Adaptive Data Mining Query Expansion for Non-Indexed Sources
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Solution Overview
Problem
Current search engines are limited in analyzing multiple data points in real-time and are restricted to exact search terms and indexed web sites, failing to effectively extract data from non-indexed sources like proprietary data silos and social media comments, which is undesirable for real-time data 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
1Adaptability or versatility
If typical search engines are used for data analysis, then the search is limited to exact search terms and indexed web sites, but real-time analysis of multiple data points including non-indexed sources cannot be achieved
Solution Approach 1:
The system makes the search engine universal by enabling it to query multiple types of data sources simultaneously - both structured indexed sources and unstructured non-indexed sources like social media, proprietary databases, and comment sections. This multi-functional capability allows comprehensive real-time data collection across diverse formats and locations without requiring separate systems for each data type.
Solution Approach 2:
The system dynamically adapts its search capabilities by adjusting query parameters, data source selections, and analysis methods based on the specific real-time data mining task. The engine can flexibly switch between exact match searches and pattern recognition approaches, and dynamically incorporate new data sources as they become available, making the search process adaptable rather than static.
2Device complexity
If search engines are limited to exact search terms, then the query structure is simple, but the ability to extract meaningful patterns from diverse data is reduced
Solution Approach 1:
The query processing system segments the search process into multiple stages: initial exact term matching, followed by pattern recognition phases that break down complex queries into searchable components. This segmentation allows the system to maintain simple exact match operations while also performing sophisticated pattern analysis, dividing the complex task into manageable parts that can be executed efficiently.
Solution Approach 2:
The system changes query parameters dynamically based on the data source being searched and the type of information needed. It transforms exact search terms into expanded query patterns, adjusts matching thresholds, and modifies search depth parameters to optimize both the simplicity of the query structure and the precision of data extraction from different sources.
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.