Parallel Smoothing Engine for Large-Scale Data Processing
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Solution Overview
Problem
Legacy implementations of time-series smoothing fail for large-scale datasets, running out of memory and being uncoupled from database engines, with step-to-step dependency preventing concurrent calculations, thus lacking the capability to exploit parallelism effectively.
Innovation Solution
A smoothing engine is integrated with a database engine and statistical packages, configuring master and worker tasks to divide data into chunks for parallel processing, allowing calculations to be independent and efficient, using map-reduce techniques to handle large-scale discrete datasets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If legacy implementations use sequential step-to-step dependency algorithms for time-series smoothing, then calculation accuracy is maintained, but parallel processing capability is prevented and processing speed deteriorates
Solution Approach 1:
The patent divides the time-series data into multiple independent chunks or segments that can be processed in parallel. Each chunk is processed independently to calculate state values, eliminating the sequential dependency while maintaining overall smoothing accuracy through subsequent aggregation of results.
Solution Approach 2:
The patent performs preliminary calculations of state values for each data chunk independently before final aggregation. This preliminary parallel processing allows subsequent steps to combine results without requiring sequential processing of entire datasets, significantly improving processing speed.
2Quantity of substance
If legacy implementations process large-scale datasets in memory, then complete data processing is achieved, but memory capacity is exceeded and system fails
Solution Approach 1:
The patent segments large-scale datasets into smaller manageable chunks that fit within available memory constraints. Each chunk is processed independently in parallel, allowing the system to handle datasets much larger than available memory by distributing processing across multiple partitions.
Solution Approach 2:
The patent introduces an intermediary processing layer that manages data chunks between storage and final output. This intermediary system coordinates parallel processing of data segments, enabling efficient handling of large datasets without requiring them to all reside in memory simultaneously.
3Adaptability or versatility
If legacy implementations are uncoupled from database engines, then algorithm independence is maintained, but integration capability deteriorates and data access efficiency is reduced
Solution Approach 1:
The patent designs a smoothing engine with universal interfaces that can work with multiple database engines and data formats. The engine maintains algorithmic independence while providing adaptable integration capabilities through standardized connection interfaces, allowing it to serve multiple functions across different data sources.
Solution Approach 2:
The patent introduces an intermediary smoothing engine layer between database engines and analytical applications. This intermediary provides standardized interfaces for data access and processing, enabling integration with multiple database systems without requiring changes to the core smoothing algorithms.
Data Source
AI summary
A system, and computer program product for large-scale data transformations. Embodiments include a smoothing engine within an R environment to configure at least one master task and at least two worker tasks. A chunk calculator receives a series of data values and divides the series of data values into portions of data values which are in turn assigned as workloads to at least two worker tasks. The worker tasks serve to calculate a first state value of a first one of the portions of data values, and calculate a second state value of a second one of the portions of data values. The workloads are selected such that calculating a second state value does not depend on the first state value. The results of the workload calculations are used to calculate a smoothing factor used to predict a trend.