Collaborative Data Layer for Interoperable Dataset Consolidation
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Solution Overview
Problem
Conventional data storage and computing technologies face challenges in managing and accessing vast, disparate datasets due to incompatible formats, limited interoperability, and resource constraints, leading to isolated 'data silos' that hinder collaboration and utilization of data across different platforms and organizations.
Innovation Solution
A collaborative dataset consolidation system that converts datasets into a unified, atomized format, allowing for interoperability across different platforms and formats, and facilitates secure access and sharing through a dataset ingestion controller, query engine, and collaboration manager, enabling the formation of consolidated datasets and real-time data sharing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If datasets are stored in conventional data stores using different computing platforms and systems, then data storage capacity is improved, but data interoperability deteriorates leading to data silos
Solution Approach 1:
The patent implements a universal data store architecture that can accommodate multiple data formats and computing platforms through a common interface layer. This layer translates and normalizes data from various sources (CSV, HTML, JSON, XML, etc.) into a unified structure, enabling the system to serve multiple functions: storing heterogeneous data, providing standardized access, and facilitating interoperability across different platforms without sacrificing storage capacity.
2Reliability
If corporate-generated datasets are kept in data silos to preserve commercial advantages, then data security is improved, but data sharing and public benefits deteriorate
Solution Approach 1:
The patent segments data access rights by implementing fine-grained authorization controls that allow different levels of data sharing. Sensitive corporate data can be segmented into controlled access portions (maintaining security) and non-sensitive portions that can be shared publicly or with specific collaborators. This segmentation enables simultaneous achievement of security and sharing goals by applying different access policies to different data segments.
3Reliability
If academia-generated datasets are stored in data silos to preserve confidentiality, then data protection is improved, but data accessibility and collaboration deteriorate
Solution Approach 1:
The patent introduces an intermediary authorization layer between the data store and access requests. This intermediary validates credentials, enforces access policies, and mediates between confidentiality requirements and collaboration needs. Researchers can collaborate on specific datasets through authorized access without exposing confidential information, as the intermediary manages authentication and authorization transparently.
4Device complexity
If traditional computing and data systems are used with limited resources, then system simplicity is improved, but access to information and collaboration capabilities deteriorate
Solution Approach 1:
The patent implements self-service mechanisms where the data store automatically performs data normalization, format conversion, and access optimization without requiring complex manual configuration. The system self-adapts to different data types and automatically applies appropriate access policies, reducing the operational burden on users with limited resources while maintaining high information access efficiency through automated processes.
Data Source
AI summary
Various embodiments relate generally to data science and data analysis, computer software and systems, and wired and wireless network communications to provide an interface between repositories of disparate datasets and computing machine-based entities that seek access to the datasets, and, more specifically, to a computing and data storage platform that facilitates consolidation of one or more datasets, whereby a collaborative data layer and associated logic facilitate, for example, efficient access to, and implementation of, collaborative datasets. In some examples, a system may include an atomized workflow loader configured to receive an atomized dataset to load into a data store, and to determine resource requirements data to describe at least one resource requirement. The atomized workflow loader may be further configured to select a data store type based on a resource requirement, and perform a load operation of the atomized dataset as a function of the data store type.