# An Efficient Analytical Approach to Visualize Text-Based Event Logs for Semiconductor Equipment
## 2 Background and Related Research  
Many logs made in semiconductor equipment must be managed well. The hard disk of the equipment controller has a limited capacity and cannot store files indefinitely. Therefore, it is essential to automatically compress [9] past logs and delete them after a certain period. If the EventLog is deleted due to insufficient space on its hard disk, the EventLog needs to be saved for as long as possible because there is no way to verify the problem. To do so, we needed a way to reduce the size of Log. By default, log-based text is compressed and archived. However, we needed to change the same information to a different format and reduce the size. Previous studies have also proposed a Comprehensive Log Compression(CLC) method that integrates data compression online [10] or uses frequent patterns and compression representations to identify repetitive information in large log files generated by communication networks [11]. However, these studies have reduced file size through compression without changing the structure of existing data. Parsing must be used to obtain the information it wants from the Log file [12; 13]. Depending on implementing parsed information, we also use the method of finding anomaly identification [14; 15; 16]. As an example of log analysis, the FIU aims to automate the process model [17] and facilitate data analysis on the system event log by combining the equipment status and event text analysis based on log files for the purpose of equipment productivity and diagnostic evaluation. There are studies on the design and implementation of an integrated system called Log Analysis Platform (FLAP) [18], and several event clustering proposal studies on log files in a network environment [19; 20]. More diverse studies have been conducted from the process mining perspective. In particular, studies have been conducted on algorithms and various models. Typical algorithm examples include pattern mining studies using logCluster algorithms [21], algorithms for detecting if logs are complete [22], process-oriented data mining algorithms and model configurations that provide insight into organizational processes using event log data [23], and algorithm studies for visualizing large-scale parallel process models [24; 25; 26; 27]. In addition, a study on a general approach to mining the personal information protection process using the information accessed to the system log [28], a study on personal information protection [29], event abstraction [30], knowledge extraction [31], operation Research [32], security vulnerabilities [33], quality-aware semi-automatic approach to extracting EventLogs from relational data [34], and event log prediction studies using RNN for utilization of EventLogs were performed [35]. From a mining perspective, some of the studies on information extraction and visualization are similar to ours. However, the visualization target is the visualization part of the model and differs from ours as the Logfile contents are not visualized in various ways. In order to store more information in the field of semiconductor equipment, semiconductor equipment is also offered as an optional PC for separate data storage. Data-Distribution Service for Real-Time Systems (DDS) for high-speed data logging, such as Process Log, is used to store many data in Log, but not all semiconductor equipment uses DDS. To evaluate DDS communication performance based on models built within the IEC 61,499 standard and compare it with existing socket-based solutions [36; 37] and evaluate equipment productivity and diagnostic purposes, automating model composition and processes based on log files [38] were investigated. DDS is focused on storing more data. Furthermore, research based on log files is similar in part to a parsing Log but different from the research we are conducting because it focuses on automating processes. We have looked into many previous studies but have not found any similar examples of research to the present study.