User Action:
◦ The user imports various logs (e.g., .csv, .json, .xml, .evtx, .zip, .xls, .xlsx) or registry files.
◦ They can optionally specify a date range to filter the imported data.
Behind-the-Scenes Processing:
◦ The provided scripts automatically:
▪ Parse the imported files.
▪ Validate the data against the predefined schema.
▪ Retain matching data and ignore the rest.
▪ Normalize dates to UTC.
▪ Clean the data (e.g., remove duplicates, empty rows).
▪ Sort the retained data into artifact categories.
▪ Save the categorized data as structured JSON files.
Real-Time Metrics:
◦ The application tracks and displays metrics during processing:
▪ Total logs imported.
▪ Logs retained after validation.
▪ Logs categorized by artifact type.
▪ Logs grouped by machine name and platform.
Final Output:
◦ Organized folders containing structured JSON files, ready for submission to the AI pipeline.
◦ A metrics summary showing case-wide statistics and machine-specific inventories.
