Location-Aware Data Storage for Compliant Large-Scale Analytics
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Solution Overview
Problem
Existing data storage and analytics systems fail to enforce location constraints due to the absence of location-aware design, leading to non-compliance with data privacy and security regulations that govern where data can be stored and analyzed.
Innovation Solution
A data storage and analytics service that uses programmatic interfaces, metadata, and interfaces to enforce location-aware storage and analysis service that implements location constraints on the storage and analysis of large data sets and on the storage of large data sets and computations performed to analyze the large data sets and on the computations performed to analyze the large data sets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is stored and processed in centralized storage repositories without location constraints, then efficient and accurate analysis of large volumes of data is enabled, but data privacy and security concerns arise regarding the locations at which data can be stored and processed
Solution Approach 1:
The patent segments the data storage system into location-specific storage units distributed across multiple geographical regions. Each segment stores data according to its associated location constraints, allowing the system to comply with data privacy regulations while maintaining efficient analysis capabilities through localized data processing.
Solution Approach 2:
The patent implements location-aware data storage where data is stored in specific geographical locations based on its origin and associated privacy constraints. This local quality approach ensures that data remains in permitted jurisdictions while enabling efficient analysis through location-specific processing units that operate on locally stored data.
2Reliability
If location constraints are enforced on data storage and processing, then data privacy and security compliance is improved, but network bandwidth and resource consumption increases due to data replication and distribution
Solution Approach 1:
The patent applies preliminary action by pre-determining the optimal storage locations for data based on location constraints before data is actually stored. This advance planning allows data to be placed in appropriate jurisdictions from the outset, eliminating the need for subsequent data movement or replication and reducing network bandwidth consumption.
Solution Approach 2:
The patent implements selective copying of data to multiple location-specific storage units only when necessary for compliance with different jurisdictional requirements. This controlled replication approach minimizes network bandwidth consumption by copying data only to the extent required for legal compliance rather than universal distribution.
3Reliability
If location-aware storage and analysis is implemented, then compliance with location constraints is ensured, but the complexity of workload management increases due to location-specific processing requirements
Solution Approach 1:
The patent implements self-service through automated location-aware routing mechanisms that automatically direct data processing workloads to appropriate location-specific computing resources. This automation eliminates the need for manual intervention in managing location constraints, allowing workload management to remain simple while ensuring compliance through system-autonomous decision-making.
Solution Approach 2:
The patent incorporates feedback mechanisms that continuously monitor data location and processing activities to ensure compliance with location constraints. This feedback system automatically adjusts workload routing and data placement decisions based on current compliance status, maintaining ease of operation while ensuring ongoing adherence to location-specific requirements.
Data Source
AI summary
A constraint on a location at which a portion of a data set can be stored is determined based on input received via a programmatic interface. The portion of the data set is stored at a location selected in accordance with the constraint. An analysis operation, whose input includes the portion of the data set, is performed at a set of computing resources selected from a plurality of resources based at least in part on their location.