Cognitive Data Ingestion Pipeline for Dark Data Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current technologies face challenges in efficiently processing and analyzing large volumes of big data, particularly 'dark data,' which is often neglected or underutilized, making it difficult to extract actionable insights in a timely manner.
Innovation Solution
A cognitive information processing system that receives data from multiple sources, establishes a dynamic data ingestion and enrichment pipeline, utilizing cognitive inference and learning operations to process and generate cognitive insights through semantic analysis, goal optimization, collaborative filtering, common sense reasoning, natural language processing, and entity resolution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional data processing approaches are used to handle big data, then data can be processed using conventional tools, but processing efficiency is insufficient and time intervals are not tolerable
Solution Approach 1:
The patent segments the data processing task into multiple parallel processing nodes distributed across a network. Each node independently processes portions of the big data using machine learning models, enabling concurrent execution and significantly improving processing efficiency while reducing overall processing time.
Solution Approach 2:
The patent introduces a distributed network infrastructure as an intermediary between data sources and analysis tools. This intermediary layer enables efficient data transmission and coordination among multiple processing nodes, allowing scalable processing of large datasets without centralized bottlenecks.
2Loss of information
If dark data is collected and stored, then potential insights are available, but the data remains neglected and underutilized
Solution Approach 1:
The patent enables dark data to self-organize and become useful through automated machine learning processes. The system automatically discovers patterns and relationships in previously neglected data without requiring manual curation or structured formatting, transforming underutilized data into actionable insights.
Solution Approach 2:
The patent changes the analytical parameters applied to dark data by using advanced machine learning algorithms that can process unstructured and semi-structured data formats. This parameter change enables the system to extract meaningful patterns from data that traditional processing methods could not utilize effectively.
3Productivity
If multiple data sources are integrated, then comprehensive analysis is possible, but system complexity increases
Solution Approach 1:
The patent implements a universal data processing framework that can handle multiple data sources and formats through a single standardized interface. The machine learning models are designed to be multi-functional, processing various types of data (structured, unstructured, semi-structured) through the same pipeline, thereby reducing system complexity despite integrating diverse data sources.
Data Source
AI summary
A method for performing dataset operations within a cognitive information processing system comprising: receiving data from a plurality of data sources; and, processing the data from the plurality of data sources, the processing the data establishing and maintaining a dynamic data ingestion and enrichment pipeline.