Empirical Attribution System for Unstructured Data Ingestion
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Solution Overview
Problem
Existing systems face challenges in consistently vetting and ingesting data at scale from unstructured or poorly curated sources, particularly social media, due to the absence of a sufficient ontology or canonical form, leading to issues with confounding characteristics like sarcasm, neologisms, and language variations, which affect scalability and automation.
Innovation Solution
A method that attributes data at multiple levels, identifies confounding characteristics, calculates weighted attributes and characteristics, and filters data to produce a disposition, allowing for automated decision-making and data ingestion, enabling faster, more scalable, and flexible systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data from unstructured sources is ingested at scale, then data quantity and processing speed increase, but data quality and reliability deteriorate due to confounding characteristics
Solution Approach 1:
The system performs preliminary attribution and quality assessment of data sources before ingestion. By pre-screening data sources using multiple attribution levels (context, source file, content) and calculating quality scores, the system identifies and filters out low-quality data before it enters the main processing pipeline, ensuring high-quality data ingestion at scale without manual intervention
Solution Approach 2:
The patent introduces an intermediary quality assessment layer between data sources and the ingestion pipeline. This intermediary system uses empirical attribution methods to evaluate data quality metrics and acts as a gatekeeper, allowing only data meeting quality thresholds to proceed, thus maintaining reliability while enabling scalable ingestion
2Extent of automation
If automated decision-making is implemented for data ingestion, then processing efficiency increases, but ability to handle confounding characteristics deteriorates
Solution Approach 1:
The system handles confounding characteristics by adding multiple attribution dimensions (context level, source file level, content level) rather than relying on single-dimensional rules. This multi-dimensional approach enables automated systems to capture nuanced qualities like sarcasm, neologisms, and language variations that traditional automation would miss
Solution Approach 2:
The patent employs dynamic parameter adjustment in the attribution process. By calculating quality scores based on multiple measurable parameters and thresholds, the automated system can adaptively handle different confounding characteristics through quantitative assessment rather than rigid rule-based processing
3Measurement precision
If multiple attribution levels are applied to data, then data quality assessment improves, but processing complexity increases
Solution Approach 1:
The system segments the quality assessment process into three distinct attribution levels: context level (data delivery frequency, shelf life), source file level (metadata, creation date), and content level (writing system, individual data elements). This segmentation allows complex multi-dimensional assessment to be broken into manageable, independent modules that can be processed systematically
Data Source
AI summary
There is provided a method that includes (a) receiving data from a data source, (b) attributing the data source in accordance with rules, thus yielding an attribute, (c) analyzing the data to identify a confounding characteristic in the data, (d) calculating a qualitative measure of the attribute, thus yielding a weighted attribute, (e) calculating a qualitative measure of the confounding characteristic, thus yielding a weighted confounding characteristic, (f) analyzing the weighted attribute and the weighted confounding characteristic, to produce a disposition, (g) filtering the data in accordance with the disposition, thus yielding extracted data, and (h) transmitting the extracted data to a downstream process. There is also provided a system that executes the method, and a storage device that contains instructions for controlling a processor to perform the method.