NDLI: Toward big data value engineering for innovation

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Toward big data value engineering for innovation

Content Provider	ACM Digital Library
Author	Gonzalez, Eloy Kazman, Rick Garbajosa, Juan Chen, Hong-Mei
Abstract	This article articulates the requirements for an effective big data value engineering method. It then presents a value discovery method, called Eco-ARCH (Eco-ARCHitecture), tightly integrated with the BDD (Big Data Design) method for addressing these requirements, filling a methodological void. Eco-ARCH promotes a fundamental shift in design thinking for big data system design -- from "bounded rationality" for problem solving to "expandable rationality" for design for innovation. The Eco-ARCH approach is most suitable for big data value engineering when system boundaries are fluid, requirements are ill-defined, many stakeholders are unknown and design goals are not provided, where no architecture pre-exists, where system behavior is non-deterministic and continuously evolving, and where co-creation with consumers and prosumers is essential to achieving innovation goals. The method was augmented and empirically validated in collaboration with an IT service company in the energy industry to generate a new business model that we call "eBay in the Grid".
Starting Page	44
Ending Page	50
Page Count	7
File Format	PDF
ISBN	9781450341523
DOI	10.1145/2896825.2896837
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-05-14
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Architecture landscape Innovation Value discovery Ecosystem Value engineering Big data Energy industry
Content Type	Text
Resource Type	Article

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