Logs are an essential part of the development and maintenance of large and complex software systems as they contain rich information pertaining to the dynamic content and state of the system. As such, developers and practitioners rely heavily on the logs to monitor their systems. In parallel, the increasing volume and scale of the logs, due to the growing complexity of modern software systems, renders the traditional way of manual log inspection insurmountable. Consequently, to handle large volumes of logs efficiently and effectively, various prior research aims to automate the analysis of log files. Thus, in this paper, we begin with the hypothesis that log files are natural and local and these attributes can be applied for automating log analysis tasks. We guide our research with six research questions with regards to the naturalness and localness of the log files, and present a case study on anomaly detection and introduce a tool for anomaly detection, called ANALOG, to demonstrate how our new findings facilitate the automated analysis of logs.