Dataset Context Analyzer for Memory Optimization
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Solution Overview
Problem
Managing and optimizing memory usage in multi-tiered software applications with multiple workspaces is challenging due to cumbersome data sharing and tracking changes across instances, especially when multiple users access and modify the same data, leading to increased memory consumption and complexity.
Innovation Solution
Implementing a system that tracks data modifications by creating workspace contexts to restrict access and modifications, using dataset context analyzers and transformation graph analyzers to determine sharing eligibility between workspaces, and employing a dataset manager to optimize memory usage by sharing datasets based on context and transformation equality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If local copies of datasets are created for each workspace instance, then data access and modification are simplified for each instance, but memory consumption increases and data sharing between workspaces becomes cumbersome
Solution Approach 1:
The patent merges multiple local dataset copies into a single shared dataset stored in cloud storage, allowing multiple workspace instances to access the same data simultaneously. This eliminates redundant data storage while maintaining individual workspace access capabilities through a unified data sharing mechanism.
Solution Approach 2:
The shared dataset serves multiple workspaces simultaneously, making a single data storage resource universal across different workspace instances. This multi-functional approach allows the same dataset to be accessed and modified by multiple users without requiring separate copies for each workspace.
2Adaptability or versatility
If multiple users access and modify the same data via workspace instances, then collaboration is improved, but tracking changes and maintaining data integrity becomes challenging
Solution Approach 1:
The patent implements a feedback mechanism where change notifications are automatically generated and propagated when data is modified in any workspace instance. The notification system provides real-time feedback to other users, enabling them to see and respond to changes made by others, thus maintaining data integrity through continuous synchronization.
Solution Approach 2:
The patent introduces a data sharing mechanism as an intermediary layer between multiple workspace instances and the underlying dataset. This mediator handles change tracking, notification distribution, and conflict resolution, simplifying the complexity of direct multi-user access by providing a controlled interface for data modifications.
3Quantity of substance
If data is shared between workspace instances, then memory efficiency is improved, but determining modifications with reference to context and maintaining data integrity becomes challenging
Solution Approach 1:
The notification system provides continuous feedback about data modifications to all workspace instances accessing the shared dataset. This feedback mechanism ensures that all users are aware of changes made by others, enabling them to adjust their work accordingly and maintain data integrity through informed decision-making.
Solution Approach 2:
The patent implements preliminary actions by establishing context-aware modification tracking before data changes occur. The system pre-configures notification rules and context reference mechanisms that automatically activate when modifications are made, ensuring data integrity is maintained proactively rather than reactively.
Data Source
AI summary
Methods and system are disclosed that optimize memory consumed, by sharing datasets between workspaces. In one aspect, multiple workspaces may be instantiated by retrieving multiple datasets from multiple data sources. An input to execute transformations on the multiple datasets may be received. Based on a determination of execution of a transformation on the multiple datasets, a dataset context analyzer may be executed to analyze a current context of data in the multiple datasets. Based on a result of execution of the dataset context analyzer, a transformation graph analyzer may be executed to compute a current context of transformations on the multiple datasets. Based on the determined current context of data and the current context of the transformations on the dataset, memory consumed may be optimized by sharing the multiple datasets between the multiple workspaces.