Distributed Document Processing with Location-Aware File Systems
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Solution Overview
Problem
Current search engine technologies face delays and resource burdens in organizing and cataloging vast numbers of web-based documents, leading to slow search query responses and inefficient use of processing resources.
Innovation Solution
A computerized workflow manager initiates a method for knowledge extraction in distributed processing nodes using a location-aware distributed file system like Apache Hadoop, allowing filtering and allocation of documents based on geographic location, reducing the need for long-distance data transport and optimizing resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If standard workflow tools are used to analyze and catalog web-based documents, then document organization and categorization can be achieved, but the processing speed is unacceptably slow and consumes enormous processing resources
Solution Approach 1:
The patent divides the large corpus of web-based documents into smaller batches or segments that can be processed in parallel across multiple computing nodes. This segmentation enables the system to handle vast document volumes without overwhelming single processing units, thereby improving overall processing speed while distributing resource consumption across the cluster.
Solution Approach 2:
The system performs preliminary indexing and categorization of web-based documents before search queries are submitted. By pre-processing and organizing documents into structured formats with extracted entities and relationships stored in advance, the system eliminates the need for real-time analysis during query processing, significantly reducing response delays and resource usage during peak search periods.
2Reliability
If real-time searching of huge numbers of web-based documents is performed in response to individually submitted queries, then search results can be provided, but an extraordinary burden is placed on search engine resources
Solution Approach 1:
The system performs preliminary extraction of entities, relationships, and structured information from web-based documents and stores them in an organized format before search queries arrive. This pre-processing creates a ready-to-query knowledge base that can be rapidly searched without requiring intensive real-time computation, thereby maintaining search accuracy while dramatically reducing resource burden during query processing.
Solution Approach 2:
The system creates structured copies of document content in standardized formats with extracted entities and relationships during the indexing phase. These pre-processed copies are stored separately from the original documents and can be efficiently queried without accessing or re-processing the full original documents, reducing the computational burden during search operations while maintaining result accuracy.
3Reliability
If cataloging and analyzing of web-based documents is performed continuously to accommodate expanding document corpora, then document relevance and timeliness are maintained, but enormous processing resources and considerable time expenditure are required
Solution Approach 1:
The system performs preliminary categorization and entity extraction from newly added or revised web-based documents as they are ingested, organizing them into structured formats with identified entities, relationships, and metadata. This advance processing ensures that new documents are immediately searchable and relevant upon addition to the corpus, eliminating delays while reducing the time burden of continuous cataloging through automated pre-processing.
Data Source
AI summary
Briefly, the disclosure describes embodiments of methods or apparatuses for document processing at distributed processing nodes.