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Facilitating Evolution during Design and Implementation

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Abstract

The volumes and complexity of data that companies need to handle are increasing at an accelerating rate. In order to compete effectively and ensure their commercial sustainability, it is becoming crucial for them to achieve robust traceability in both their data and the evolving designs of their systems. This is addressed by the CRISTAL software which was originally developed at CERN by UWE, Bristol, for one of the particle detectors at the Large Hadron Collider, which has been subsequently transferred into the commercial world. Companies have been able to demonstrate increased agility, generate additional revenue, and improve the efficiency and cost-effectiveness with which they develop and implement systems in various areas, including business process management (BPM), healthcare and accounting applications. CRISTAL’s ability to manage data and its semantic provenance at the terabyte scale, with full traceability over extended timescales, together with its description-driven approach, has provided the flexible adaptability required to future proof dynamically evolving software for these businesses.

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References

  1. McClatchey R, le Goff J-M (1998) Support for product data from design to production Z Kovacs. Computer integrated manufacturing systems. Elsevier Publishers. doi:10.1016/S0951-5240(98)00026-3

  2. McClatchey R et al (1998) A distributed workflow & product data management application for the construction of large scale scientific apparatus. NATO ASI series F: computer & systems sciences, Springer- Verlag. doi:10.1007/978-3-642-58908-9_2

  3. Chatrchyan S et al (2008) The CMS experiment at the CERN LHC. The CMS collaboration. J Instrum 3, Institute of Physics Publishing. doi:10.1088/1748-0221/3/08/S08004

  4. Estrella F et al (2003) Pattern reification as the basis for description-driven systems. J Softw Syst Model, Springer-Verlag. doi:10.1007/s10270-003-0023-0

  5. Branson A, McClatchey R, Le Goff J-M, Shamdasani J (2014) CRISTAL: a practical study in designing systems to cope with change information systems 42, Elsevier Publishers. doi:10.1016/j.is.2013.12.009

  6. McClatchey R et al (2013) Providing traceability for neuroimaging analyses. Int J Med Inform, Elsevier publishers. doi:10.1016/j.ijmedinf.2013.05.005

  7. Anjum A et al (2012) Intelligent grid enabled services for neuroimaging analysis. Neurocomputing 122, Elsevier Publishers. doi:10.1016/j.neucom.2013.01.042

  8. Shamdasani J et al (2014) CRISTAL-ISE: provenance applied in industry. In: Proceedings of the 16th international conference on enterprise information systems (ICEIS), vol 3, Lisbon. doi:10.5220/,SCITEPRESS

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Acknowledgments

The development of CRISTAL has been made possible by the support of CERN, CNRS and UWE and colleagues therefrom and in the context of projects supported by the European Commission.

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Correspondence to Richard McClatchey.

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McClatchey, R. Facilitating Evolution during Design and Implementation. Künstl Intell 29, 213–217 (2015). https://doi.org/10.1007/s13218-014-0324-1

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