Adaptive Parallel Data Processing for Document Conversion
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Solution Overview
Problem
Conventional data processing techniques become inefficient with large or complex data, leading to user frustration due to prolonged processing times, especially when converting documents from image formats to searchable and editable formats.
Innovation Solution
Adaptive parallel data processing techniques involve splitting data into smaller portions and sub-portions, which are processed in parallel across multiple nodes, allowing for dynamic redistribution and completion, thereby reducing overall processing time by leveraging nodes with faster processing times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If traditional sequential data processing techniques are used, then processing accuracy and completeness are maintained, but processing time increases significantly for large and complex documents
Solution Approach 1:
The patent divides a large data file into multiple portions and further splits remaining portions into sub-portions, distributing them across multiple nodes for parallel processing. This segmentation enables simultaneous processing of multiple data segments, dramatically reducing total processing time while maintaining complete data coverage through systematic reassignment of unfinished sub-portions.
2Productivity
If data is split into multiple portions and processed in parallel, then processing speed increases, but system complexity increases due to dynamic splitting and node coordination
Solution Approach 1:
The patent implements dynamic splitting of data portions into sub-portions based on real-time processing status. When a portion is completed, its remaining sub-portions are dynamically reassigned to available nodes. This dynamic adaptation allows the system to optimize resource utilization and maintain parallel processing efficiency without requiring complex predetermined scheduling algorithms.
Solution Approach 2:
The system continuously monitors processing completion status of each data portion and uses this feedback to dynamically reassign sub-portions to available nodes. This feedback mechanism enables automatic load balancing and ensures that all data portions are eventually processed without requiring manual intervention or complex centralized control.
3Ease of operation
If large documents are converted from image format to searchable format using traditional methods, then conversion accuracy is maintained, but user experience deteriorates due to lengthy processing times
Solution Approach 1:
The patent segments large image documents into multiple manageable portions for parallel conversion processing. By dividing the conversion task across multiple nodes simultaneously working on different portions, the system maintains accurate conversion quality while dramatically reducing the time users wait for document conversion to complete.
Data Source
AI summary
Adaptive parallel data processing techniques are described. In one or more embodiments, a request is received to process a data file. The data file is split into multiple portions and sent to multiple nodes, where each node is configured to process a respective portion of the data file. Responsive to an amount of processing of the data file being completed, at least one of the multiple portions of the data file is dynamically split into multiple sub-portions. The sub-portions are submitted to one or more of the multiple nodes for processing of the sub-portions.