Map Reduction Aggregation for Parallel Data Classification
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Solution Overview
Problem
Current machine learning and classification systems lack the ability to automatically distribute learning and classification processes across multiple processors and disks in a distributed parallel computing environment, requiring extensive pre-processing and supporting only a single predefined aggregation method and classification metric function, which limits their flexibility and efficiency in handling large volumes of structured and unstructured data.
Innovation Solution
The Collaborative Analytics Map Reduction Classification Learning System enables rapid parallel processing by allowing programmers to create and deploy application-specific map reduction aggregation methods and classification metric functions, dissecting data into independent units for simultaneous processing, and using locality sensitive hashing to reduce storage requirements, while supporting multiple stages of mapping and reduction for increased parallelism.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a rigid framework with single predefined aggregation method is used, then system simplicity is maintained, but flexibility and adaptability are reduced
Solution Approach 1:
The system segments the aggregation process into separate, independently configurable aggregation functions that can be selected and combined flexibly. Each aggregation method (e.g., SUM, COUNT, AVG) is implemented as a distinct function that can be applied to different data groups, allowing the system to adapt to various analytical needs without requiring a complete system redesign.
Solution Approach 2:
The system implements dynamic configuration where aggregation methods and classification metrics can be changed at runtime based on specific analytical requirements. The framework allows programmers to define and switch between different aggregation functions and classification metrics without recompiling or reconfiguring the entire system, enabling flexible adaptation to changing data analysis needs.
2Productivity
If extensive pre-processing is performed to transform targeted data, then data compatibility with rigid framework is improved, but processing time and computational overhead increase
Solution Approach 1:
The system performs preliminary actions by pre-defining multiple aggregation functions and classification metric functions that can be directly applied to raw data without extensive transformation. The framework includes built-in support for various data types and structures, allowing data to be processed in its original format while still achieving compatibility through the selection of appropriate pre-defined functions.
Solution Approach 2:
The system changes parameters by allowing dynamic selection of aggregation methods and classification metrics based on data characteristics. Instead of transforming data to fit a single rigid framework, the system adapts the processing parameters (aggregation function, metric type) to match the data format and analytical requirements, eliminating the need for extensive pre-processing transformations.
3Productivity
If map reduction aggregation method is used for parallel processing, then processing efficiency is improved, but memory usage and computational overhead increase
Solution Approach 1:
The system extracts only the essential data elements needed for aggregation and classification, processing and discarding intermediate results as they are generated. The map reduction implementation extracts key features and aggregates them incrementally, avoiding the need to store complete datasets in memory during processing. This extraction approach maintains parallel processing efficiency while reducing memory requirements.
Solution Approach 2:
The system performs partial aggregation actions at each processing stage, computing only the necessary intermediate results needed for the final classification. Rather than computing all possible aggregations, the framework applies selective aggregation functions that produce sufficient statistics for classification decisions, reducing the total computational workload and memory requirements while maintaining processing efficiency.
Data Source
AI summary
Disclosed herein are systems and methods for data learning and classification for rapidly processing extremely large volumes of input data using one or more computing devices, that are application and platform independent, participating in a distributed parallel processing environment. In one embodiments, a system may comprise a plurality of parallel Map Reduction Aggregation Processors operating on the one or more computing devices, and configured to receive different sets of input data for data aggregation. Each of the Map Reduction Aggregation Processors may comprise one or more parallel Mapping Operation Modules configured to consistently dissect the input data into individual intermediate units of mapping outputs comprising consistently mapped data keys, and any values related to mapped data keys, conducive to simultaneous parallel reduction processing; and one or more parallel Reduction Operation Modules configured to continually and simultaneously consume the mapping outputs by eliminating the matching keys and aggregating values consistent with a specified reduction operation for all matching keys that are encountered during consumption of the mapping outputs. The system may also include an application-specific Classification Metric Function Operations Module operating on the one or more computing devices and configured to receive reduction outputs from the Reduction Operations Modules to determine distance and/or similarity between each of the different sets of input data with respect to one or more data classification categories using one or more distance and/or similarity calculations.