Context-Aware Data Model for Search Information Reduction
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Solution Overview
Problem
The exponential growth of real-time information from diverse sources overwhelms decision-makers during events, making it difficult to filter out irrelevant information quickly, which hampers timely and accurate decision-making.
Innovation Solution
A system and method that utilize a context-entity factory to build a data model defining context-aware data objects, perform text mining and constraint-based mining, and submit outputs to a contextual query engine to build contextual query filters, which are combined with semantic query templates to produce refined semantic queries, effectively filtering out irrelevant information by capturing and comparing current context values with stored values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If information from multiple sources is collected and stored, then the quantity and diversity of available information increases, but the volume of irrelevant information increases making it difficult to filter quickly
Solution Approach 1:
The system performs preliminary action by pre-processing and tagging information from multiple sources with metadata before storage. This includes extracting entities, relationships, and contextual attributes, and storing them in a structured knowledge graph format. When a query is received, the system can immediately filter and retrieve relevant information without performing time-consuming analysis on raw data, thus resolving the contradiction between having abundant information and filtering it quickly.
Solution Approach 2:
The patent introduces an intermediary layer (the knowledge graph with contextual metadata) between the raw information sources and the query processing system. This intermediary structure organizes information with rich contextual attributes and relationships, enabling efficient filtering and retrieval. The intermediary translates unstructured or semi-structured data into a format that supports rapid querying, thus resolving the time loss issue while preserving information quantity.
2Reliability
If real-time information from diverse sources is processed, then the availability of actionable information improves, but the complexity of processing and storage increases
Solution Approach 1:
The system segments information processing into distinct modular components: data collection modules for different sources, entity extraction modules, relationship inference modules, and query processing modules. Each module handles specific tasks independently. The knowledge graph itself segments information into discrete entities, attributes, and relationships. This segmentation reduces overall system complexity by making each component manageable and interchangeable, while maintaining the ability to process real-time information from diverse sources.
Solution Approach 2:
The patent implements a universal knowledge graph structure that can accommodate multiple types of information sources and query types through a common framework. The contextual metadata schema is designed to be multi-functional, handling various data formats and relationships uniformly. This universality reduces complexity by avoiding the need for separate processing pipelines for different information types, while still maintaining high availability of actionable information across diverse domains.
3Measurement precision
If contextual metadata is extracted and stored for each data object, then the precision of information filtering improves, but the storage requirements and processing overhead increase
Solution Approach 1:
The system applies local quality by extracting and storing contextual metadata selectively based on the specific data object and its relevance to potential queries. Not all data objects receive the same level of metadata enrichment; instead, the system focuses computational resources on extracting contextual attributes that are most valuable for filtering and retrieval. This approach maintains high filtering precision for critical information while reducing storage overhead for less important data, resolving the contradiction between precision and storage requirements.
Data Source
AI summary
A system including a context-entity factory configured to build a data model defining an ontology of data objects that are context-aware, the model further defining metadata tags for the data objects. The system further includes a storage device storing the data objects as stored data objects, the device further storing associated contexts for corresponding ones of the stored objects. The system further includes a reduction component configured to capture a current context value of a first data object defined in the ontology, the component further configured to compare the current context value of the first data object with stored values of the associated contexts, and wherein when the current context value does not match a particular stored value of a particular associated context, the component is further configured to remove a corresponding particular stored data object and the particular associated context from the stored data objects.