Evolvable systems for big data management in business

McClatchey, Richard; Branson, Andrew; Shamdasani, Jetendr; Emin, Patrick

Abstract

Big Data systems are increasingly having to be longer lasting, enterprise-wide and interoperable with other (legacy or new) systems. Furthermore many organizations operate in an external environment which dictates change at an unforeseeable rate and requires evolution in system requirements. In these cases system development does not have a definitive end point, rather it continues in a mutually constitutive cycle with the organization and its requirements. Also when the period of design is of such duration that the technology may well evolve or when the required technology is not mature at the outset, then the design process becomes considerably more difficult. Not only that but if the system must inter-operate with other systems then the design process becomes considerably more difficult. Ideally in these circumstances the design must also be able to evolve in order to react to changing technologies and requirements and to ensure traceability between the design and the evolving system specification. For interoperability Big Data systems need to be discoverable and to work with information about other systems with which they need to cooperate over time. We have developed software called CRISTAL-ISE that enables dynamic system evolution and interoperability for Big Data systems; it has been commercialised as the Agilium-NG BPM product and is outlined in this paper.

Citation

McClatchey, R., Branson, A., Shamdasani, J., & Emin, P. (2017, July). Evolvable systems for big data management in business. Paper presented at Int Conf on High Performance Computng & Simulation ISGC2017, Genoa, Italy

• Presentation Conference Type: Conference Paper (unpublished)
• Conference Name: Int Conf on High Performance Computng & Simulation ISGC2017
• Conference Location: Genoa, Italy
• Start Date: Jul 17, 2017
• End Date: Jul 21, 2017
• Acceptance Date: Jul 21, 2017
• Online Publication Date: Sep 14, 2017
• Publication Date: Jul 21, 2017
• Deposit Date: Oct 20, 2017
• Publicly Available Date: Oct 20, 2017
• Peer Reviewed: Peer Reviewed
• ISBN: 9781538632499
• Keywords: description-driven systems, big data, object design, system evolution, traceability
• Public URL: https://uwe-repository.worktribe.com/output/884016
• Publisher URL: http://dx.doi.org/10.1109/HPCS.2017.14
• Additional Information: Additional Information : (c) 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.; Title of Conference or Conference Proceedings : International Conference on High Performance Computing & Simulation HPCS2017