Social Graph Clustering for Data Analytics Efficiency
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
As data sets grow in size, existing technologies face challenges in efficiently processing and analyzing large datasets, particularly in identifying relevant clusters of documents and user collaborations within enterprise organizations, leading to inefficiencies in data analytics operations.
Innovation Solution
The method involves extracting a social graph from message metadata, identifying clusters of users, and grouping messages based on these clusters to improve data analytics, using modules for extraction, detection, and provisioning to provide actionable insights through a computing interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional data processing methods are used on large datasets, then data can be processed, but processing efficiency and speed deteriorate significantly
Solution Approach 1:
The patent segments large datasets into smaller clusters based on social graph communities. By dividing the data processing task into manageable clusters of messages and users, the system can process each cluster independently and in parallel, significantly improving processing efficiency and reducing overall processing time while maintaining analytical accuracy.
2Loss of information
If message bodies are parsed for analysis, then more comprehensive insights can be obtained, but processing speed and performance deteriorate
Solution Approach 1:
The patent extracts and utilizes only the essential metadata fields (sender, recipient, carbon copy, blind carbon copy address fields) from messages, eliminating the need to parse message bodies. This extraction approach maintains sufficient information for social graph construction and community detection while dramatically improving processing speed and performance.
3Measurement precision
If manual review of large datasets is performed, then detailed analysis can be conducted, but time and effort requirements increase significantly
Solution Approach 1:
The patent implements automated community detection algorithms that self-organize users and messages into clusters based on their communication patterns. The system performs self-service analysis by automatically identifying communities, detecting relationships, and organizing data without requiring manual review, thereby maintaining high analysis accuracy while eliminating time-consuming manual processes.
Data Source
AI summary
A computer-implemented method for clustering data to improve data analytics may include (1) extracting a social graph from a data set of messages, the social graph indicating messages as edges such that nodes of the edges indicate corresponding senders and recipients in sender-recipient relationships, (2) detecting communities of collaborators by identifying clusters of nodes within the social graph, (3) applying the identified clusters of nodes within the social graph to a grouping calculation to group the messages of the data set into groups of messages, and (4) providing, through a computing interface, results of a data analytics operation to an end user based at least in part on applying the identified clusters of nodes within the social graph to the grouping calculation. Various other methods, systems, and computer-readable media are also disclosed.