Automation of Outage Analysis using Natural Language Processing

Nowadays, most of the data in an organization is unstructured in nature. This unstructured data consists of huge amount of information that businesses can use it to understand about customers, products and market. Any organization that provides business to customers is required to deliver faster and better quality service. The analysis of this data is necessary and can help highlight issues that customers are facing in regard to products that employees can respond to and correct, improve customer satisfaction. We analyze the unstructured data using natural language processing techniques in order to obtain data in structured form. This work makes use of unstructured customer error logs that are related to server failure. Manual analysis of these error logs is tedious and time consuming. To automate the analysis and to extract relevant information, natural language processing and rule engine consisting of set of rules are being used. The aim of this work is to analyze the logs by the above mentioned techniques which help us to predict the causes of the server failure like the type of case it belongs, commodity, and symptoms related to the failure. In this way this work can help us to handle the customer issues efficiently and help determine the cause in an easier and faster way.