Collaborative design in the era of cloud computing

Andreadis, Georgios; Fourtounis, Georgios; Bouzakis, K.‐D.

doi:10.1016/j.advengsoft.2014.11.002

Cited by 37 publications

(16 citation statements)

References 4 publications

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“…provenance, security, governance, etc. ; -The inclusion of various data sources-beside databases and data lakes, web data currently represents a fundamental source of information; for instance, social media aware manufacturing is a latest trend that aims at making product manufacturing a collaborative and collective process [47]; -The use of ontologies-to structure knowledge elicited from data, make it reusable and automatise the data mining process; -Big data challenges.…”

Section: Discussionmentioning

confidence: 99%

Evolution Paths for Knowledge Discovery and Data Mining Process Models

Rotondo

Quilligan

2020

SN COMPUT. SCI.

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Despite the hype around data analytics, the success rate of analytics initiatives remains very low and the value of data in organisations is left hidden. Various research studies show that the main barriers to analytics adoption are organisational and the lack of structured approaches on how to conduct analytics initiatives is a possible cause of analytics project failures. Data mining process models then become fundamental means to support analytics project management and minimise the risk of data dredging. In this paper, Knowledge Discovery and Data Mining process models are reviewed starting from the most popular models currently in use. Four distinctive research paths for data mining process models have emerged. These evolution paths seem to address limitations of the CRISP-DM model which remains the de facto standard in industry. The research streams identified include the evolution of the human role; the relevance of iteration and interactions; the role of data and knowledge repositories; and the integration of software engineering/agile methodologies. In the future, these four research streams should be combined to support the development of more encompassing process models.

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Section: Discussionmentioning

confidence: 99%

Evolution Paths for Knowledge Discovery and Data Mining Process Models

Rotondo

Quilligan

2020

SN COMPUT. SCI.

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“…Wang, X. et al [47] Development of a cloud based manufacturing system to support ubiquitous manufacturing which provides a service pool that maintains physical facilities in terms of manufacturing services. Andreadis, G. et al [48] Examination of the role of collaborative design in manufacturing in the era of cloud computing and proposal of a specific architecture with different servers for the implementation of a collaborative cloud based design system. Wu, D. et al [49] Study on applications of cloud computing paradigm in product design & manufacturing and exploration of the potential of utilizing cloud computing for selected aspects of collaborative design, distributed manufacturing, collective innovation and virtualization.…”

Section: Manufacturing On Cloudmentioning

confidence: 99%

Implementation of Machining on the Cloud: A Case Study in PLM Environment

Bhatt¹,

Segonds²,

Maranzana³

et al. 2016

IFIP Advances in Information and Communication Technology

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“…Cloud-based design and manufacturing transforms the design, simulation, computing and manufacturing resources and abilities into design and manufacturing services to form a design and manufacturing cloud, and distributes them to the user on demand (Li et al 2010;Andreadis et al 2015). The services encapsulation and virtualization are the key technology of cloud-based models .…”

Section: Introductionmentioning

confidence: 98%

Fuzzy holons for intelligent multi-scale design in cloud-based design for configurations

et al. 2015

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Cloud-based design for configurations can be referred to as a service-oriented networked design for configurations model. However, cloud-based models also pose challenges such as reliability, availability, capability, ability, adaptability of resources, and services across spatial boundaries. Multi-scale design can presumably stimulate greater intelligence in cloud-based models. Using the concepts of the fuzzy holon and the fuzzy attractor, this paper proposes the fuzzy holonic approach to address multi-scale design for configurations. A fuzzy design holon is defined through two basic holons: fuzzy function holon and fuzzy solution holon. A fuzzy attractor is defined as a fuzzy function holon or fuzzy function solution toward which a design tends to evolve. The proposed fuzzy holon model is driven by two conflicting drives: (a) completeness of fuzzy function holons and fuzzy solution holons, and (b) discrimination of fuzzy function holons and fuzzy solution holons. Through simulations, four possible states of behavior of fuzzy holon design are found: (a) the impossibility state characterized by the impossibility of fuzzy holon creation; (b) the creation and destruction state sometimes characterized by the creation of fuzzy holons and sometimes the destruction of fuzzy holons, (c) the development state characterized by a natural creation and development of fuzzy holons and (d) the failure state characterized by the interruption of the development of the fuzzy design holon and the destruction of already created fuzzy design holon. The model explains that design is not an orderly and well behaved phenomenon. It shows that fuzzy holon design is a discontinuous phenomenon.Keywords Intelligent design · Cloud-based design and manufacturing · Cloud-based design for configuration · Multi scale design · Fuzzy holon design

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Collaborative design in the era of cloud computing

Cited by 37 publications

References 4 publications

Evolution Paths for Knowledge Discovery and Data Mining Process Models

Evolution Paths for Knowledge Discovery and Data Mining Process Models

Implementation of Machining on the Cloud: A Case Study in PLM Environment

Fuzzy holons for intelligent multi-scale design in cloud-based design for configurations

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