Contextual Information Processing Framework for Distributed Systems
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Solution Overview
Problem
In distributed, heterogeneous computing systems, users face difficulties in interpreting information due to the lack of contextual information, which becomes increasingly challenging as systems grow and access to more information sources expands, with traditional metadata not providing sufficient context.
Innovation Solution
A contextual information processing framework that classifies information physically and logically, utilizing a context engine and sensors to retrieve and derive domain-independent and domain-specific information, dynamically linking it to information objects for enhanced interpretation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional metadata is used to describe information objects, then the system structure remains simple, but the contextual information provided is insufficient for proper interpretation
Solution Approach 1:
The patent segments contextual information into two distinct categories: primary contextual information (retrieved from external sources) and secondary contextual information (derived from primary information using derivation rules). This segmentation allows the system to manage complex contextual data in an organized manner, where each segment serves a specific purpose in providing comprehensive context without overwhelming the system structure.
Solution Approach 2:
The patent introduces context sensors as intermediary components that bridge external information sources and the information objects. These context sensors retrieve primary contextual information from external sources and make it available to information objects, acting as mediators that simplify the interaction between complex external data sources and the core system while providing rich contextual information.
2Measurement precision
If contextual information from multiple external sources is retrieved, then the interpretation accuracy improves, but the difficulty of managing and processing the information increases
Solution Approach 1:
The patent performs preliminary action by retrieving and storing primary contextual information from external sources before it is needed for interpretation. Context sensors proactively gather contextual data and make it available in advance, so when information objects require contextual interpretation, the relevant data is already prepared and organized, reducing the complexity of real-time information management.
Solution Approach 2:
The patent implements self-service through derivation rules that automatically generate secondary contextual information from primary contextual information without requiring manual intervention. The system self-manages the transformation and enrichment of contextual data, reducing the complexity of information management by automating the derivation process based on predefined rules.
3Adaptability or versatility
If domain-specific contextual information is provided, then the relevance to specific tasks improves, but the complexity of deriving such information increases
Solution Approach 1:
The patent applies local quality by providing different types of contextual information tailored to specific domains and information objects. Instead of a uniform approach, the system delivers domain-specific contextual information (such as weather conditions for outdoor operations or geographic information for location-based tasks) that is locally optimized for each particular information object's requirements, enhancing relevance without requiring complex manual customization.
Solution Approach 2:
The system achieves domain-specific adaptability through self-service derivation rules that automatically generate relevant contextual information based on the type of information object and its requirements. The derivation rules encode domain-specific knowledge and automatically apply the appropriate transformations, eliminating the need for complex manual configuration while maintaining high domain-specific relevance.
Data Source
AI summary
A method includes retrieving, at a context sensor associated with an information object, first contextual information from a source external to the context sensor. The first contextual information includes domain-independent information associated with the information object. The method also includes generating second contextual information based on application of at least one derivation rule to the first contextual information. Alternately, or in addition, the second contextual information may be generated based on application of the at least one derivation rule to a combination of the first contextual information and other contextual information associated with another context sensor. The second contextual information includes domain-specific information associated with the information object. The method further includes adding the second contextual information to the information object.