DOCUMENTATION,INFORMATION & KNOWLEDGE ›› 2016, Vol. 0 ›› Issue (3): 4-128.doi: 10.13366/j.dik.2016.03.004

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Integration of Intelligence Engineering and Intelligence Parallelization: the Conception of Emergency Decision-making Intelligence Service in the Era of Big Data

  

  • Online:2016-05-10 Published:2016-05-10

Abstract:

Quick response intelligence service system is an important content of intelligence study at present stage. In order to meet intelligence needs and promote intelligence support ability for emergency decision-making in the big data era, the paper tries to explore new emergency decision-making intelligence support architecture: the mode that integrates intelligence engineering and intelligence parallelization in which the former plays a leading role and the latter plays a supporting role. Under this architecture, data resources, methods and tools, as well as expert wisdom should be in accordance with the intelligence engineering mode to complete the whole process of emergency intelligence work, and reach the goals of resources integration and coordination. At the same time, the essential elements of emergency intelligence are interconnected and be implemented by intelligent processing. The world of artificial and actual emergency intelligence will be interactive and complementary, and it can realize automatic deduction, situation and display, which can guide emergency decision and action. The mode that integrates intelligence engineering and intelligence parallelization can break the limitation of “domain” between intelligence sources and emergency decision-making subjects, for realizing intelligent emergency decision by intelligence guidance and realtime interaction. The paper provides a new idea for the construction of flexible, accurate and quick response emergency decisionmaking intelligence system in our country at present stage.

Keywords: Intelligence, Parallel intelligence, Intelligence engineering, Intelligence service, Emergency decision-making, Smart city, Intelligence system, Big data