Collaborative Dataset Consolidation via Atomized Data Layer
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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, using a dataset ingestion controller, query engine, and collaboration manager to facilitate data sharing and access while ensuring security and authorization.
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 interoperability deteriorates due to incompatible formats and systems creating data silos
Solution Approach 1:
The patent segments data into standardized, interoperable units that can be independently accessed and combined across different systems. By breaking down monolithic data silos into modular, standardized components, the system enables selective data sharing and integration while maintaining overall data capacity.
Solution Approach 2:
The patent implements a universal data access interface that allows diverse data sources to be accessed through a common protocol. This multi-functional approach enables the same interface to handle different data formats, storage systems, and access patterns, thereby improving interoperability without sacrificing data capacity.
2Reliability
If datasets are stored in separate silos to preserve confidentiality and commercial advantages, then data security is improved, but data sharing and collaboration deteriorate
Solution Approach 1:
The patent introduces an intermediary data access layer that sits between data owners and data consumers. This mediator enables secure data sharing by implementing controlled access policies, authentication mechanisms, and data transformation protocols that protect confidentiality while facilitating collaboration.
Solution Approach 2:
The patent implements preliminary security measures and access control configurations before data sharing occurs. By pre-establishing trust relationships, access policies, and data usage agreements, the system enables seamless collaboration while maintaining security without requiring real-time negotiations or complex authentication during data access.
3Device complexity
If traditional data accessing techniques are used for vast datasets, then system simplicity is improved, but access efficiency deteriorates due to inadequate handling of large-scale data
Solution Approach 1:
The patent adds a new dimension to data access by implementing a hierarchical or multi-layered access architecture. Instead of flat, monolithic data access, the system organizes data access in multiple dimensions (e.g., logical/physical layers, cached/computed layers), enabling efficient querying of vast datasets while maintaining interface simplicity.
4Volume of stationary object
If remote cloud-based data storage is used to collect differently-formatted repositories, then data centralization is improved, but data interoperability deteriorates due to format incompatibilities
Solution Approach 1:
The patent changes the parameters of data representation by implementing standardized schemas, data formats, and metadata structures. By transforming diverse data formats into a common parameter space, the system enables centralized storage of differently-formatted repositories while maintaining interoperability through consistent data description and access protocols.
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 method may include receiving data representing a query into a collaborative dataset consolidation system, identifying datasets relevant to the query, generating one or more queries to access disparate data repositories, and retrieving data representing query results. In some cases, one or more queries are applied (e.g., as a federated query) to atomized datasets stored in one or more atomized data stores, at least two of which may be different.