Data Point Version Control for Parallel Collaborative Workflows
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Solution Overview
Problem
Current collaborative systems face inefficiencies and errors in multi-user data management, particularly in managing large datasets, leading to inaccuracies and increased risk of data conflicts, which can result in noncompliance and significant financial costs.
Innovation Solution
A collaborative system with a centralized relational database and version control unit that tracks individual data points, allowing users to query and manage data efficiently, reducing the need for local installations and minimizing conflicts through version control at the data point level.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data is stored in a highly distributed fragmented heterogenous and unstructured way across different sources, then users can access data from multiple locations, but manual updating becomes notoriously prone to errors and inaccuracies
Solution Approach 1:
The system segments data into discrete data points within a structured relational database framework, allowing individual data points to be tracked and versioned independently. This segmentation enables precise control over data updates while maintaining overall data integrity across the distributed system.
Solution Approach 2:
The patent introduces a version control unit as an intermediary layer between users and the distributed data sources. This mediator automatically manages version tracking, conflict resolution, and data consistency across multiple sources, eliminating the need for manual updating while preserving data accessibility.
2Loss of information
If version control tracks entire datasets instead of individual data points, then complete data history is maintained, but system performance deteriorates when large numbers of changes are made
Solution Approach 1:
The version control mechanism is segmented to track individual data points rather than entire datasets. Each data point maintains its own version history, allowing the system to process only the specific data points that have changed, thereby maintaining complete data history while significantly improving performance when large numbers of changes are made.
3Measurement precision
If frequent pulls are done to stay up to date with the master branch, then data currency is maintained, but overall system performance is affected
Solution Approach 1:
Instead of performing full dataset pulls to maintain data currency, the system implements partial updates that fetch only the specific data points that have changed since the last synchronization. This selective approach maintains data currency while minimizing the performance impact on the system.
4Speed
If data is stored locally on user devices, then access speed is improved, but security risks increase when sensitive data is contained
Solution Approach 1:
The system creates controlled local copies of data for caching purposes while maintaining the master copy in the secure centralized relational database. These local copies are read-only and automatically synchronized, providing fast access speeds while eliminating security risks associated with storing sensitive data locally on user devices.
Data Source
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AI summary
A collaborative system for multi-user data management is provided. The system comprises a relational database having a plurality of data points, a communication interface configured to allow users to query the relational database, and a version control unit configured to keep track of successive historic versions of each data point of the plurality of data points in the relational database. The version control unit is configured to support parallel workflows with a plurality of version branches. The communication interface is configured to provide isolated sandbox environments for carrying out changes to at least one data point, each isolated sandbox environment being associated to one version branch. The version control unit is configured to detect changes between version branches, wherein the version control unit is configured to detect changes to data points in one or more higher level branches, and automatically apply the changes to the data points in the one or more higher level branches to data points in one or more lower level branches.