Parallel Gene Sequence Classification via Map-Reduce Aggregation
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Solution Overview
Problem
Current systems for machine learning and classification of gene sequence data lack the ability to distribute processing across multiple processors and disks, require rigid frameworks, and are inefficient in storage and processing power, leading to high overhead in data pre-processing and less-than-desirable classification times.
Innovation Solution
A system and method for parallel processing of gene sequence data using map reduction aggregation methods, including chunking, mapping, locality sensitive hashing, and reduction operations, allowing for application-specific methods and flexible classification metrics, and distributing data across multiple processors and disks to reduce storage and processing requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If current systems use rigid frameworks with single predefined aggregation methods, then system simplicity is maintained, but adaptability and versatility are reduced
Solution Approach 1:
The system transitions from rigid predefined aggregation methods to dynamic, user-configurable aggregation methods. The framework allows programmers to define custom aggregation methods and classification metric functions, enabling the system to adapt to different application requirements while maintaining a structured processing pipeline.
Solution Approach 2:
The patent creates a universal framework that can handle multiple types of data aggregation and classification tasks through a single configurable system. The map-reduce architecture with customizable aggregation methods allows the same system structure to serve multiple classification purposes, from gene sequence analysis to other data classification problems.
2Productivity
If data is processed in centralized systems with limited storage, then storage requirements are constrained, but processing power and classification accuracy deteriorate
Solution Approach 1:
The patent divides the data processing task into multiple segments that can be processed in parallel across distributed systems. The map-reduce framework splits the classification workload into independent map tasks that can run simultaneously on multiple processors, then aggregates results through reduce operations, enabling scalable processing without requiring centralized storage of all data.
Solution Approach 2:
The system transitions from single-processor sequential processing to multi-processor parallel processing by adding the dimension of distributed computation. The map-reduce architecture enables data to be processed across multiple dimensions of computing resources, allowing the system to scale processing power by adding more processors rather than increasing storage capacity.
3Ease of operation
If extensive data pre-processing is performed to transform gene sequence data, then data compatibility is improved, but processing time and overhead increase
Solution Approach 1:
The system performs preliminary data transformation and feature extraction during the map phase of the map-reduce framework, converting raw gene sequence data into a standardized format suitable for aggregation and classification. This preliminary action ensures data compatibility is achieved as part of the parallel processing workflow rather than as a separate pre-processing step, reducing overall processing time.
Data Source
AI summary
The present invention relates in general to the field of parallel data processing, and more particularly to machine learning and classification of extremely large volumes of unstructured gene sequence data using Collaborative Analytics Gene Sequence Classification Learning Systems and Methods.