Log Token Monitoring for MDM Process Progress Visibility
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Solution Overview
Problem
Existing mobile device management (MDM) systems lack visibility into the progress of large-scale processes such as security scans and application installations, as these processes are often controlled by third parties and data is obfuscated, making it difficult for users and administrators to monitor progress, identify errors, and improve efficiency.
Innovation Solution
A monitoring module analyzes preliminary log files generated during these processes to identify tokens representing specific actions, calculates an expected number of tokens, and compares this with actual counts to determine progress and identify errors, providing real-time feedback to users and administrators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If third-party processes are used for MDM operations, then process functionality and capabilities are improved, but visibility into process progress and data transparency deteriorate
Solution Approach 1:
The patent introduces an intermediary monitoring system that sits between the MDM administrator and the third-party processes. This intermediary captures log files from third-party processes, extracts relevant progress information, and presents it to administrators through a user interface, thereby mediating the information flow without requiring direct control of the third-party processes.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring log files and updating the user interface with current process status. Progress indicators, completion percentages, and error messages are fed back to administrators in real-time, enabling informed decision-making despite third-party process control.
2Loss of information
If log files are used for progress tracking, then process information is captured, but manual monitoring difficulty increases due to large file size and transient information
Solution Approach 1:
The system extracts only the essential progress-relevant information from large, complex log files. By identifying and extracting key tokens and progress indicators from the log data, the system presents a simplified view to users without requiring them to manually analyze entire log files.
Solution Approach 2:
Instead of requiring users to directly examine the original large log files, the system creates simplified copies or representations of the progress information. The user interface displays curated progress data that mirrors the essential information from logs but in a human-readable, easily monitorable format.
3Reliability
If comprehensive log monitoring is implemented, then error detection capability is improved, but system complexity and processing requirements increase
Solution Approach 1:
The monitoring system is segmented into distinct functional modules: log file acquisition, token identification, progress calculation, error detection, and user interface presentation. This segmentation allows each component to perform its specific function efficiently without requiring the entire system to be complex.
Solution Approach 2:
The system performs partial monitoring by focusing only on specific tokens and progress indicators relevant to process monitoring, rather than analyzing every detail of log files. This selective approach provides sufficient error detection capability without the complexity of comprehensive log analysis.
Data Source
AI summary
An embodiment includes a method of process analysis that includes obtaining a preliminary log file. The preliminary log file is generated during implementation of a first process. The method includes identifying a first token associated with the first process in the preliminary log file. The method includes calculating an expected number of the first token in the preliminary log file. The method includes identifying a number of times the first token is present in the preliminary log file. The method includes determining a progress indicator of the first process based on a comparison between the expected number of the first token in the preliminary log file and the number of times the first token is present in the preliminary log file.