Research on the Realizing Mechanism of an Industrialized PL-ISEE Database Platform

2012 ◽  
Vol 490-495 ◽  
pp. 2897-2901
Author(s):  
Jian Li Dong ◽  
Ning Guo Shi ◽  
Yan Yan Chen
Keyword(s):  
Data Model ◽  
Manufacturing Industry ◽  
Middle Part ◽  
Product Line ◽  
Management Service ◽  
Concept Model ◽  
Environment Model ◽  
Database Platform ◽  
Integrated Software ◽  
Database Views

Using the automation production mode of modern manufacturing industry for reference, a new industrialized PL-ISEE (product line based integrated software engineering environment) model was proposed by us in reference [8]. The middle part of the PL-ISEE architecture is data layer which is composed of the software components bus and core assets database platform to achieve the storage and managements of product line core assets data. In this paper, the architecture and its implementation mechanism of the PL-ISEE database platform is further researched. Whole platform architecture consists of core asset objects management service and assets database system. The data model and database schema as well as database views of the database platform are designed and created by using united product line concept model and data model. So, the database supporting platform’s functions and configuration also well satisfy the requirements of interfaces, tools and data integration as well as product line assembling production in the PL-ISEE. Therefore, the design and realizing ideas of the PL-ISEE database platform will be valuable to research product line engineering and methodology in future.

An Ideal Schema Design of an Industrialized PL-ISEE Database Platform

2012 ◽  
Vol 160 ◽  
pp. 341-345
Author(s):  
Jian Li Dong
Keyword(s):  
Manufacturing Industry ◽  
Database System ◽  
Middle Part ◽  
Product Line ◽  
Management Service ◽  
Organization Model ◽  
Concept Model ◽  
Platform Architecture ◽  
Schema Design ◽  
Database Platform

Using the automation production and management system of modern manufacturing industry product line for reference, a new industrialized PL-ISEE (product line based integrated software engineering environment) model was proposed by us in reference [4]. In the new model, the middle part in the PL-ISEE architecture is data layer which is composed of the software components bus and environment database platform to achieve the storage and managements of product line core assets data. In this paper, the environment database platform architecture and its schema design as well as implementation mechanism are further researched. Whole database platform architecture consists of the product line asset objects management service and core assets database system. The data model and database schema of the database system are designed and created by using a united core asset concept model and data organization model. The database supporting platform with the model and schema can well satisfy all the requirements of interfaces, tools and data integration as well as product line assembling production in the PL-ISEE. Therefore, the design and realizing ideas of the PL-ISEE database platform will be useful for researching on product line engineering and methodology in the future.

3SST Model

2010 ◽  
pp. 1-14
Author(s):  
Andreea Sabau
Keyword(s):  
Data Model ◽  
Conceptual Modeling ◽  
Domain Model ◽  
Relational Model ◽  
Temporal Data ◽  
Spatio Temporal ◽  
Entity Relationship ◽  
Temporal Objects ◽  
Database Platform ◽  
Event Based

In order to represent spatio-temporal data, many conceptual models have been designed and a part of them have been implemented. This chapter describes an approach of the conceptual modeling of spatio-temporal data, called 3SST. Also, the spatio-temporal conceptual and relational data models obtained by following the proposed phases are presented. The 3SST data model is obtained by following three steps: the construction of an entity-relationship spatio-temporal model, the specification of the domain model and the design of a class diagram which includes the objects characteristic to a spatiotemporal application and other needed elements. The relational model of the 3SST conceptual model is the implementation of the conceptual 3SST data model on a relational database platform. Both models are characterized by generality in representing spatial, temporal and spatio-temporal data. The spatial objects can be represented as points or objects with shape and the evolution of the spatio-temporal objects can be implemented as discrete or continuous in time, on time instants or time intervals. More than that, different types of spatial, temporal, spatio-temporal and event-based queries can be performed on represented data. Therefore, the proposed 3SST relational model can be considered the core of a spatio-temporal data model.

Study of Green Logistics Technology to Manufacturing Based on Green Supply Chain Theory

2014 ◽  
Vol 1073-1076 ◽  
pp. 2438-2442
Author(s):  
Xia Zou ◽  
Wei Zhao ◽  
Ting Ting Zhang
Keyword(s):  
Supply Chain ◽  
Manufacturing Industry ◽  
Distribution Center ◽  
Green Supply Chain ◽  
Three Dimension ◽  
Green Logistics ◽  
Logistics System ◽  
Concept Model ◽  
Chain Theory

Based on the theory of green supply chain, this paper constructed a two-layer and three-dimension green logistics concept model by the analysis of green logistics’ composition of the manufacturing industry, discussed the influence of technology environment on the green logistics system, and explored the implementation methods of green warehousing, sorting, packing and green handling for industry distribution center.

A Security Framework for Cloud Manufacturing

2014 ◽  
Author(s):  
Yazhe Wang ◽  
Shunan Ma ◽  
Lei Ren
Keyword(s):  
Data Protection ◽  
Manufacturing Industry ◽  
Cloud Manufacturing ◽  
Security Level ◽  
Management Service ◽  
Access Management ◽  
Security Framework ◽  
Security Issues ◽  
Security Service ◽  
Security As A Service

Cloud manufacturing has been considered as a promising new service-oriented manufacturing paradigm that can transform traditional industry. However security is one of the major issues which hamper the growth of cloud manufacturing industry. In this paper, we analyze the cloud manufacturing security issues and challenges, and propose a security framework for cloud manufacturing, which includes four levels: infrastructure security, identity and access management, data protection and security, and cloud security as a service. The Infrastructure security level can ensure an organization’s core IT infrastructure security at the network, host, and application levels; Identity and access management level can improve operational efficiency and to comply with privacy and data protection requirements; Data protection and security level can help users evaluate their data security scenarios and make informed judgments regarding risk for their organizations; security as a service level, which includes web security service, storage security service and IAM(Identity and Access Management) service, would extract security functions and capacities to assemble software as a service. The cloud manufacturing security framework we proposed can resolve the security issues and improve the security performance of cloud manufacturing industry.

scholarly journals Classification of botnet attacks in IoT smart factory using honeypot combined with machine learning

2021 ◽  
Vol 7 ◽  
pp. e350
Author(s):  
Seungjin Lee ◽  
Azween Abdullah ◽  
Nz Jhanjhi ◽  
Sh Kok
Keyword(s):  
Machine Learning ◽  
Manufacturing Industry ◽  
False Positive Rate ◽  
Denial Of Service ◽  
Smart Factory ◽  
Economic Losses ◽  
Fast Time ◽  
Management Service ◽  
Learning Program ◽  
Botnet Detection

The Industrial Revolution 4.0 began with the breakthrough technological advances in 5G, and artificial intelligence has innovatively transformed the manufacturing industry from digitalization and automation to the new era of smart factories. A smart factory can do not only more than just produce products in a digital and automatic system, but also is able to optimize the production on its own by integrating production with process management, service distribution, and customized product requirement. A big challenge to the smart factory is to ensure that its network security can counteract with any cyber attacks such as botnet and Distributed Denial of Service, They are recognized to cause serious interruption in production, and consequently economic losses for company producers. Among many security solutions, botnet detection using honeypot has shown to be effective in some investigation studies. It is a method of detecting botnet attackers by intentionally creating a resource within the network with the purpose of closely monitoring and acquiring botnet attacking behaviors. For the first time, a proposed model of botnet detection was experimented by combing honeypot with machine learning to classify botnet attacks. A mimicking smart factory environment was created on IoT device hardware configuration. Experimental results showed that the model performance gave a high accuracy of above 96%, with very fast time taken of just 0.1 ms and false positive rate at 0.24127 using random forest algorithm with Weka machine learning program. Hence, the honeypot combined machine learning model in this study was proved to be highly feasible to apply in the security network of smart factory to detect botnet attacks.

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