Vector Modeling System for Distributed Data Transformation
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Solution Overview
Problem
Existing machine learning systems face difficulties in transforming data from distributed client devices into formats compatible with machine learning processes, lacking options to tailor transformations based on data types and machine learning processes, and are often incompatible with distributed data sets.
Innovation Solution
A vector modeling system that generates vectors by accessing and transforming data from distributed client devices, allowing for tailored transformation strategies and parallel data processing, using components like access, template, vector, mapping, storage, and learning components to convert data into suitable formats for machine learning analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data is transformed using integrated transformation methods, then data can be converted to machine learning format, but the system lacks flexibility to tailor transformations based on data types and machine learning processes
Solution Approach 1:
The patent segments the data transformation process into multiple independent transformation operations that can be selectively applied. Each transformation method handles specific data types or machine learning requirements, allowing the system to compose customized transformation pipelines rather than using a single integrated transformation approach.
Solution Approach 2:
The patent implements dynamic transformation strategies where the transformation approach is selected and configured based on the specific data characteristics and machine learning process requirements. This allows the system to adapt transformation methods in real-time rather than using static, pre-defined transformation rules.
2Quantity of substance
If data sets are distributed across multiple client devices, then data storage capacity increases, but existing transformation systems cannot directly access and transform distributed data
Solution Approach 1:
The patent introduces a server system as an intermediary that coordinates transformation operations across distributed client devices. The server generates and manages transformation code that client devices execute locally on their stored data, enabling transformation of distributed data without requiring centralized data collection.
Solution Approach 2:
The patent distributes transformation code copies to multiple client devices, allowing each device to perform transformations locally on its stored data. This eliminates the need to centralize data while still enabling comprehensive transformation of the distributed data set.
3Productivity
If data transformation is performed on distributed data sets, then machine learning analysis becomes possible, but transformation efficiency decreases due to distributed architecture
Solution Approach 1:
The patent divides the data transformation task into segments that are distributed across multiple client devices. Each device transforms its local data portion in parallel, reducing overall transformation time compared to sequential processing of centralized data.
Solution Approach 2:
The patent enables continuous transformation operations where multiple client devices simultaneously perform transformations on their respective data portions. This parallel continuous processing maintains high productivity across the distributed system without idle time between transformations.
Data Source
AI summary
In various example embodiments, a vector modeling system is configured to access a set of data distributed across client devices and stored in a structured format. The vector modeling system determines vector parameters and vector templates suitable for the set of data and transforms the set of data from the structured format into a second format including one or more vectors based on one or more transformation strategies. The vector modeling system stores the transformed data and performs machine learning analysis on the vector.