Data Processing Framework for Real-Time Error Identification
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Solution Overview
Problem
Conventional data processing techniques require substantial processing overhead as they import, parse, and then analyze data for errors, often identifying corrupted data only after it has been imported and analyzed, which can lead to malfunctioning or crashing end-user applications.
Innovation Solution
A data processing framework that imports data from distributed data stores, parses it in real-time, and validates it using a global shared library with rule-based configuration, identifying errors such as invalid or empty columns while data is being imported, thereby reducing processing time and overhead by performing operations in parallel.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional sequential data cleaning techniques are used to ensure data compliance, then data validity requirements are met, but processing time and computational overhead increase significantly
Solution Approach 1:
The patent applies preliminary action by performing data parsing and validation operations before data is fully imported into the target system. The validation layer checks data compliance with schema rules during the import process itself, rather than after completion. This early detection and filtering of invalid data prevents subsequent processing delays and application errors, thereby maintaining data validity while reducing overall processing time.
Solution Approach 2:
The patent segments the data processing workflow into distinct layers: a parsing layer that breaks down data structures, and a validation layer that independently checks compliance with schema rules. This segmentation allows parallel processing of different data aspects simultaneously, reducing sequential processing overhead while ensuring comprehensive validity checking through specialized validation rules.
2Reliability
If data is imported and analyzed before error identification, then complete data is available for processing, but application malfunctions or crashes may occur due to corrupted data
Solution Approach 1:
The validation layer performs preliminary checks on data compliance with schema rules before the data is fully imported and used by applications. By validating data structure, data types, and constraint compliance during the import process, the system identifies and isolates corrupted or invalid data records beforehand, preventing them from causing application malfunctions or crashes while maintaining data integrity.
Solution Approach 2:
The patent implements preliminary anti-action by proactively detecting and flagging invalid data patterns that could lead to application failures. The validation rules preemptively identify corrupted data by checking against predefined schema constraints, and the system can reject or quarantine these problematic records before they reach the application layer, thereby preventing potential crashes and maintaining application stability.
3Reliability
If sequential operations are performed for data cleaning, then thorough validation is achieved, but processing overhead and computational resources increase
Solution Approach 1:
The patent divides data validation into segmented operations across different layers: the parsing layer handles structural decomposition while the validation layer independently checks schema compliance. This segmentation allows certain validation checks to be performed in parallel during data import, reducing sequential processing overhead and computational resource consumption while maintaining thorough validation coverage through layered verification.
Solution Approach 2:
The validation layer applies partial validation by focusing computational resources on checking only the most critical schema constraints and data types during import, rather than performing exhaustive validation on every data attribute simultaneously. This selective validation approach reduces immediate computational overhead while still ensuring data compliance for the most important validation rules, with optional deeper validation available if needed.
Data Source
AI summary
A data processing framework suitable for use with data stores is described. The data processing framework identifies which data stores are storing corrupt or invalid data that may cause end user software applications to malfunction or crash altogether. Data parsing, validation and transformation may be performed as parts of a single operation to increase speed and reduce computing overhead.