Constraint-Driven Database Search With Visual Entry Filtering
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Solution Overview
Problem
Conventional database searching methods, particularly keyword-based searches across large or multiple databases, are time-intensive, often miss important results, and provide confusing or off-target results, making them inefficient and difficult to interpret.
Innovation Solution
Implementing a constraint-driven database searching system that allows users to input constraints across multiple databases, refining searches with additional constraints, and providing a visual representation of results based on entry field types, allowing for more targeted and accurate data retrieval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If keyword-based searching is used across large databases, then the search can cover broad ranges of data, but the search time becomes excessively long and results become confusing
Solution Approach 1:
The patent segments the search process by dividing the database into multiple collections and applying constraints at different levels (collection level and entry level). This segmentation allows the system to quickly filter large databases by evaluating constraints against collection metadata first, then only processing relevant entries, thereby reducing overall search time while maintaining comprehensive coverage.
Solution Approach 2:
The patent implements preliminary action by pre-evaluating constraints against collection-level data structures and metadata before performing detailed entry-level searches. This preliminary filtering step identifies and eliminates irrelevant collections upfront, so that subsequent searches only process a reduced subset of data, significantly improving search speed without sacrificing result accuracy.
2Loss of time
If targeted search queries are limited to certain fields, then search time is reduced, but important results may be missed
Solution Approach 1:
The patent implements dynamic constraint evaluation where the system automatically determines which constraints to apply at collection level versus entry level based on the data structure and query characteristics. This dynamic approach ensures that the search process adapts to different scenarios, applying field-level constraints only where necessary to maintain result accuracy while avoiding unnecessary processing elsewhere.
Solution Approach 2:
The patent adds a hierarchical dimension to the search process by implementing multi-level constraint evaluation (collection level and entry level). This dimensional approach allows constraints to be applied selectively at different hierarchical levels, ensuring comprehensive result accuracy while reducing overall search time through progressive filtering.
3Measurement precision
If multiple constraints are applied to refine searches, then result accuracy is improved, but the system complexity increases
Solution Approach 1:
The patent segments constraints into different categories (collection-level constraints and entry-level constraints) and processes them through separate evaluation pathways. This segmentation simplifies the overall system complexity by allowing each constraint type to be handled with specialized, optimized logic rather than requiring a single complex evaluation mechanism to handle all constraint types.
4Reliability
If comprehensive database searches are performed, then all possible results are captured, but the results become difficult to interpret and off-target
Solution Approach 1:
The patent segments results into different hierarchical levels (collection-level summaries and entry-level details), allowing users to first review high-level results to understand the scope and relevance of findings, then drill down into specific entries as needed. This segmented presentation improves interpretability while maintaining result completeness.
Data Source
AI summary
Systems and methods are provided for performing constraint driven database searching. At least one constraint is received for a search of one or more databases comprising one or more datasets, each comprising multiple entry fields. The one or more databases comprising the one or more datasets may be accessed and searched based on the constraint to identify one or more dataset entries that contain the at least one constraint in one of multiple entry fields. A visual representation of the identified dataset entries may be provided to the user as results of the constraint driven database search. Additional constraints may be accepted to further narrow the results of constraint driven database search. The results may be exported and made accessible for later retrieval and/or cached for use in subsequent searches.