Entity Record Filtering for Faster ML Clustering Updates
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Solution Overview
Problem
Clustering entity records using machine learning models is computationally demanding and requires significant processing time, especially when dealing with large datasets, which can strain server resources and network connections.
Innovation Solution
Implement a filtering mechanism to reduce the input to machine learning models by identifying and communicating only changed entity records, using common fields with a threshold overlap to determine related entities, thereby reducing the data volume and computational load.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all entity records are input to the machine learning model for clustering, then clustering accuracy is maintained, but computational demand and processing time increase significantly
Solution Approach 1:
The system performs preliminary filtering of entity records before inputting them to the machine learning model. By identifying and selecting only changed entity records based on updates to their fields, the system reduces the input data volume while ensuring that records likely to affect clustering results are included. This preliminary action maintains clustering accuracy while significantly reducing processing time and computational demand.
Solution Approach 2:
The system extracts only the necessary subset of entity records that have undergone changes, rather than processing all records. By taking out only the changed records and their associated cluster information, the system eliminates redundant data processing while preserving the essential information needed for accurate clustering, thus resolving the contradiction between accuracy and processing speed.
2Loss of information
If all entity records are transmitted over the network to the machine learning model, then complete data is available for clustering, but network traffic and server resource consumption increase
Solution Approach 1:
The system extracts and transmits only the changed entity records and their associated cluster assignments over the network, rather than transmitting all entity records. This selective extraction maintains the essential information needed for clustering while dramatically reducing network traffic and server resource consumption, effectively resolving the contradiction between data completeness and energy loss.
3Reliability
If the machine learning model processes all entity records, then comprehensive clustering is achieved, but storage requirements and computational resources increase
Solution Approach 1:
The system performs preliminary identification of changed entity records and their cluster assignments before processing. By pre-filtering the data to include only records that have changed and their associated cluster information, the system reduces the volume of data that needs to be stored and processed while maintaining comprehensive clustering results, thus resolving the contradiction between clustering comprehensiveness and data volume.
Data Source
AI summary
In some implementations, the techniques may include accessing entity data that identifies one or more updates to a set of entity records, and ach entity record may include a plurality of fields. In addition, the techniques may include generating a graphical user interface that displays the plurality of fields. The techniques may include causing the graphical user interface to be displayed on a display device. Moreover, the techniques may include receiving information for identifying a subset of entity records from the set of entity records via the user interface. The information for identifying the subset of entity records may include information identifying one or more fields of the plurality of fields. Also, the techniques may include generating the subset of entity records from the set of entity records. Further, the techniques may include causing the subset of entity records be communicated to a computer system.