Knowledge Management System with User Affinity Mapping
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Solution Overview
Problem
Existing knowledge management systems fail to effectively provide relevant information to users by not understanding user relationships with the information, leading to irrelevant or overwhelming amounts of data, and struggle with gathering information from disparate systems.
Innovation Solution
A system and method for processing knowledge data asynchronously and in parallel, mapping information to users with affinity, distributing processing services in a distributed architecture, and normalizing data using XML for categorization, full-text indexing, and metrics extraction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If existing knowledge management systems gather information from various information systems without understanding user relationships, then large amounts of information are provided to users, but the information becomes irrelevant or overwhelming to the user
Solution Approach 1:
The system applies local quality by personalizing information delivery based on individual user profiles, affinities, and relationships. Each user receives customized information subsets rather than uniform bulk data, making the information relevant to their specific context and needs while maintaining high information quantity across the system as a whole
Solution Approach 2:
The system changes parameters by dynamically adjusting information selection based on user affinity scores, relationship data, and contextual factors. This allows the system to transform raw information quantities into personalized information streams that maintain relevance while preserving overall information availability
2Adaptability or versatility
If knowledge management systems access information from disparate systems, then comprehensive information coverage is achieved, but the complexity of gathering and processing information increases
Solution Approach 1:
The system introduces intermediary components including profile databases, affinity calculation modules, and relationship mapping systems that mediate between disparate information sources and users. These intermediaries standardize data access and simplify the complexity of gathering information from multiple sources by providing unified access points and processing layers
Solution Approach 2:
The system achieves versatility through universal components that can handle multiple information sources and user types. The affinity calculation engine, profile management system, and information retrieval mechanisms serve multiple functions across different data sources and user contexts, reducing overall system complexity while maintaining comprehensive coverage
3Quantity of substance
If knowledge management systems provide information in large quantities, then complete information availability is achieved, but the information becomes useless to the user due to lack of personalization
Solution Approach 1:
The system performs preliminary actions by pre-calculating user affinities, establishing relationship profiles, and pre-filtering information based on user characteristics before information requests are made. This preliminary personalization work ensures that when users access information, it is already tailored to their needs, maintaining both availability and usefulness without requiring real-time processing
Solution Approach 2:
The system implements feedback mechanisms that continuously refine information personalization based on user interactions, affinity changes, and relationship developments. This feedback loop ensures that information remains useful and relevant while maintaining comprehensive availability, as the system adapts to user needs over time based on observed behavior and explicit preferences
Data Source
AI summary
A system, method, and processor readable medium for processing data in a knowledge management system gathers information content and transmits a work request for the information content gathered. The information content may be registered with a K-map and assigned a unique document identifier. A work queue processes the work requests. The processed information may then be transmitted to another work queue for further processing. Further processing may include categorization, full-text indexing, metrics extraction or other process. Control messages may be transmitted to one or more users providing a status of the work request. The information may be analyzed and further indexed. A progress statistics report may be generated for each of the processes performed on the document. The progress statistics may be provided in a record. A shared access to a central data structure representing the metrics history and taxonomy may be provided for all work queues via a CORBA service.