Dynamic Data Extraction With Format-Based Stream Processing
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Solution Overview
Problem
Prior data analytics systems process data in batches, leading to inefficiencies and resource wastage due to unnecessary processing of files that do not require specific processes.
Innovation Solution
A dynamic extraction, distillation, and enhancement framework separates files into logical streams based on native format, identifies and extracts embedded objects, and deconstructs files into components, assigning them to corresponding streams for targeted processing using specifically tuned processors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If batch processing is used to process all files at once, then data processing can be performed in a unified manner, but processing time and resource usage increase due to unnecessary processing of files that do not require specific processes
Solution Approach 1:
The patent segments the batch of files into multiple streams based on their native formats (text, audio, video, image). Each stream is processed independently by format-specific processors, allowing parallel processing and avoiding unnecessary processing steps for each file type. This segmentation resolves the contradiction by maintaining unified batch processing benefits while eliminating redundant operations that waste time.
Solution Approach 2:
The system dynamically assigns files to different streams based on their detected native format, and dynamically selects appropriate processors for each stream. This dynamic adaptation allows the system to optimize processing pathways in real-time, improving efficiency without sacrificing the comprehensive processing capability of batch operations.
2Productivity
If all files are processed with the same processes, then consistency is maintained, but resource wastage occurs due to unnecessary processing of files that do not require specific processes
Solution Approach 1:
The patent applies different processing qualities and methods to different file types within the same batch. Each stream receives processing tailored to its specific format requirements - text files get text-specific processing, video files get video-specific processing, etc. This local quality approach ensures that resources are allocated efficiently to match the actual needs of each file type, resolving the contradiction between consistency and resource efficiency.
Solution Approach 2:
The system changes processing parameters based on file format detection. Different processors are invoked with format-appropriate parameters for each stream, optimizing resource utilization. This parameter adaptation allows the system to maintain consistent processing goals while adjusting implementation details to match specific file type requirements, reducing unnecessary resource consumption.
3Productivity
If files are separated into different streams based on format, then targeted processing improves efficiency, but system complexity increases due to multiple processors and streams
Solution Approach 1:
The patent creates a universal framework that handles multiple file formats through a common architecture. The stream assignment mechanism and processor selection logic serve as multi-functional components that work across all file types. This universality allows the system to achieve targeted processing efficiency while avoiding the complexity of completely separate processing systems for each format.
Solution Approach 2:
The system introduces stream assignment as an intermediary layer between file intake and processing. This mediator detects file formats and routes files to appropriate streams, simplifying the overall system architecture by providing a single point of decision-making rather than requiring direct complex interactions between all file types and all processors.
4Measurement precision
If embedded objects are extracted and assigned to corresponding streams, then data accuracy improves, but processing time increases due to additional extraction steps
Solution Approach 1:
The system performs preliminary extraction of embedded objects during the initial stream assignment phase, before main processing begins. By detecting and extracting embedded objects upfront and assigning them to their appropriate streams, the system ensures data accuracy is established early, while the extracted objects are ready for parallel processing without adding time to the critical processing path.
Data Source
AI summary
A dynamic data extraction, distillation, and enhancement system is disclosed that includes a dynamic extraction, distillation, and enhancement framework. The framework includes an allocator, extractor, and deconstructor stored in a non-transitory memory that, when executed by a processor, receive files in different formats from data sources, determine a native format of each file, identify and extract an embedded object from a file, deconstruct the file into components, assign each file to one of a plurality of streams based on the native format of the file, assign the embedded object to a stream based on a format of the embedded object, and assign a deconstructed component to a stream based on a format of the deconstructed component. The native format includes one of text, video, image, or audio. Each stream corresponds to one native format. The streams include a text stream, an audio stream, a video stream, and an image stream.