Research on the Application of Big Data Analysis Platform in the Enterprise Management Optimization

Abstract

As companies attempt to make analysis an important data support component of daily decision-making, big data analysis technology is rapidly expanding across all industries. Although there are many software tools and libraries available to assist analysts and software engineers in developing solutions, enterprises are looking for reliable analysis platforms that can meet their specific goals and requirements. In order to minimize costs, such platforms also need to coexist with existing IT infrastructure and reuse the knowledge and resources already accumulated within the organization. To meet these requirements, this article proposes the Data Analysis Solution Engineering (DASE) framework - a knowledge driven approach supported by semantic web technology, for the design and development of requirements engineering and new data analysis platforms. It includes capturing data analysis platform requirements through a knowledge base, and enterprises learning how to use this data analysis platform to analyze all daily production data involved in the engineering data analysis platform. This article analyzes the DASE framework through knowledge modeling, requirement modeling, data architecture modeling, and platform design modeling, and demonstrates how it promotes knowledge and requirement driven data analysis platform engineering. The resulting data analysis platform is considered user-friendly, easy to maintain, and flexible in handling changes in requirements. This work contributes to the knowledge system of knowledge driven requirements engineering and data analysis platform engineering by providing customized models and reference architectures for different analytical application fields.