Real-Time Social Connection Aggregation via Sliding Window Processing
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Solution Overview
Problem
Current systems lack effective methods for real-time processing and analysis of data streams from social media and other sources to capture and aggregate relevant social information, such as connections between social elements, which is crucial for market intelligence and decision-making.
Innovation Solution
A system that includes a processor for analyzing data streams, a semantic analysis engine for identifying social elements and connections, and a data structure for storing these elements and connections over a rolling time window, enabling real-time aggregation and querying of social information, with a distributed processing platform for efficient data handling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-time processing of data streams is implemented, then the ability to capture evolving social connections is improved, but the computational complexity and resource requirements increase
Solution Approach 1:
The patent segments the data stream processing into discrete temporal windows (e.g., sliding windows of fixed duration). Each window is processed independently to identify social connections within that time frame, reducing the complexity of analyzing the entire continuous stream at once while maintaining real-time capture capability.
Solution Approach 2:
The system performs preliminary actions by pre-defining temporal windows and connection identification rules before processing the data stream. This allows the complex processing logic to be prepared in advance, reducing runtime computational complexity while maintaining accurate real-time connection detection.
2Loss of information
If comprehensive social elements and connections are aggregated over time, then the depth of market intelligence is improved, but the data storage and processing requirements increase
Solution Approach 1:
The patent extracts only the essential social connection information from the comprehensive data stream, storing specifically the identified connections and their temporal characteristics rather than retaining all raw data. This extraction approach maintains the completeness of social information while significantly reducing storage requirements.
Solution Approach 2:
The system applies partial action by focusing processing and storage on the most relevant social connections identified within each time window, rather than processing and storing all possible data. This selective approach ensures adequate market intelligence depth while controlling data quantity.
3Productivity
If distributed processing is implemented for real-time data streams, then the processing speed and scalability are improved, but the system architecture complexity increases
Solution Approach 1:
The patent segments the distributed processing system into independent processing nodes that each handle specific temporal windows or data subsets. This segmentation enables parallel processing across multiple nodes, improving overall processing speed and scalability while keeping individual node complexity manageable.
Solution Approach 2:
The distributed processing nodes are designed with universal functionality to perform the same connection identification algorithms on different data segments. This multi-functionality approach simplifies the overall system architecture by using identical processing logic across all nodes, reducing the complexity of coordinating diverse components.
Data Source
AI summary
Systems and methods are provided for processing a data stream in real time to identify connections between one or more social elements social elements and aggregating and storing the identified connections over a rolling time window. Social elements may be stored as nodes and connection between social elements may be stored as labeled connections between nodes. Other embodiments are provided.