Python Based Analysis For Customer Issue Resolution Using CMT App Device Logs

Cambridge Mobile Telematics (CMT) is the world's biggest telematics specialist organization. The organization's AI-driven stage accumulates sensor information from several IoT gadgets including cell phones, exclusive Tags, associated vehicles, dashcams, and outsider gadgets and combines them with contextual information. Thanks to the heterogeneity of components, manufacturers, and evolution of underlying firmware. Creating logs is one of the strategies for recording the operating events, which has variety of file structures and due to the lack of standardization of log syntax, analysing of these files is complex for manual processing. The Support Team must analyse the Device logs to Give a solution for the issues raised by the CMT cus-tomers. CMT customers face two significant issues; one is related to the Tag linking Issue that is specific to the App Plus Tag Program, and the other is regarding the Trip Recording Issue, which is typical for both the App-only Program and App Plus Tag Program. Pattern Detection using Python is one of the best approaches for better detection and classification of data from device logs. Two different pattern detection utilities were developed, which gives the detailed explanation of the workflow and can also serve as a support for: tracking the necessary Patterns, Storing the process of different functional patterns, method of data(patterns) extraction and the output formatting of issues with the solution. In this paper, pattern extraction is done using python programming language, and the output will contain the problem statement with solution in various formats like JSON and CSV.