Cloud Analytics Filter Reset via Data Story Snapshots
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Solution Overview
Problem
Cloud-based analytics engines face challenges in efficiently managing filters applied to data presentations, as existing systems often propagate filters across levels, leading to data loss and lack of efficient reset mechanisms, which complicates data analysis and visualization.
Innovation Solution
The implementation of a cloud-based analytics engine that generates and stores snapshots of data story definitions, allowing for filter application and reset without propagating changes across levels, thereby restoring original data states and enhancing user control over data visualization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If filters are applied to data presentations in cloud-based analytics engines, then data analysis precision is improved, but data loss occurs and reset mechanisms are lacking
Solution Approach 1:
The system performs preliminary action by creating and storing a snapshot of the original data story definition before filters are applied. This snapshot contains the complete, unfiltered data configuration and is preserved for potential restoration, preventing permanent data loss when filters are applied to improve analysis precision.
Solution Approach 2:
The system creates a copy of the original data story definition in the form of a snapshot. This copy stores the complete data configuration before filtering, allowing the system to restore the original state by replacing the filtered runtime definition with the preserved snapshot, thereby preventing data loss while maintaining filtering capabilities.
2Ease of operation
If filters are applied across data presentation levels, then data analysis control is improved, but system complexity increases due to propagation issues
Solution Approach 1:
The system segments the data story definition into two distinct parts: a snapshot of the original definition and a runtime definition. This segmentation allows filters to be applied to the runtime definition without affecting the original snapshot, simplifying filter management and reducing system complexity by isolating filter operations from the core data structure.
Solution Approach 2:
The runtime definition acts as an intermediary between the original data story snapshot and the filtered data presentations. This intermediary layer absorbs the complexity of filter propagation and restoration operations, allowing simple reset operations that replace the runtime definition with the snapshot, thereby improving ease of operation while managing system complexity.
3Reliability
If snapshots of data story definitions are stored, then data restoration capability is improved, but memory usage increases
Solution Approach 1:
The system extracts and stores only the essential snapshot of the data story definition - a serialized representation of the original configuration - separate from the active runtime data. This extraction approach preserves data restoration capability by storing minimal necessary information while allowing the runtime system to operate with reduced memory overhead.
Data Source
AI summary
A method for accessing data stored in a database may include generating a snapshot of a definition of a data story that includes a software widget configured to receive inputs for creating, based on a portion of data stored in the database, a data presentation providing a visual representation of the data. In response to a first indication to apply a filter removing some of the data associated with the data presentation, modifying a runtime definition of the data story to reflect the application of the filter. In response to a second indication to reset the filter, restoring the data story to a state prior to the application of the filter by replacing the runtime definition of the data story with the snapshot of the definition of the data story. Related systems and articles of manufacture are also provided.