Parallel Database Gaussian Mixture Model Processing
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Solution Overview
Problem
Current database systems face limitations in processing speed due to hardware constraints, data storage methods, and restricted co-processing options, which hinder efficient execution of database functions.
Innovation Solution
The implementation of a parallelized database system with a K Nearest Neighbors (KNN) model that utilizes dimensionality reduction and model training during query execution, allowing for efficient processing and storage of large datasets across multiple computing devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If data is stored in traditional database systems, then data storage capacity is sufficient, but processing speed is limited due to hardware constraints
Solution Approach 1:
The patent segments the database system into multiple computing devices or nodes that work in parallel. Each node processes portions of the data independently, thereby increasing overall processing speed while distributing the hardware complexity across multiple simpler units rather than requiring a single complex system
2Speed
If traditional data storage methods are used, then storage simplicity is maintained, but execution speed is limited
Solution Approach 1:
The patent transitions from traditional single-dimension sequential processing to multi-dimensional parallel processing by organizing data and computing resources across multiple dimensions (multiple nodes, multiple processing threads). This dimensional expansion enables simultaneous execution of multiple operations, dramatically improving both execution speed and overall productivity
3Productivity
If co-processing options are restricted, then system simplicity is maintained, but processing capability is limited
Solution Approach 1:
The patent creates a universal parallel processing framework that can handle multiple types of data processing tasks across different computing nodes. The system is designed to be multi-functional, supporting various co-processing operations (filtering, aggregation, sorting, etc.) simultaneously, thereby enhancing processing capability while maintaining system coherence through standardized interfaces
Data Source
AI summary
A database system is operable to determine a first query that indicates a first request to generate a Gaussian mixture model. The first query is executed to generate Gaussian mixture model data for the Gaussian mixture model based on generating a training set of rows based on accessing a plurality of rows of a relational database table of a relational database, generating initial cluster parameter data, and performing an iterative process to generate final cluster parameter data by updating the initial cluster parameter data. A second query is executed to generate model output of the Gaussian mixture model for the input data based on the final cluster parameter data.