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With the advances in information technology, universities and companies aiming to ascertain actual societal situations are deploying computers to analyze data, and several analysis methods have been proposed. To further such analysis, data mining technology that uses accumulated data to extract meaningful information is required. In organizational management, making decisions based on data and examining the organizational structure and management greatly contributes to business growth and development.
For management systems, such as Information Security Management Systems (ISMS) (International Organization for Standardization, 2013) and IT Service Management Systems (ITSMS) (International Organization for Standardization, 2011; International Organization for Standardization, 2018), to function successfully an overview of the current state of the organization’s activities and their surrounding environment is required. To achieve the organization’s objective, management systems must address the entire organization and provide continual improvements. To obtain management system certification, the organic connection with the actual business is an issue, and to operate effectively, it is necessary to grasp the actual state of the relationship between business and management system operations. To realize effective management operations, organizations need to adopt various forms of improvement in addition to operational adjustments and continual improvements, such as breakthrough changes, innovation, and reorganization.
Fuzzy theory has opened the way to dealing with ambiguous information quantitatively. It is necessary to obtain useful information by analyzing factors including ambiguity in a variety of fields. We have been researching analysis methods using fuzzy theory, which enables imprecise expressions (Yoshizumi et al., 2017; Yoshizumi et al., 2019). Here data relevance has been defined by fuzzy membership functions. The relevance is constructed as a database, and its usefulness is investigated. The goal is to realize useful extraction, visualization, and analysis methods that are easy to understand and can be applied to individual relationships under imprecise information conditions. Intelligible representation of results based on extracted organization management features helps analyze such results. Fuzzy graphs can tackle ambiguity quantitatively and can express variable relationships. Their characteristics also hinder its ability to represent and draw fuzzy graphs comprehensively. In light of various studies of graph drawing (Sugiyama, 1993; Sugiyama, 2002), we have proposed an algorithm (Shiono et al., 2012) based on cluster analysis (Miyamoto, 1999; Shinkai, 2008; Uesu, 2006) using a partition tree to draw intelligible and comprehensive fuzzy graphs. This algorithm can be applied to visualize fuzzy databases and can be a useful analysis tool.
In management system operations, it is important to analyze and understand how events and records relate to the management process and the overall structure including business processes. Fuzzy databases can quantify imprecise relationships and represent the entire structure step by step and locally. Therefore, we construct a fuzzy database that facilitates understanding and analyzing actual situations in management system operations. This paper introduces the database realization method and presents case studies. In the analysis, we use fuzzy graphs to visualize and grasp the overall picture and characteristics of management system operation.