面向大数据的情报分析框架

Big-Data-Oriented Intelligence Analysis Framework

本文首先分析了大数据时代情报学面临的挑战和机遇,整理了目前国内外情报学研究的现状和未来发展趋势;分析了大数据时代情报工作面临的挑战,梳理了面向大数据的情报学相关理论和方法,并按照情报学一般工作流程进行了分类。其次根据大数据计算的特点以及相应的方法,从总体架构、功能及实现方法等方面构建了面向大数据的情报分析框架,并以一个基于Spark平台的关键词统计实验对框架的有效性进行了验证。最后,探讨了不同行业应用该框架的一些细则问题。本文构建的情报分析框架不仅可提供面向大数据的情报学理论和方法的基本查询,还可提供这些理论和方法支撑下的决策服务的调用。其构建细节则可为大数据环境下情报学理论和方法的实际应用提供参考。

This study analyzes the challenges and opportunities faced by intelligence studies in the era of big data and then organizes the status and future trends of current intelligence studies at home and abroad.It analyzes the challenges that information work faces in the big data era,arranges the big-data-oriented intelligence theories and methods,and sorts the theories and methods based on the general working process of intelligence studies.Next,according to the calculation characteristics and methods of big data,this study builds the big-data-oriented intelligence analysis framework from the overall architecture,functions,and implementation methods and verifies the framework’s validity by a keyword count experiment on the Spark platform.The study finally discusses some regulation problems of constructing the framework in diversified industries.The framework provides not only the basic query but also the decision service invocation driven by these theories and methods.The details required to build this framework could provide implementation guidance for the practical use of intelligence theories and methods in the big data environment.