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 and systems, leading to data silos that hinder interoperability and limit access to valuable information, especially for organizations with limited resources.
Innovation Solution
A collaborative dataset consolidation system that converts datasets into a unified, atomized format, allowing for interoperability across different platforms and systems, and provides a secure, authorized access mechanism while facilitating data sharing and collaboration among users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional data storage technologies are used to store vast amounts of data, then data capacity is improved, but data accessibility and interoperability deteriorate due to data silos and incompatible formats
Solution Approach 1:
The patent introduces a collaborative data layer as an intermediary between disparate data stores and computing machines. This layer includes data stores that can be accessed by multiple machines and collaborative logic that enables interoperability through standardized interfaces, allowing data to be shared across different platforms without losing accessibility or compatibility
Solution Approach 2:
The patent creates universal data stores and collaborative logic that can serve multiple different computing platforms and data formats simultaneously. The system enables a single data store to be accessed by various machines with different requirements, making the data infrastructure multi-functional and adaptable to diverse needs
2Reliability
If datasets are stored in separate data silos to preserve commercial advantages or confidentiality, then data security is improved, but data sharing and collaboration deteriorate
Solution Approach 1:
The patent segments data access control into granular levels, allowing different portions of data to be shared with different levels of access. The collaborative data layer enables fine-grained authorization where specific data elements can be made accessible to particular computing machines or users while maintaining security for other portions, resolving the conflict between security and sharing
Solution Approach 2:
The collaborative data layer acts as a mediator between security requirements and sharing needs. It provides a controlled interface that enables data sharing while maintaining security policies, allowing organizations to share data collaboratively without compromising commercial advantages or confidentiality through the standardized access mechanisms
3Device complexity
If traditional computing and data systems are used, then system simplicity is maintained, but access to information deteriorates for organizations with limited resources
Solution Approach 1:
The collaborative data layer serves as an intermediary that simplifies data access for organizations with limited resources. By providing standardized interfaces and collaborative logic, it enables these organizations to access and share data without needing to develop complex proprietary systems, maintaining simplicity while improving accessibility
Solution Approach 2:
The patent creates universal access mechanisms that work across different resource levels. The collaborative data layer provides functionality that benefits both resource-rich and resource-limited organizations, enabling easy data access and sharing regardless of the underlying system complexity
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.