Adaptive Data Mining Query Expansion for Real-Time Analysis
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Solution Overview
Problem
Current data mining technologies are limited in analyzing real-time data across multiple sources, particularly due to their inability to search unindexed web pages and proprietary data silos, which restricts the availability of information for real-time analysis.
Innovation Solution
A method and apparatus for generating and expanding queries based on topics of interest, executing them across various data sources, including both open-source and proprietary data, and monitoring selected data sources for matches, enabling real-time data extraction and analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If typical search engines are used to query data, then the search is limited to indexed web sites and exact search terms, but the ability to analyze multiple data points in real time and access unindexed data sources is lost
Solution Approach 1:
The system makes data extractors universal by enabling them to operate across multiple data source types (web pages, databases, APIs, social media) with a single unified interface. The extractors can adapt to different data formats and structures automatically, allowing one extractor to serve multiple functions across diverse data sources rather than requiring specialized tools for each source type.
Solution Approach 2:
The system implements dynamic query expansion where search terms are automatically expanded based on contextual understanding of the topic. The query evolves dynamically by incorporating related terms, synonyms, and conceptually connected keywords that are discovered during the extraction process, allowing the system to adapt to unindexed sources that may use different terminology.
2Productivity
If real-time data extraction is implemented across multiple data sources, then comprehensive data analysis is achieved, but the complexity of managing and coordinating multiple extractors increases
Solution Approach 1:
The system merges multiple data extractors into a coordinated network that operates under unified management. Extractors are combined into teams that share resources, knowledge, and coordination mechanisms, reducing overall system complexity while maintaining high productivity. The merged extractors work collaboratively to extract data from multiple sources simultaneously.
Solution Approach 2:
The system introduces intermediary components that mediate between multiple data extractors and the central processing system. These intermediaries handle coordination, conflict resolution, and resource allocation, simplifying the management of complex multi-extractor operations and enabling scalable deployment without proportionally increasing system complexity.
3Adaptability or versatility
If query terms are expanded to cover more topics and data sources, then more comprehensive data is extracted, but the precision of the original search intent may be diluted
Solution Approach 1:
The system segments the expanded query results into hierarchical categories based on relevance to the original search intent. Primary extracts that directly address the original query maintain highest priority, while secondary and tertiary extracts organized by thematic segments provide broader context. This segmentation preserves precision for core results while incorporating versatile topic coverage in organized layers.
Solution Approach 2:
The system implements feedback loops where extraction results are continuously evaluated against the original search intent. Relevant extracts reinforce and refine the query expansion, while less relevant results are filtered or downweighted. This feedback mechanism ensures that query expansion adapts to maintain precision while achieving comprehensive topic coverage.
Data Source
AI summary
A method of analyzing data is presented. The method includes generating a query based on a topic of interest, expanding search terms of the query, executing the query on one or more data sources, monitoring a specific data source selected from the one or more data sources. The monitoring is performed to monitor for matches to the query.