Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Accelerating the Backpropagation Algorithm by Using NMF-Based Method on Deep Neural Networks Read chapter Read first chapter

2021 | OriginalPaper | Chapter

Description Framework for Stakeholder-Centric Value Chain of Data to Understand Data Exchange Ecosystem

Authors : Teruaki Hayashi, Gensei Ishimura, Yukio Ohsawa

Published in: Knowledge Management and Acquisition for Intelligent Systems

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

In recent years, the expectation that new businesses and economic value can be created by combining/exchanging data from different fields has risen. However, value creation by data exchange involves not only data, but also technologies and a variety of stakeholders that are integrated and in competition with one another. This makes the data exchange ecosystem a challenging subject to study. In this paper, we propose a framework for describing the stakeholder-centric value chain (SVC) of data by focusing on the relationships among stakeholders in data businesses and discussing creative ways to use them. The SVC framework enables the analysis and understanding of the structural characteristics of the data exchange ecosystem. We identified stakeholders who carry potential risk, those who play central roles in the ecosystem, and the distribution of profit among them using business models collected by the SVC.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Stabilizing the Predictive Performance for Ear Emergence in Rice Crops Across Cropping Regions
next chapter Attributed Heterogeneous Network Embedding for Link Prediction
Literature
1.
Balazinska, M., Howe, B., Suciu, D.: Data markets in the cloud: an opportunity for the database community. The VLDB Endowment 4(12), 1482–1485 (2011)CrossRef
2.
Liang, F., Yu, W., An, D., Yang, Q., Fu, X., Zhao, W.: A survey on big data market: pricing, trading and protection. IEEE Access 6, 15132–15154 (2018)CrossRef
3.
Stahl, F., Schomm, F., Vossen, G.: Data marketplaces: an emerging species. In: Frontiers in Artificial Intelligence and Applications, pp. 145–158 (2014)
4.
Spiekermann, M.: Data marketplaces: trends and monetisation of data goods. Intereconomics 54(4), 208–216 (2019)CrossRef
5.
Xu, L., Jiang, C., Wang, J., Yuan, J., Ren, Y.: Information security in big data: privacy and data mining. IEEE Access 2, 1149–1176 (2014)CrossRef
6.
Line, N., Dogru, T., El-Manstrly, D., Buoye, A., Malthouse, E., Kandampully, J.: Control, use and ownership of big data: a reciprocal view of customer big data value in the hospitality and tourism industry. Tourism Manage. 80, 104106 (2020)CrossRef
7.
Yoo, Y., Henfridsson, O., Lyytinen, K.: The new organizing logic of digital innovation: an agenda for information systems research. Inf. Syst. Res. 21(4), 724–735 (2010)CrossRef
8.
Ohsawa, Y., Kido, H., Hayashi, T., Liu, C., Komoda, K.: Innovators marketplace on data jackets, for valuating, sharing, and synthesizing data. In: Tweedale, J.W., Jain, L.C., Watada, J., Howlett, R.J. (eds.) Knowledge-Based Information Systems in Practice. SIST, vol. 30, pp. 83–97. Springer, Cham (2015). https://​doi.​org/​10.​1007/​978-3-319-13545-8_​6CrossRef
9.
Hayashi, T., Ohsawa, Y.: Retrieval system for data utilization knowledge integrating stakeholders’ interests. In: AAAI Spring Symposium (2018)
10.
Hayashi, T., Ohsawa, Y.: Context-based network analysis of structured knowledge for data utilization, In: AAAI Spring Symposium (2019)
11.
Hayashi, T., Ohsawa, Y.: Processing combinatorial thinking: Innovators marketplace as role-based game plus action planning. Int. J. Knowl. Syst. Sci. 4(3), 14–38 (2013)CrossRef
12.
Deloitte LLP, Open growth: Stimulating Demand for Open Data in the UK, Deloitte LLP (2012)
13.
Yoshizawa-Watanabe, S., Kunigami, M., Takahashi, S., Terano, T., Yoshikawa, A.: Pictogram network: evaluating english composition skills. In: IEEE International Professional Communication Conference, pp. 1–6 (2012)
14.
Barabási, A.L.: Network Science. Cambridge University Press (2016)
15.
Newman, M.E.J.: Assortative mixing in networks. Phys. Rev. Lett. 89(20), 208701 (2002)CrossRef
16.
Quix, C., Chakrabarti, A., Kleff, S., Pullmann, J.: Business process modelling for a data exchange platform. In: Proceedings of the 29th International Conference on Advanced Information Systems Engineering, pp. 153–160 (2017)
17.
Levchuk, G., Roberts, J., Freeman, J.: Learning and detecting patterns in multi-attributed network data. In: AAAI Fall Symposium Series (2012)
18.
Ito, H., Komatsu, T., Amagasa, T., Kitagawa, H.: Detecting communities and correlated attribute clusters on multi-attributed graphs. IEICE Trans. Inf. Syst. 102(4), 810–820 (2019)CrossRef
Metadata
Title
Description Framework for Stakeholder-Centric Value Chain of Data to Understand Data Exchange Ecosystem
Authors
Teruaki Hayashi
Gensei Ishimura
Yukio Ohsawa
Copyright Year
2021
Book
Knowledge Management and Acquisition for Intelligent Systems

Print ISBN: 978-3-030-69885-0

Electronic ISBN: 978-3-030-69886-7

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-3-030-69886-7_8

Premium Partner

    Image Credits