Data quality and data alignment in E-business

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A Framework of Implementation of Collaborative Product Service in Virtual Enterprise

To satisfy new market requirements, manufacturing industry has shifted from mass production that takes advantage of the scale of production, to quality management that optimizes the internal enterprise functions, to e-manufacturing era that leverage intellectual capital via collaborative innovation. In the same time, the product itself is becoming the most important asset for sustainable business success. Consequently, the effectiveness, efficiency and innovation for the development of the product across the whole product lifecycle are becoming key business factors for manufacturing enterprise to obtain competitive advantages for survival. To tackle such challenges, a new business model called collaborative product services in virtual enterprise is proposed in this paper. The architecture of this new model is developed based on the framework and the application of web service and process management for collaboration product service in virtual enterprise. Indeed, it is hoped that this architecture will lay the foundation for further research and development of effective product lifecycle management in virtually collaborative enterprise environment.Singapore-MIT Alliance (SMA

The integration of lessons learned knowledge in Building Information Modelling (BIM)

Lessons learned systems are vital means for integrating construction knowledge into the various phases of the construction project life cycle. Many such systems are tailored towards the owner organisation’s specific needs and workflows to overcome challenges with information collection, documentation and retrieval. Previous works have relied on the development of conventional local and network/cloud-based database management systems to store and retrieve lessons gathered on projects. These lessons learned systems operate independently and have not been developed to take full advantage of the benefits of integration with emerging building information modelling (BIM) technology. As such construction professionals are faced with the shortcomings of the lack in efficient and speedy retrieval of context-focused information on lessons learned for appropriate utilization in projects. To tackle this challenge, we propose the integration of lesson learned knowledge management in BIM in addition to existing 2D-8D modelling of project information. The integration was implemented through the embedding of non –structured query system, NoSQL (MongoDB), in a BIM enabled environment to host lessons learned information linked to model items and 4D modelling project tasks of the digitised model. This is beyond existing conventional text-based queries and is novel. The system is implemented in .NET Frameworks and interfaced with a project management BIM tool, Navisworks Manage. The demonstration with a test case of a federated model from a pre-design school project suggests that lessons learned systems can become an integral part of BIM environments and contribute to enhancing knowledge reuse in projects

Industrial Artificial Intelligence in Industry 4.0 - Systematic Review, Challenges and Outlook

UIDB/00066/2020The advent of the Industry 4.0 initiative has made it so that manufacturing environments are becoming more and more dynamic, connected but also inherently more complex, with additional inter-dependencies, uncertainties and large volumes of data being generated. Recent advances in Industrial Artificial Intelligence have showcased the potential of this technology to assist manufacturers in tackling the challenges associated with this digital transformation of Cyber-Physical Systems, through its data-driven predictive analytics and capacity to assist decision-making in highly complex, non-linear and often multistage environments. However, the industrial adoption of such solutions is still relatively low beyond the experimental pilot stage, as real environments provide unique and difficult challenges for which organizations are still unprepared. The aim of this paper is thus two-fold. First, a systematic review of current Industrial Artificial Intelligence literature is presented, focusing on its application in real manufacturing environments to identify the main enabling technologies and core design principles. Then, a set of key challenges and opportunities to be addressed by future research efforts are formulated along with a conceptual framework to bridge the gap between research in this field and the manufacturing industry, with the goal of promoting industrial adoption through a successful transition towards a digitized and data-driven company-wide culture. This paper is among the first to provide a clear definition and holistic view of Industrial Artificial Intelligence in the Industry 4.0 landscape, identifying and analysing its fundamental building blocks and ongoing trends. Its findings are expected to assist and empower researchers and manufacturers alike to better understand the requirements and steps necessary for a successful transition into Industry 4.0 supported by AI, as well as the challenges that may arise during this process.publishersversionepub_ahead_of_prin

IAO-Intel: An Ontology of Information Artifacts in the Intelligence Domain

We describe on-going work on IAO-Intel, an information artifact ontology developed as part of a suite of ontologies designed to support the needs of the US Army intelligence community within the framework of the Distributed Common Ground System (DCGS-A). IAO-Intel provides a controlled, structured vocabulary for the consistent formulation of metadata about documents, images, emails and other carriers of information. It will provide a resource for uniform explication of the terms used in multiple existing military dictionaries, thesauri and metadata registries, thereby enhancing the degree to which the content formulated with their aid will be available to computational reasoning

Data Spaces

This open access book aims to educate data space designers to understand what is required to create a successful data space. It explores cutting-edge theory, technologies, methodologies, and best practices for data spaces for both industrial and personal data and provides the reader with a basis for understanding the design, deployment, and future directions of data spaces. The book captures the early lessons and experience in creating data spaces. It arranges these contributions into three parts covering design, deployment, and future directions respectively. The first part explores the design space of data spaces. The single chapters detail the organisational design for data spaces, data platforms, data governance federated learning, personal data sharing, data marketplaces, and hybrid artificial intelligence for data spaces. The second part describes the use of data spaces within real-world deployments. Its chapters are co-authored with industry experts and include case studies of data spaces in sectors including industry 4.0, food safety, FinTech, health care, and energy. The third and final part details future directions for data spaces, including challenges and opportunities for common European data spaces and privacy-preserving techniques for trustworthy data sharing. The book is of interest to two primary audiences: first, researchers interested in data management and data sharing, and second, practitioners and industry experts engaged in data-driven systems where the sharing and exchange of data within an ecosystem are critical