Hajiheydari, N., Talafidaryani, M. and Khabiri, S. (2019) IoT big data value map : how to generate value from IoT data. In: ICSLT 2019: Proceedings of the 2019 the 5th International Conference on e-Society, e-Learning and e-Technologies. 5th International Conference on e-Society, e-Learning and e-Technologies, 10-12 Jan 2019, Vienna, Austria. Association for Computing Machinery (ACM) , pp. 98-103. ISBN 9781450362351
Abstract
Huge sources of business value are emerging due to big data generated by the Internet of Things (IoT) technologies paired with Machine Learning (ML) and Data Mining (DM) techniques' ability to harness and extract hidden knowledge from data and consequently learning and improving spontaneously. This paper reviews different examples of analyzing big data generated through IoT in previous studies and in various domains; then claims their business Value Proposition Map deploying Value Proposition Canvas as a novel conceptual tool. As a result, the proposed unprecedented framework of this paper entitled "IoT Big Data Value Map" shows a roadmap from raw data to real-world business value creation, blossomed out of a kind of three-pillar structure: IoT, Data Mining, and Value Proposition Map. The result of this study paves the way for prototyping business models in this field based on value invention from huge data analysis generated by IoT devices in different industries. Furthermore, researchers may complete this map by associating proposed framework with potential customers' profile and their expectations.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2019 Association for Computing Machinery. This is an author-produced version of a paper subsequently published in ICSLT 2019: Proceedings. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Internet of Things (IoT); Data Mining; Value Proposition Canvas; Value Proposition Map; IoT Big Data Value Map |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Social Sciences (Sheffield) > Management School (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 20 May 2020 11:18 |
Last Modified: | 20 May 2020 14:28 |
Status: | Published |
Publisher: | Association for Computing Machinery (ACM) |
Refereed: | Yes |
Identification Number: | 10.1145/3312714.3312728 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:161015 |