Appliance Computer System for Domain-Specific Content Delivery
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Solution Overview
Problem
Current Web-scale repositories contain large amounts of unstructured information without domain-specific context, leading to high costs in custom text analytics application support and deployment, especially when accessing huge volumes of data directly or using limited mined data.
Innovation Solution
A system comprising a cluster computer system, a gateway computer system, and an appliance computer system that provides a sandboxed environment for users to leverage data integration, indexing, and pre-existing mining platform capabilities, allowing for the identification and delivery of domain-specific unstructured content through explicit and derived metadata, with features like throttling and compression to manage workloads.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If analytics applications access huge volumes of data by talking directly to the cluster, then the applications can retrieve comprehensive data, but the cost of application support and deployment becomes very high
Solution Approach 1:
The patent introduces an intermediary component (gateway or appliance) between the analytics application and the cluster. This intermediary handles data retrieval, filtering, and delivery, allowing applications to access comprehensive cluster data without bearing the full complexity and cost of direct cluster integration. The intermediary abstracts the data access complexity while maintaining access to large volumes of data.
2Device complexity
If standard database or data feed of limited mined data is used, then application deployment cost is reduced, but the data volume and analytical capability are limited
Solution Approach 1:
The patent creates a universal data delivery mechanism (gateway/appliance) that can serve multiple analytics applications simultaneously. This universal component provides access to comprehensive cluster data for different applications without requiring separate expensive integrations for each application, thus reducing individual deployment costs while maintaining access to large data volumes through shared infrastructure.
3Manufacturing precision
If domain-specific unstructured content is manually curated, then the content has high domain relevance, but the process is time-consuming and expensive
Solution Approach 1:
The patent implements self-service mechanisms where the system automatically performs domain-specific content identification, filtering, and delivery. The gateway or appliance autonomously queries the cluster for relevant unstructured content based on domain criteria, retrieves it, and makes it available to applications without manual intervention. This automated self-service maintains high domain relevance while eliminating the time and cost of manual curation.
Data Source
AI summary
A cost efficient solution for supporting and deploying custom text analytics applications suited is to provide third party application developers a sand-boxed application development environment such as an appliance computer system, allowing users to leverage data integration, indexing and pre-existing mining platform capabilities for a domain-specific data. Thus, embodiments herein present a system, method, etc. for identifying and delivering domain specific unstructured content for advanced business analysis. The system generally comprises a cluster computer system, a gateway computer system and an appliance computer system.