Construction and Implementation of Multi Collaborative Service Platform Based on Industrial Competitive Intelligence

With the economic globalization and the increasingly fierce industrial competition at home and abroad, the importance of industrial competitive intelligence service is becoming increasingly prominent. Under the policy background of cooperation and sharing, pluralistic coordination has become a new trend in regional economic development. The multi collaborative online service platform of industrial competitive intelligence is jointly constructed by all service subjects. The platform is guided and promoted by the government. Colleges and universities provide support for industrial competitive intelligence theory and professionals, scientific research institutes provide talent and advanced technology support, industry associations are responsible for dynamic monitoring of industrial development, and profit-making institutions are responsible for supplementing industrial competitive intelligence achievements. All service subjects integrate and explore existing intelligence resources and services through the unified online industrial competitive intelligence sharing platform, so as to realize benign cooperation, collaborative management, resource integration, user integration and service integration among subjects, so as to realize multiple collaborative services of industrial competitive intelligence.

Multiple Collaborative Service Model and System Construction Based on Industrial Competitive Intelligence

This paper constructs a multiple collaborative service model of industrial competition intelligence with the main purpose of promoting the development of regional industries. The multiple service subjects include enterprises, governments, colleges and universities, scientific research institutes, industry associations and for-profit institutions. This article starts from the overall development of regional industrial economy, weighs the mutual relationship between the elements of the service model, and promotes multiple service subjects such as enterprises, governments, universities, research institutes, industry associations, and profit-making organizations to realize the collaborative service of resource intelligence, demand intelligence and data intelligence provides linkage intelligence service for the development and innovation of regional industries. This service model can improve the efficiency of industrial competitive intelligence services and the overall competitiveness of regional industries.

Competitive Intelligence Management in Knowledge Economy

This paper is to discuss management of competitive Intelligence （CI） from the viewpoint of knowledge management, in which the author creatively uses adapted SECI model to make explanation, and points out the different roles played by individual and organization in this process. At the end of this paper, some implications are given in respect with some fundamental problems with application of individual knowledge and organizational knowledge resulting from their idiosyncrasy, and orientation for CI management.

Bibliometrics-based analysis on competitive intelligence in China

By applying to the theories and methodologies on informetrics,the authors collected the statistical data of relevant published articles,and then made some analysis including the prolific authors community,the long tail distribution of authors,and the disciplinary distribution of the published articles.To conclude,some development trends and suggestions were put forward for the reference.

信息市场中垄断行为和不正当竞争行为的表象及其法律特征——兼析美国政府诉讼微软案

The paper analyzes the monopoly and illegal competition actions in the information market.Their phenomena and characteristics in law are described.

Design and Implementation of Office Management System in Small and Medium Sized Enterprises based on Data Mining Technology

This paper introduces data mining technology in enterprise competitive intelligence system; and then introduced theoretical foundation and main clustering method of cluster analysis. The article emphasis on the FCM algorithm and principle and described implementation steps, and proposed the improvement FCM algorithm based on K mean particle size; finally, realize the design and implementation of enterprise competitive intelligence analysis and mining service system, and the improved FCM algorithm is applied in the system.

Call for Papers Journal of Electronic Science and Technology Special Section on Energy-Efficient Technologies—Crowd Energy Applications

Since decades,the global electricity demand shows only one direction：a considerable constant increase every year.But the unlimited growth in energy consumption is discussed increasingly critical,not only primarily in terms of limitations but also in terms of more efficient,more intelligent,and more sustainable usage of energy.Energy-efficient technologies（EET）and renewable energy technologies are already in a competitive position in different markets,and they are also activelyembedded in scientific research. Meanwhile, enormous research on key points, such as smart home, smart cities, smart environments, and smart living in general, have been executed. Applying EET to improve human well-being and to reduce energy and resource consumption is on focus.

Identifying the Core Competitive Intelligence Based on Enterprise Strategic Factors

Competitive intelligence(CI)is a key factor in helping business leaders gain and maintain competitive advantages.The emergence of big data and Web 2.0 has created new opportunities and more challenges for enterprises to effectively obtain CI.This paper attempts to explore a CI identification method based on strategic factors(SF).By filtering process before CI collection,the core CI,closely related to critical success factors and crisis inducement factors,are identified reliably and efficiently.Based on knowledge element model and multiattribute fusion method,emphasis is placed on the construction of a criterion function by which the SF thesaurus in achieving CI objectives is established.The advantages of this method lie not only in the capability of mining the core CI from massive data,but also in the foundation of efficient CI storage and analysis.This paper is of significance to make a thorough inquiry on CI obtaining and fusing methods of CI system in era of big data.Experiment results verified the feasibility and validity of this study.