Purpose




The purpose of this paper is to propose a cross-enterprises framework to achieve a higher level of sharing of knowledge and services in manufacturing ecosystems.




Design/methodology/approach




The authors describe the development of the emerging open manufacturing and discuss the model of knowledge creation processes of manufacturers. The authors present a decentralized framework based on blockchain and edge computing technologies, which consists of a customer layer, an enterprise layer, an application layer, an intelligence layer, a data layer, and an infrastructure layer. And a case study is provided to illustrate the effectiveness of the framework.




Findings




The authors discuss that the manufacturing ecosystem is changing from integrated and centralized systems to shared and distributed systems. The proposed framework incorporates the recent development in blockchain and edge computing that can meet the secure and distributed requirements for the sharing of knowledge and services in manufacturing ecosystems.




Practical implications




The proposed framework provides a more secure and controlled way to share knowledge and services, thereby supports the company to develop scalable and flexible business at a lower cost, and ultimately improves the overall quality, efficiency, and effectiveness of manufacturing services.




Originality/value




The proposed framework incorporates the recent development in edge computing technologies to achieve a flexible and distributed network. With the blockchain technology, it provides standards and protocols for implementing the framework and ensures the security issues. Not only information can be shared, but the framework also supports in the exchange of knowledge and services so that the parties can contribute their parts.

Toward open manufacturing: A cross-enterprises knowledge and services exchange framework based on blockchain and edge computing

Ontology, a model of knowledge representation and storage, has had extensive applications in pharmaceutics, social science, chemistry and biology. In the age of “big data”, the constructed concepts are often represented as higher-dimensional data by scholars, and thus the sparse learning techniques are introduced into ontology algorithms. In this paper, based on the alternating direction augmented Lagrangian method, we present an ontology optimization algorithm for ontological sparse vector learning, and a fast version of such ontology technologies. The optimal sparse vector is obtained by an iterative procedure, and the ontology function is then obtained from the sparse vector. Four simulation experiments show that our ontological sparse vector learning model has a higher precision ratio on plant ontology, humanoid robotics ontology, biology ontology and physics education ontology data for similarity measuring and ontology mapping applications.

Ontology Sparse Vector Learning Algorithm for Ontology Similarity Measuring and Ontology Mapping via ADAL Technology

With the rapid development of electronic business, Web services have attracted much attention in recent years. Enterprises can combine individual Web services to provide new value-added services. An emerging challenge is the timely discovery of close matches to service requests among large service pools. In this study, we first define a new semantic similarity measure combining functional similarity and process similarity. We then present a service discovery mechanism that utilises the new semantic similarity measure for service matching. All the published Web services are pre-grouped into functional clusters prior to the matching process. For a user’s service request, the discovery mechanism first identifies matching services clusters and then identifies the best matching Web services within these matching clusters. Experimental results show that the proposed semantic discovery mechanism performs better than a conventional lexical similarity-based mechanism.

Web service discovery among large service pools utilising semantic similarity and clustering

Purpose – The purpose of this paper is to identify information governance (IG) requirements in the context of dynamic business networking (BN). Design/methodology/approach – For the identification of IG requirements a systematic literature review is conducted. The practical significance of identified IG requirements is evaluated through a case study. Findings – A comprehensive list of IG requirements in dynamic BN is identified. These requirements are classified in information quality, information security, and metadata domains. The conducted case study demonstrates information exchange issues in a real world dynamic BN that reflects the practical significance of the identified IG requirements. Research limitations/implications – Exploiting emerging market opportunities through a dynamic BN necessitates the realization of a comprehensive IG program within the network. Otherwise, information exchange related risks can interrupt the operations in a BN. In this research the authors concentrate on interaction...

Information governance requirements in dynamic business networking

Enterprise architecture aligns business and information technology through the management of different elements and domains. An architecture description encompasses a wide and heterogeneous spectrum of areas, such as business processes, metrics, application components, people and technological infrastructure. Views express the elements and relationships of one or more domains from the perspective of specific system concerns relevant to one or more of its stakeholders. As a result, each view needs to be expressed in the description language that best suits its concerns. However, enterprise architecture languages tend to advocate a rigid "one-model fits all" approach where an all-encompassing description language describes several architectural domains. This approach hinders extensibility and adds complexity to the overall description language. On the other hand, integrating multiple models raises several challenges at the level of model coherence, consistency and trace ability. Moreover, EA models should be computable so that the effort involved in their analysis is manageable. This work advocates the employment of ontologies and associated techniques in EA for contributing to the solving of the aforementioned issues. Thus, a proposal is made comprising an extensible architecture that consists of a core domain-independent ontology that can be extended through the integration of domain-specific ontologies focusing on specific concerns. The proposal is demonstrated through a real-world evaluation scenario involving the analysis of the models according to the requirements of the scenario stakeholders.

http://www.ifs.tuwien.ac.at/~mayer/publications/pdf/ant_tear13.pdf

Using Ontologies for Enterprise Architecture Analysis

Based on an in-depth analysis of the existing approaches in applying semantic technologies to business process management (BPM) research in the perspective of cross-enterprise collaboration or so-called business-to-business integration, we analyse, discuss and compare methodologies, applications and best practices of the surveyed approaches with the proposed criteria. This article identifies various relevant research directions in semantic BPM (SBPM). Founded on the result of our investigation, we summarise the state of art of SBPM. We also address areas and directions for further research activities.

Chi P. Tran

Papers

Ontology-based approaches for cross-enterprise collaboration: a literature review on semantic business process management