Semantic Model for Distributed Cloud Storage Management
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Solution Overview
Problem
Managing distributed storage across geographically dispersed data centers poses challenges related to access times, processing costs, and load balancing, particularly due to varying access demands and geographical distances.
Innovation Solution
A method for storage resource management using semantic modeling in a cloud processing environment, where data is modeled according to a semantic model such as RDF or OWL, stored across multiple sites, and accessed to service queries efficiently, exceeding processing thresholds at local sites.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If data is stored and processed at local sites in a distributed cloud environment, then access speed and data availability are improved, but data consistency and coherence across geographically dispersed sites deteriorate
Solution Approach 1:
The patent introduces a semantic model as an intermediary layer that sits between the distributed data stores at different sites. This semantic model provides a unified view and coordination mechanism that enables fast local access while maintaining global data consistency through semantic relationships and constraints defined in the model.
Solution Approach 2:
The patent segments the data management system into autonomous local sites that can operate independently for fast access, while the semantic model provides the coordination framework. Each site can cache and process data locally without waiting for other sites, yet the semantic model ensures overall coherence across the distributed system.
2Reliability
If data is distributed across multiple geographically dispersed sites, then data availability and access localization are improved, but bandwidth consumption and processing costs increase
Solution Approach 1:
The patent applies local quality by allowing each distributed site to maintain local copies of data and make autonomous access decisions based on local conditions. The semantic model enables sites to determine what data can be accessed locally versus what requires remote access, optimizing bandwidth usage while maintaining data availability.
Solution Approach 2:
The semantic model performs preliminary action by pre-defining data relationships, constraints, and access patterns. This allows the system to predict and prepare data access operations in advance, reducing the need for frequent bandwidth-intensive communications while maintaining data availability across sites.
3Device complexity
If large amounts of data are processed at a single location, then processing simplicity is maintained, but processing capacity and access time performance deteriorate
Solution Approach 1:
The patent adds a semantic modeling dimension to the traditional data processing architecture. Instead of simply distributing data across multiple locations (spatial dimension), the semantic model provides a logical dimension that coordinates these distributed resources, enabling the system to maintain simplicity in the semantic layer while achieving high processing capacity through distributed execution.
Solution Approach 2:
The semantic model serves multiple functions simultaneously: it provides data definition, constraint enforcement, query optimization, and coordination across distributed sites. This multi-functionality allows the system to maintain a relatively simple processing architecture while achieving high processing capacity through the versatile semantic layer.
Data Source
AI summary
A system is provided for managing data sets in a cloud processing and/or federated environment. In an embodiment, the system described herein may be used in connection with cloud processing of big data sets. The term “big data,” as used herein, may be generally defined to describe data sets so large and complex that they become difficult to work with using on-hand database management tools. The system described herein enables the persistent storage of semantic technology statements for big data sets for processing in a cloud processing and/or federated environment.