Workforce Data Integration via Parallel Block Processing
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Solution Overview
Problem
Existing methods for integrating workforce information across multiple business units are inefficient, non-standard, and insecure, often resulting in redundancy, data inaccuracy, and poor performance due to latency and security risks.
Innovation Solution
A computerized method that processes workforce information by retrieving data blocks from a repository, applying business rules sequentially and in parallel, and transmitting them securely, without requiring external systems or internet transmission, using a computing device to manage and encrypt the data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If workforce information is transmitted via web service for rule application, then business rules can be applied to standardized data, but network latency increases and security risks arise from transmitting un-encrypted data
Solution Approach 1:
The patent segments the workforce information into discrete data blocks that are processed in parallel, eliminating the need for sequential web service transmissions. This allows multiple data blocks to be processed simultaneously, reducing overall latency while maintaining standardization through the structured block format.
Solution Approach 2:
The patent applies business rules locally on data blocks before transmission, rather than requiring external web service processing. This preliminary action eliminates network round-trips for rule application, significantly reducing latency while data is already in standardized format.
2Reliability
If workforce information is transmitted via web service for rule application, then business rules can be applied to standardized data, but security risks increase due to un-encrypted data transmission
Solution Approach 1:
The system performs self-service encryption on data blocks before processing, eliminating the need for external web service intervention. Each data block is encrypted locally using secure algorithms, ensuring data protection without requiring vulnerable web service transmissions. This self-service approach maintains standardization while securing data throughout the processing pipeline.
3Reliability
If business rules are applied to large datasets sequentially, then complete rule execution is achieved, but processing time exceeds service level agreements
Solution Approach 1:
The patent divides large datasets into smaller data blocks that can be processed in parallel. This segmentation allows multiple rule applications to occur simultaneously on different data blocks, maintaining complete rule execution coverage while dramatically reducing total processing time to meet service level agreements.
Solution Approach 2:
The system implements continuous parallel processing where multiple data blocks are processed simultaneously without interruption. This continuous useful action eliminates idle time between sequential operations, maintaining 100% rule execution completeness while maximizing processing throughput to meet performance targets.
4Reliability
If XML files are generated and processed for rule application, then workforce information can be standardized, but file generation delays processing and security breaches are possible
Solution Approach 1:
The patent extracts the XML file generation step from the processing workflow, eliminating it entirely in favor of direct data block processing. This extraction removes the delay caused by file generation while maintaining standardization through the structured data block format that requires no external file system operations.
5Reliability
If serial processing approach is used for large XML files, then complete data processing is achieved, but resource idling increases as file size grows
Solution Approach 1:
The patent segments the serial processing approach into parallel data block processing. Instead of processing one large XML file sequentially, the system divides data into blocks that can be processed simultaneously on different resources. This maintains complete data processing while eliminating resource idling through concurrent execution.
Solution Approach 2:
The system transitions from single-threaded serial processing to multi-threaded parallel processing, adding the time dimension to resource utilization. Multiple resources work simultaneously on different data blocks, converting what would be sequential resource idling into parallel productive work, thereby maximizing resource utilization while maintaining processing completeness.
Data Source
AI summary
A computerized-method of integrating workforce information data across multiple business units is provided. Workforce information for each business unit is parsed into discrete sized data blocks. There are multiple steps to processing each data block, and the steps are executed sequentially on each data block and in parallel with processing of other data blocks.