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Desktop App: Data to Server Flow

This outlines the typical process the Mera Monitor desktop application uses to process collected user activity data and upload it to the central server.

Steps​

  1. API Token Validation: Ensure the application has a valid token to communicate with the server.
  2. Daily Table Check: Identify relevant daily data tables (often SQLite) containing raw captured data.
  3. User Data Retrieval: Fetch user-specific activity data recorded by the desktop agent.
  4. Screen Activity Processing:
    • Aggregate raw screen activity records, grouping them by application name, window title, and URL (if applicable).
    • Calculate derived metrics like away time, first/last activity timestamps within a timeframe.
    • Apply productivity classifications (Productive/Unproductive) based on defined rules.
  5. Keystroke/Mouse Data Processing:
    • Aggregate keyboard stroke counts and mouse click/movement data.
    • Identify periods of idle time based on lack of input.
    • Update relevant activity and idle time tables.
  6. Iterative Processing: Repeat the processing steps for all monitored users managed by the application instance.
  7. Data Upload: Securely transmit the processed data to the main server database.
  8. Cleanup: Optionally, clean up or archive raw data locally after successful processing and upload to manage disk space.

Key Data Tables (Conceptual)​

The process often involves interacting with several local and server-side tables:

  • Local Daily Tables: Store raw, timestamped activity logs (e.g., application focus changes, URLs visited, keyboard/mouse events).
  • User Screen Activity Table: Stores aggregated and processed screen time per application/URL.
  • User Daily Activity Table: Contains daily summaries of total active, productive, unproductive, and idle time.
  • User Productivity Table: Maps applications/websites to their productivity status.
  • User Key/Mouse Activity Tables: Stores aggregated input activity counts.
  • User Daily Idle Activity Table: Records detected periods of user idle time.
Note

Data processing might occur at set intervals or triggered by specific events (e.g., user logout, system shutdown) to ensure timely updates on the server.