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An Extension Collaborative Innovation Model in the Context of Big Data

Author

Listed:
  • Xingsen Li

    (School of Management, Ningbo Institute of Technology, Zhejiang University, Ningbo 315100, P. R. China)

  • Yingjie Tian

    (Research Center on Fictitious Economy and Data Science, CAS, Beijing 100190, P. R. China)

  • Florentin Smarandache

    (Mathematics and Science Department, University of New Mexico, Gallup, NM 87301, USA)

  • Rajan Alex

    (School of Engineering and Computer Science, West Texas A&M University, Conyon, TX 79015, USA)

Abstract

The processes of generating innovative solutions mostly rely on skilled experts who are usually unavailable and their outcomes have uncertainty. Computer science and information technology are changing the innovation environment and accumulating Big Data from which a lot of knowledge is to be discovered. However, it is a rather nebulous area and there still remain several challenging problems to integrate the multi-information and rough knowledge effectively to support the process of innovation. Based on the new cross disciplineExtenics, the authors have presented a collaborative innovation model in the context of Big Data. The model has two mutual paths, one to transform collected data into an information tree in a uniform basic-element format and another to discover knowledge by data mining, save the rules in a knowledge base, and then explore the innovation paths and solutions by a formularized model based onExtenics. Finally, all possible solutions are scored and selected by 3D-dependent function. The model which integrates different departments to put forward the innovation solutions is proved valuable for a user of the Big Data by a practical innovation case in management.

Suggested Citation

  • Xingsen Li & Yingjie Tian & Florentin Smarandache & Rajan Alex, 2015. "An Extension Collaborative Innovation Model in the Context of Big Data," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 14(01), pages 69-91.
    • Handle: RePEc:wsi:ijitdm:v:14:y:2015:i:01:n:s0219622014500266
    DOI: 10.1142/S0219622014500266
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    Cited by:

    1. Bahareh Rahmati & Mohammad Karim Sohrabi, 2019. "A Systematic Survey on High Utility Itemset Mining," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(04), pages 1113-1185, July.
    2. Andrea De Mauro & Marco Greco & Michele Grimaldi, 2019. "Understanding Big Data Through a Systematic Literature Review: The ITMI Model," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(04), pages 1433-1461, July.
    3. Xingsen Li & Haibin Pi & Junwen Sun & Hao Lan Zhang & Zhencheng Liang, 2023. "An Integration Model on Brainstorming and Extenics for Intelligent Innovation in Big Data Environment," International Journal of Data Warehousing and Mining (IJDWM), IGI Global, vol. 19(1), pages 1-23, January.

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