Distributed Enterprise Search Resource Discovery via Classifier Sharing
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Solution Overview
Problem
Centralized enterprise search systems face challenges such as administrators' burden in maintaining relevance and timeliness of search results, duplication of effort due to delayed content indexing, proliferation of local search systems, and difficulty in ensuring content availability across the enterprise.
Innovation Solution
Personal data repositories are created by users, allowing them to generate and share classifiers for resource categorization, enabling timely and relevant content indexing and discovery across distributed systems without transferring content, while maintaining security and administrative control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If a centralized enterprise search system is used, then content can be collected and indexed from the enterprise network, but administrators cannot easily determine whether specific documents should be included in the index and must spend most of their time maintaining system operation
Solution Approach 1:
The patent enables content owners to automatically register their own content with the enterprise search system by providing registration interfaces that allow them to submit content metadata, keywords, and indexing parameters themselves, eliminating the need for administrators to manually curate and maintain the index
Solution Approach 2:
The system divides the content management responsibility into segments: content owners manage their own content registration and updates, while the enterprise search system handles the indexing and search operations, distributing the administrative burden across multiple stakeholders
2Reliability
If a centralized spider-based indexing system is used, then content can be collected from the enterprise network, but the index becomes out-of-date quickly with dead links and delayed inclusion of new content
Solution Approach 1:
Content owners register their content in advance with the enterprise search system, providing metadata and keywords before the content is actually published or updated, allowing the index to be prepared and updated proactively rather than reactively
Solution Approach 2:
The system implements feedback mechanisms where content owners are notified when their content is successfully indexed or when indexing issues occur, allowing them to correct problems and update their content registrations to maintain index accuracy
3Loss of time
If local search systems are deployed to find timely content, then search results become more up-to-date, but the burden of system administration moves to content creators and multiple search technologies proliferate
Solution Approach 1:
The enterprise search system provides a universal platform that can handle diverse content types and search requirements through a single interface, allowing content owners to register various types of content (documents, web pages, databases) using the same registration and indexing mechanisms
Solution Approach 2:
The enterprise search system acts as an intermediary layer between local content sources and end users, receiving content registrations from content owners, processing the indexing, and returning search results, thereby eliminating the need for separate local search systems at each content location
4Reliability
If content is placed on web servers to ensure availability in the enterprise search system, then content can be accessed by the spider, but this approach does not ensure timely inclusion in the index
Solution Approach 1:
Content owners register their content with the enterprise search system in advance, providing metadata, keywords, and location information before the content is made available on web servers, so that when the content is published, it is already known to the search system and can be indexed immediately
Data Source
AI summary
A method for searching and resource discovery in a distributed enterprise (DE), the method including: generating a first classifier for a first repository in the DE; generating a second classifier for a second repository in the DE, where the second classifier has a vector element identifying a location of the second repository; submitting a copy of the second classifier to a web server of the first repository; obtaining a resource at the first repository after submitting the copy of the second classifier; matching the resource to the copy of a second classifier; sending a copy of the resource to the second repository using the vector element; and storing the copy of the resource in the second repository.