Database Search Query Enhancement With Prior-Query Machine Learning
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Solution Overview
Problem
The increasing volume of data in databases leads to excessive resource consumption and time expenditure in traditional database queries, particularly when intermediary systems direct searches to multiple entities, resulting in wasted processing and network resources due to unmeaningful or inapplicable responses.
Innovation Solution
A search query enhancer at the intermediary level uses a machine learning algorithm to analyze previous queries, responses, and user selections to enhance queries and responses, providing more meaningful and applicable information, thereby reducing resource waste.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional database queries are used to search large amounts of data, then the system can retrieve information, but the resources and time needed to query the data increase significantly
Solution Approach 1:
The system performs preliminary actions by pre-processing and storing data in a structured format before actual queries are received. The intermediary system pre-organizes data from multiple entities, creating an optimized data structure that enables faster query processing without requiring full database scans during actual user requests.
Solution Approach 2:
An intermediary system is introduced between the user and the database entities. This intermediary receives queries from users, translates them into optimized search parameters, and coordinates with multiple entities to retrieve relevant information. The intermediary acts as a mediator that reduces the burden on individual entities and optimizes overall query processing efficiency.
2Adaptability or versatility
If search queries are directed to multiple entities through an intermediary, then more comprehensive results can be obtained, but the amount of data queried and processed increases dramatically
Solution Approach 1:
The system applies local quality by tailoring query parameters and search strategies to the specific characteristics of each entity. Rather than treating all entities uniformly, the intermediary adjusts query parameters based on the particular entity being queried, optimizing the balance between comprehensiveness and data processing volume for each individual entity.
Solution Approach 2:
The system dynamically changes query parameters based on the target entity and the user's search intent. The intermediary modifies search parameters such as query depth, data fields to retrieve, and search scope according to the specific entity characteristics, thereby reducing unnecessary data processing while maintaining comprehensive results.
3Reliability
If entities perform searches to provide responses, then meaningful results can be generated, but resources are wasted when responses are not meaningful or applicable
Solution Approach 1:
The system implements feedback mechanisms where the intermediary receives responses from entities and evaluates their meaningfulness and applicability. Based on this feedback, the system can identify unmeaningful or inapplicable responses and prevent further processing resources from being wasted on such responses. The feedback loop enables continuous optimization of query effectiveness.
Solution Approach 2:
The system changes query parameters dynamically based on the quality and relevance of responses received from entities. When responses are found to be unmeaningful or inapplicable, the intermediary adjusts subsequent query parameters to focus on more relevant data fields or modifies the search strategy to prioritize high-quality responses, thereby reducing resource waste.
4Reliability
If the intermediary queries its own database to provide enhanced queries, then more meaningful information can be determined, but processing and network resources are consumed
Solution Approach 1:
The intermediary performs preliminary actions by pre-querying its own database to retrieve and store relevant information before actual user searches occur. This pre-processing allows the system to have meaningful information readily available when users initiate searches, reducing the need for extensive real-time database queries and conserving processor and network resources during actual user interactions.
Data Source
AI summary
An apparatus includes a memory and a hardware processor that receives a query from a device. The query includes first search parameters. The processor also retrieves, from a database and based on the first search parameters, a plurality of previously issued queries and applies a machine learning algorithm on the plurality of previously issued queries to determine second search parameters. The processor further adds the second search parameters to the query to form an enhanced query and communicates the enhanced query to a plurality of response systems. The processor then receives, from the plurality of response systems, a plurality of responses to the enhanced query, constructs, based on the plurality of responses to the enhanced query, an enhanced response to the query, and communicates the enhanced response to the device for selection of a response from the plurality of responses.