Metastore Synchronization for Cross-Platform Data Integrity
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Solution Overview
Problem
Establishing dependencies between different data management platforms for cross-platform data management is challenging, leading to potential data loss and corruption due to differing structures and configurations, especially in conferencing software systems like UCaaS platforms.
Innovation Solution
Implementing a metastore manager that facilitates access to data and metadata across multiple data management platforms, utilizing AI/ML systems with user consent and privacy safeguards, and leveraging commercially licensed data sets for model training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data management platforms operate independently with their own structures and configurations, then each platform maintains operational independence and autonomy, but cross-platform data management becomes challenging leading to data loss and corruption
Solution Approach 1:
The patent introduces a metastore manager as an intermediary component that mediates between different data management platforms. This metastore manager handles metadata exchange and synchronization between platforms, enabling cross-platform data management while maintaining data integrity through standardized metadata protocols and dependency tracking mechanisms.
2Adaptability or versatility
If metadata synchronization is implemented across multiple platforms, then data exchange capability is improved, but system complexity increases due to dependency management requirements
Solution Approach 1:
The patent segments the metadata synchronization function into a dedicated metastore manager component that operates independently from the main data processing workflows. This segmentation isolates dependency management complexity into a specialized module, allowing other parts of the system to maintain simplicity while still benefiting from cross-platform data exchange capabilities.
3Productivity
If AI/ML systems are used for data management operations, then data processing efficiency is improved, but privacy concerns and user consent requirements increase complexity
Solution Approach 1:
The patent implements preliminary user consent acquisition and privacy configuration setup before any AI/ML data processing operations commence. The metastore manager establishes and maintains user consent records and privacy settings in advance, enabling subsequent automated AI processing while ensuring compliance through pre-established authorization frameworks.
Data Source
AI summary
Processing and transformation of data across multiple data management platforms is enabled through coordination of metadata and processing pipelines. A request is communicated from a driver node to a metastore manager to initiate a data processing operation on a data set within a first data store, resulting in a processed data set stored in the same data store and accompanied by partition metadata in a corresponding metastore. The metastore manager synchronizes this metadata with a second metastore associated with a distinct data management platform. The metastore manager then activates a data processing pipeline that operates independently of the first data management platform, enabling it to access the processed data set, apply further data transformations, and output a resulting processed data set.