Semantic Reasoning for Dynamic Data Assembly
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Solution Overview
Problem
Existing information processing systems in cloud infrastructure lack a unified and flexible approach to handling data set metadata, leading to inefficiencies, errors, and doubts about process validity due to inadequate consideration of data set provenance, versioning, and suitability for designated purposes.
Innovation Solution
A semantic reasoning system that interacts with data processing elements to perform reasoning operations on metadata, identifying suitable data sets for assembly based on designated purposes, using a dynamic information assembly module to assemble subsets of data sets for achieving specific objectives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If existing inflexible information assembly approaches are used, then system simplicity is maintained, but processing accuracy and reliability deteriorate due to inadequate consideration of data set provenance, versioning, and suitability
Solution Approach 1:
The patent introduces a reasoning system as an intermediary component that sits between the data processing elements and the information assembly process. This reasoning system performs semantic reasoning operations on metadata to determine data set suitability, acting as a mediator that enhances processing accuracy without requiring fundamental changes to the underlying complex cloud infrastructure. The intermediary layer handles the complexity of provenance, versioning, and suitability assessment, isolating these concerns from the core processing pipeline.
Solution Approach 2:
The patent segments the information assembly process into distinct functional components: a reasoning module for performing semantic reasoning operations on metadata, and a dynamic information assembly module for assembling data sets based on reasoning results. This segmentation allows each module to specialize in specific tasks, improving overall processing accuracy while maintaining manageable system complexity through modular design.
2Reliability
If optimistic or pessimistic assumptions are made about data sets, then processing speed is maintained, but reliability deteriorates due to inaccurate assumptions about data set availability and suitability
Solution Approach 1:
The patent implements preliminary action by performing semantic reasoning operations on metadata before the actual information assembly process. The reasoning system assesses data set suitability, provenance, and versioning information in advance, ensuring that only appropriate data sets are selected for assembly. This preliminary assessment improves reliability by validating data set appropriateness before processing, while the automated nature of the reasoning operations maintains processing efficiency.
Solution Approach 2:
The reasoning system provides feedback about data set suitability and metadata quality to the information assembly process. This feedback mechanism allows the system to adjust its processing based on actual data characteristics rather than relying on optimistic or pessimistic assumptions. The feedback loop ensures that processing decisions are based on accurate assessments of data set properties, improving both reliability and efficiency.
3Adaptability or versatility
If no unified metadata repository is implemented, then system complexity is reduced, but information assembly effectiveness deteriorates due to lack of explicit metadata representation for reasoning
Solution Approach 1:
The patent implements a unified metadata repository that serves multiple functions: storing data set metadata, enabling semantic reasoning operations, supporting provenance tracking, and facilitating interoperability across different data processing elements. This universal metadata infrastructure improves adaptability and interoperability by providing a common framework for metadata representation, while the standardized structure actually reduces overall complexity compared to multiple disparate metadata systems.
Data Source
AI summary
A reasoning system is configured to interact with data processing elements of an information processing system. The reasoning system includes a reasoning module configured to perform one or more reasoning operations on metadata. The metadata characterizes data sets associated with the data processing elements in order to identify at least selected portions of one or more of the data sets as being suitable for use in achieving a designated purpose. The reasoning system also includes a dynamic information assembly module configured to utilize results of the one or more reasoning operations to assemble at least a subset of the selected portions so as to achieve the designated purpose. The reasoning system and associated data processing elements may be implemented, by way of example, in cloud infrastructure of a cloud service provider, or on another type of processing platform.