Virtualized File Analytics With Lineage-Based Event Filtering
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Solution Overview
Problem
Existing systems for managing enterprise data files are complex and cumbersome, obscuring the overall performance view by including actions taken by applications that are ancillary to user requests, leading to inaccurate metrics and obscured reporting.
Innovation Solution
A file analytics system that filters event data to select events associated with user actions, uses a lineage index to track associations between requested and application-generated files, and provides accurate reporting by discarding ancillary application events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all application events are included in file analytics, then comprehensive monitoring coverage is achieved, but measurement precision deteriorates due to ancillary events obscuring user action metrics
Solution Approach 1:
The system segments events into user-initiated actions and application-generated ancillary events. By dividing the event stream into meaningful categories (user actions vs. application processing events), the system can selectively include only relevant events in analytics reports, thereby maintaining comprehensive monitoring while improving measurement precision through exclusion of obscuring ancillary events
Solution Approach 2:
The patent extracts and removes ancillary application events from the analytics data stream. By identifying and taking out events that are generated by applications but do not represent user actions (such as internal file processing events), the system preserves complete monitoring coverage of what happened while eliminating noise that would degrade the accuracy of user action metrics
2Productivity
If application-generated events are included in analytics, then complete event tracking is achieved, but reporting accuracy deteriorates due to obscured performance views
Solution Approach 1:
The system applies different quality filters to different event types. User-initiated events are included in analytics with full detail, while application-generated events are either excluded or marked as ancillary. This local differentiation ensures that the analytics report provides clear performance information about user actions without being clouded by application internal processing events, thus maintaining information clarity while preserving tracking completeness
3Reliability
If all file system events are monitored, then comprehensive analytics coverage is achieved, but system complexity increases due to difficulty in filtering relevant events
Solution Approach 1:
The system performs preliminary classification of events at the point of collection, tagging each event as either user-initiated or application-generated before it enters the analytics pipeline. This preliminary action simplifies subsequent filtering operations by pre-organizing events into categories, thereby maintaining comprehensive analytics coverage while reducing the complexity of event filtering through advance structuring of event data
Data Source
AI summary
Examples of file analytics systems are described that may obtain event data from a virtualized file server. The event data may be aggregated and/or filtered to provide metrics which may be adjusted based on the operation of an application used to accomplish a user action. For example, actions relating to an application's temporary file handling may be aggregated and/or excluded when reporting metrics for the virtualized file server. To facilitate reporting of metrics, the file analytics system may provide a lineage index storing an association between files related through operation of the application used to accomplish the user action.