Collaborative Dataset Consolidation via Intermediary 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 and accessibility deteriorate due to incompatible formats and systems
Solution Approach 1:
The patent introduces a collaborative data layer as an intermediary between diverse data sources and users. This layer includes data translation services that convert data between different formats and protocols, enabling interoperability without requiring changes to the underlying data storage capacity. The mediator translates and adapts data representations, allowing systems with limited resources to access vast data repositories through standardized interfaces.
2Reliability
If datasets are stored in separate silos to preserve security and confidentiality, then data security is improved, but data sharing and collaboration deteriorate
Solution Approach 1:
The patent segments data access control into granular permission levels applied to individual data elements within the collaborative data layer. Instead of treating entire datasets as monolithic security units, the system divides access permissions into fine-grained segments that can be selectively applied. This allows organizations to share specific data subsets while maintaining security boundaries, enabling collaborative productivity without compromising overall data security posture.
Solution Approach 2:
The system implements feedback mechanisms through activity feeds and notification services that monitor and communicate data access, sharing, and collaboration events. This feedback loop allows security administrators to observe data sharing patterns in real-time and adjust permissions dynamically, while users receive notifications about collaborative activities. The feedback mechanism balances security monitoring with productive collaboration by making the system transparent and adaptable.
3Adaptability or versatility
If traditional computing platforms and database technologies are used, then system compatibility is improved, but ease of data access and analysis deteriorates due to data silos
Solution Approach 1:
The collaborative data layer implements universal data access interfaces that work across multiple traditional computing platforms and database technologies. The layer provides multi-functional capabilities including data translation, query federation, and unified access protocols that enable users to interact with diverse data sources through a single standardized interface. This universality eliminates the need for platform-specific access methods, significantly improving ease of operation while maintaining broad system compatibility.
4Volume of stationary object
If remote cloud-based data storage is used to collect differently-formatted repositories, then data centralization is improved, but resolution of format incompatibility and interoperability issues deteriorates
Solution Approach 1:
The patent positions the collaborative data layer as an intermediary between centralized cloud storage and end-user access points. This mediator includes automated data translation services that handle format conversion, schema mapping, and protocol adaptation. By placing the translation functionality in the collaborative layer rather than at storage or access endpoints, the system centralizes data while managing format complexity through a dedicated translation infrastructure, reducing the burden on both storage systems and user applications.
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.