Nowadays, since there are so many big data available everywhere, those big data can be used to find useful information to improve design and operation by using various analysis methods such as data mining. Especially if we have event log data that has execution history data of an organization such as case_id, event_time, event (activity), performer, etc., then we can apply process mining to discover the main process model in the organization. Once we can find the main process from process mining, we can utilize it to improve current working environment. In this paper we developed a new method to find a final diagnosis of a patient, who needs several procedures (medical test and examination) to diagnose disease of the patient by using process mining approach. Some patients can be diagnosed by only one procedure, but there are certainly some patients who are very difficult to diagnose and need to take several procedures to find exact disease name. We used 2 million procedure log data and there are 397 thousands patients who took 2 and more procedures to find a final disease. These multi-procedure patients are not frequent case, but it is very critical to prevent wrong diagnosis. From those multi-procedure taken patients, 4 procedures were discovered to be a main process model in the hospital. Using this main process model, we can understand the sequence of procedures in the hospital and furthermore the relationship between diagnosis and corresponding procedures.

With the recent development of manufacturing technology and the diversification of consumer needs, not only the process and quality control of production have become more complicated but also the kinds of information that manufacturing facilities provide the user about process have been diversified. Therefore the importance of big data analysis also has been raised. However, most small and medium enterprises (SMEs) lack the systematic infrastructure of big data management and analysis. In particular, due to the nature of domestic manufacturing companies that rely on foreign manufacturers for most of their manufacturing facilities, the need for their own data analysis and manufacturing support applications is increasing and research has been conducted in Korea. This study proposes integrated analysis platform for process and quality analysis, considering manufacturing big data database (DB) and data characteristics. The platform is implemented in two versions, Web and C/S, to enhance accessibility which perform template based quality analysis and real-time monitoring. The user can upload data from their local PC or DB and run analysis by combining single analysis module in template in a way they want since the platform is not optimized for a particular manufacturing process. Also Java and R are used as the development language for ease of system supplementation. It is expected that the platform will be available at a low price and evolve the ability of quality analysis in SMEs.

Due to sudden transition to intellectual society corresponding with fast technology progress, companies and nations need to focus on development and guarantee of intellectual property. The possession of intellectual property has been the important factor of competition power. In this paper we developed the efficient patent search process with big data analysis tool R. This patent search process consists of 5 steps. We result that at first this process obtain the core patent search key words and search the target patents through search formula using the combination of above patent search key words.

In recent years, safety recalls have occurred frequently in the biopharmaceutical industry, which affects the health of consumers. This article attempts to use the fsQCA method to draw a conclusion through the study of ESG and its quantification system, as well as the study of data samples related to MES and GMP processes, that is, MES-DPT has a positive impact on process safety management, and GMP-DPT has a positive impact on process safety. Management has a positive and positive impact, and ESG-DPT has a positive and positive impact on process safety management. Finally, this article puts forward suggestions for improving ESG-DPT, MES-DPT, GMP-DPT and the biomedical ESG-DPT model. Future research hopes to further study ESG -DPT model and ESG biomedical industry indicators.

석산은 6개의 소화(小花)가 완전 모양을 갖추고 출현한 뒤 개화되어 폐화까지의 기간은 7일 정도 소요되었다. 1번 소화의 개화에서 폐화까지는 5일 정도 소요되었다. 소화가 개화하는 시간대는 주로 15:00 이후인 것으로 나타났으며, 09:00-13:00까지는 소화의 형태 변화는 거의 없었다. 석산 꽃의 개화 과정은 본 연구에서와 같이 꽃봉오리의 출현 이후 경과 일과 시간에 따라 일정한 형태 변화를 나타내었다.