Layered Dataset Linking for Cross-Platform Data Ingestion
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Solution Overview
Problem
Conventional data management techniques face challenges in efficiently linking and managing datasets across disparate platforms due to format incompatibilities, manual intervention requirements, and inefficiencies in data cleaning and integration, leading to unreliable data sets and hindered data sharing.
Innovation Solution
A collaborative dataset consolidation system that converts datasets into atomized data points and generates layered data files, enabling interoperability and seamless integration across platforms, with features like inference engines and layer data generators to enhance data quality and facilitate data sharing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional data management techniques are used to link and manage datasets across disparate platforms, then data format compatibility and integration efficiency are improved, but manual intervention requirements and data cleaning efforts increase
Solution Approach 1:
The system employs automated data ingestion processes that self-configure and self-adapt to different data formats without requiring manual intervention. The platform automatically detects data types, infers relationships, and performs cleaning operations autonomously, allowing the system to serve itself in managing heterogeneous datasets across platforms.
Solution Approach 2:
The system dynamically adjusts data processing parameters and transformation rules based on the characteristics of incoming datasets. By automatically modifying ingestion parameters, cleaning thresholds, and integration strategies according to data format and quality metrics, the system adapts to different platforms while maintaining automation.
2Reliability
If manual data cleaning and integration processes are employed, then data quality control is improved, but productivity and integration efficiency deteriorate
Solution Approach 1:
The system replaces manual mechanical data cleaning processes with automated computational algorithms. Machine learning models and inference engines automatically perform data validation, anomaly detection, and quality assurance tasks that previously required human intervention, thereby maintaining data quality while dramatically improving integration efficiency and productivity.
Solution Approach 2:
The system performs preliminary data cleaning, validation, and quality checks during the data ingestion phase rather than as a separate post-processing step. By proactively addressing data quality issues before integration, the system ensures high data reliability while maintaining continuous integration workflows without manual intervention.
3Ease of manufacture
If data is stored in conventional data silos with incompatible formats, then data storage simplicity is improved, but data sharing and collaboration are hindered
Solution Approach 1:
The system implements a universal data ingestion platform that can handle multiple data formats, structures, and protocols through a single integrated interface. The platform performs multi-functional operations including format conversion, schema mapping, and relationship inference to enable seamless data sharing and collaboration across disparate systems while maintaining storage simplicity.
Solution Approach 2:
The system acts as an intermediary layer between conventional data silos and collaborative data environments. By automatically transforming and standardizing data from various sources into a unified format while preserving original data characteristics, the platform enables interoperability without requiring changes to existing storage systems or data formats.
4Productivity
If automated data ingestion is implemented to reduce manual intervention, then productivity is improved, but system complexity increases
Solution Approach 1:
The system segments the automated data ingestion process into distinct modular components including data discovery, format detection, relationship inference, quality validation, and integration execution. Each module performs a specific function independently, allowing the system to maintain high productivity through automation while managing complexity through clear separation of concerns and reusable components.
Data Source
AI summary
Various embodiments relate generally to data science and data analysis, and computer software and systems 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 data ingestion is performed to form data representing layered data files and data arrangements to facilitate, for example, interrelations among a system of networked collaborative datasets. In some examples, a method may include forming a first layer data file and a second layer data file, assigning addressable identifiers to uniquely identify units of data and data units to facilitate the linking of data, and implementing selectively one or more of a unit of data and a data unit as a function of a context of a data access request for a collaborative dataset.