a simulation framework. In a nutshell, this paper presents a discrete-event-based open-source simulation using multi-agency and ontology.

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An Ontology and Multi-Agent Based Decision Support Framework for Prefabricated Component Supply Chain

Cited by 29 publications

References 31 publications

Model Construction and Research on Decision Support System for Education Management Based on Data Mining

Model Construction and Research on Decision Support System for Education Management Based on Data Mining

AgriEnt: A Knowledge-Based Web Platform for Managing Insect Pests of Field Crops

Ontology-Based Production Simulation with OntologySim

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