Poll Optimizer Query Flattening for Crowd-Sourced Polls
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Solution Overview
Problem
Existing crowd-sourced polling systems face inefficiencies in terms of completion times, costs, and error rates due to complex query structures and unbalanced worker participation, leading to increased expenses and reduced accuracy in survey results.
Innovation Solution
The Poll Optimizer employs techniques such as query flattening, splitting, common subexpression elimination, and dynamic execution strategies to optimize crowd-sourced polls by simplifying query structures and adapting worker payments to balance participation, thereby reducing costs and improving response times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If complex multi-layer query structures are used in crowd-sourced polling, then comprehensive survey coverage is improved, but poll completion time increases
Solution Approach 1:
The patent segments complex multi-layer query structures into multiple simpler sub-queries that can be executed in parallel or sequentially with optimized resource allocation. This breakdown reduces the computational overhead and execution time while maintaining comprehensive survey coverage across all segments.
Solution Approach 2:
The system performs preliminary actions by pre-compiling and optimizing query execution plans before actual poll execution. Query structures are analyzed and transformed into optimized forms in advance, reducing runtime processing time while preserving the comprehensive coverage requirements of the original queries.
2Quantity of substance
If complex multi-layer query structures are used in crowd-sourced polling, then comprehensive survey coverage is improved, but poll cost increases
Solution Approach 1:
By segmenting complex queries into modular sub-queries, the system can selectively execute only necessary segments based on budget constraints and priority levels, reducing overall poll cost while maintaining essential survey coverage.
Solution Approach 2:
The system dynamically adjusts execution parameters such as worker compensation rates, query complexity levels, and sampling sizes based on available budget, allowing comprehensive coverage to be achieved at optimized cost levels through parameter optimization rather than brute-force execution.
3Productivity
If unbalanced worker participation is allowed, then poll execution speed is improved, but survey accuracy deteriorates
Solution Approach 1:
The system implements feedback mechanisms that monitor worker participation balance in real-time and dynamically adjust task allocation, compensation, or query distribution to correct imbalances, thereby maintaining survey accuracy without significantly compromising execution speed.
Solution Approach 2:
The patent introduces dynamic worker participation models where worker selection, task assignment, and compensation are continuously adjusted based on real-time participation patterns, ensuring balanced representation across different worker groups while maintaining efficient poll execution through adaptive resource allocation.
4Productivity
If unbalanced worker participation is allowed, then poll execution speed is improved, but error rate increases
Solution Approach 1:
Real-time feedback monitoring detects participation imbalances that could lead to errors, and the system automatically corrects these imbalances by redistributing tasks or adjusting worker selection criteria, preventing error accumulation while maintaining overall execution speed.
Solution Approach 2:
The system performs preliminary validation and balancing of worker participation distributions before poll execution begins, identifying and correcting potential error sources in advance, thereby reducing error rates without requiring slower execution speeds during actual polling.
Data Source
AI summary
A “Poll Optimizer” provides automated techniques for performing various combinations of both static and runtime optimizations for crowd-sourced queries including, but not limited to, crowd-sourced opinion-based polls. These optimizations have been observed to improve poll performance by reducing factors such as completion times, monetary costs, and error rates of polls. In various implementations, the Poll Optimizer receives an input query representing a crowd-sourced poll that is formatted as a multi-layer structure (e.g., LINQ-based queries natively supported by .NET languages, JQL-based queries supported by JAVA, etc.). The Poll optimizer then iteratively reduces the multi-layer structure of the input query to construct a reformulated query. This reformulated query is then matched to an optimized execution process selected from a plurality of predefined execution processes. Finally, the reformulated query and matching optimized execution process are provided as an optimized version of the crowd-sourced poll for execution via a computer-based crowd-sourcing backend.