An Analysis of Health System Log Files using ELK Stack

Abstract

With the growth of machine data, logging is progressively critical. Logging helps in investigating and diagnosing the issues for the execution of ideal applications. The logs are not only used for discovering issues but also for searching the required data. The ELK stack abbreviated as Elasticsearch, Logstash, and Kibana is mainly centered around the logs. As the majority of logs are centered at one spot so that it can be able to see the procedure stream and query the questions against logs from all kind of applications from one spot. ELK underpins many log the executives and examination use cases that can get experiences from information. This finds what the information is defining all about and what needs to be done for the accomplishment of the business needs. In the current scenario the identification of the defect in the health system and system location is much difficult, So we propose a method to investigation on the log details of the health systems can give the assistance on identifying the defect on the existing safe guards using ELK stack, which in turn gives the assurance to the frameworks, by this it can have a learning from the data that was separated from the information so it can be able to assist us with keeping track of the defects in the system and the health of the system needs to be prioritized. The Log data is filtered based on system priority and country, because to identify the system state and location of the system, and this is visualized on the Kibana dashboard. This helps service engineers to identify the defect and location of system with short period of time.