Crowd-Sensed Data Validation via Reputation Scoring
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional crowd-sensing systems face challenges in validating the reliability of data due to incorrect event reports, timing, location, and delayed submissions, necessitating solutions that consider vital parameters like participants' preferences for authenticating data management.
Innovation Solution
A method and system that receive crowd-sensed data, generate a data structure for aggregation, determine reputation scores and metadata for event reports, and prioritize their display based on user preferences and event types, enhancing data management and validation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If crowd-sensed data is collected from multiple participants, then the quantity of data increases, but the reliability of the data decreases due to incorrect event reports, wrong timing, and delayed submissions
Solution Approach 1:
The system implements feedback mechanisms by computing reputation scores for data sources based on their historical reporting accuracy and using this feedback to weight and validate new event reports. The reputation score is dynamically updated based on the verification status and accuracy of submitted data, creating a closed-loop system that continuously improves data reliability.
Solution Approach 2:
The patent introduces an intermediary validation layer that acts as a mediator between raw crowd-sensed data and the final event database. This intermediary system aggregates reports from multiple sources, cross-validates information, and filters out unreliable data before storing events, thereby maintaining reliability while processing large quantities of data.
2Loss of information
If all crowd-sensed data is processed and stored, then the completeness of information increases, but the complexity of data management increases
Solution Approach 1:
The system segments the data management process into distinct modules: data reception, aggregation, validation, reputation scoring, and storage. Each module handles a specific aspect of data processing, which reduces overall system complexity while maintaining complete information flow. The segmentation allows independent optimization of each component.
Solution Approach 2:
The patent applies preliminary action by pre-computing reputation scores for data sources and pre-establishing validation rules before data arrives. This preliminary preparation reduces the computational complexity during real-time data processing, as the system doesn't need to make complex decisions for each individual data point but can apply pre-determined criteria.
3Measurement precision
If reputation scores are computed for all data sources, then the accuracy of data validation improves, but the computational time increases
Solution Approach 1:
The system performs preliminary computation of reputation scores in advance, before actual data validation is needed. These pre-computed scores are stored and can be quickly retrieved during validation processes, significantly reducing the computational time required for real-time data verification while maintaining high validation precision.
Solution Approach 2:
The patent implements partial action by computing and updating reputation scores selectively rather than for all data sources simultaneously. The system focuses computational resources on data sources that are actively submitting events or show variations in reporting quality, reducing overall computational time while maintaining validation accuracy for critical sources.
Data Source
AI summary
The disclosed embodiments illustrate a method and a system for managing crowd-sensed data, associated with events occurring in a geographical area. The method includes receiving crowd-sensed data from one or more data sources, wherein the crowd-sensed data comprises one or more event reports associated with at least a type of each of one or more events reported by the one or more data sources. The method further includes generating a data structure based on an aggregation of the received crowd-sensed data. Further, the method includes determining first information and second information based on at least the generated data structure, a reputation score of each of the one or more data sources and metadata associated with each of the one or more event reports. The method further includes displaying at least the determined first information and second information based on at least a prioritization of the one or more events.