Social Network Graph Sensor Data Analytics
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Solution Overview
Problem
Current IoT systems lack a method to effectively connect and analyze sensor data using social networking structures, leading to inefficient data mining and analytics, as they fail to establish relationships between sensors based on user-defined criteria, resulting in unnecessary data processing and reduced accuracy in socially-centered applications.
Innovation Solution
A system and method that utilizes social networking graphs to connect relevant sensors based on familiarity and common interests, creating a reduced sensor data set for efficient analytics by establishing relationships through user profiles, data transformation, and logical interpretation using an analytical engine.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If all sensor data is collected and processed for analytics, then completeness of data is improved, but data processing complexity and time increase
Solution Approach 1:
The patent segments the vast sensor data into meaningful groups by creating social networks of sensors based on spatial proximity, temporal correlation, and functional relationships. This segmentation allows the analytics system to process only relevant sensor groups rather than all sensors, reducing processing complexity while maintaining data completeness for each analytical context.
Solution Approach 2:
The patent extracts and removes irrelevant sensor data from the processing pipeline by using social network relationships to identify and exclude sensors that are not relevant to the current analytical context. This extraction principle reduces the data volume requiring processing while preserving all necessary information for accurate analytics.
2Measurement precision
If sensor relationships are established based on user-defined criteria, then analytics accuracy is improved, but system complexity increases
Solution Approach 1:
The patent implements a universal social network framework that handles multiple types of sensor relationships (spatial, temporal, functional) through a single unified system. This multi-functional approach allows the system to establish diverse sensor relationships using common infrastructure, improving analytics accuracy across different application domains without proportionally increasing system complexity.
Solution Approach 2:
The patent enables dynamic adjustment of relationship-establishment parameters such as spatial proximity thresholds, temporal correlation windows, and confidence levels. These parameter changes allow the system to adapt to different analytical requirements and data characteristics, improving accuracy for specific use cases while maintaining manageable system complexity through configurable rather than hard-coded relationships.
3Productivity
If social networking structure is applied to sensor networks, then data mining efficiency is improved, but implementation difficulty increases
Solution Approach 1:
The patent introduces social network theory as an intermediary layer between raw sensor data and analytics processing. This intermediary framework provides standardized mechanisms for establishing sensor relationships, managing data flows, and performing analytics, thereby improving data mining efficiency while reducing implementation difficulty through proven social networking patterns rather than requiring novel sensor-specific solutions.
Data Source
AI summary
The present invention relates to a system and method of effective physical data aggregation and its logical analytics by way of utilizing socially interacting and networking platforms to create meaningful association and relevancy between the captured physical data. The physical data associated by social networking platforms results in creation of reduced data set for analytics and requires low processing requirements for application usage.