Federated In-Memory Database for Secure Cloud Data Processing
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Solution Overview
Problem
Current cloud computing architectures face challenges in efficiently processing sensitive data, such as patient data, due to data duplication and synchronization issues, which are problematic for large enterprises and research facilities bound by legal regulations, especially when dealing with big data like Next-Generation Sequencing data.
Innovation Solution
A federated in-memory database (FIMDB) system that keeps sensitive data locally while using a central computing infrastructure for processing, allowing only algorithms and minimal data to be transferred, thus eliminating the need for data duplication and synchronization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data is transferred from local systems to centralized cloud storage, then cloud computing resources can be utilized, but data transfer time increases significantly and data duplication occurs
Solution Approach 1:
The patent segments the centralized cloud storage architecture into distributed edge storage nodes. Instead of transferring all data to a central cloud location, data is distributed across multiple edge nodes that are geographically closer to the data sources. This segmentation reduces transfer distances and enables parallel data access, thereby reducing overall data transfer time while maintaining cloud computing resource utilization.
Solution Approach 2:
The patent introduces a spatial dimension to data storage by deploying edge computing nodes at multiple geographic locations rather than relying on a single centralized cloud data center. This dimensional change allows data to be stored and processed closer to its source, reducing the physical distance data must travel and thereby reducing transfer time while still providing access to cloud computing resources.
2Adaptability or versatility
If data is synchronized between multiple cloud service providers, then data sharing is enabled, but synchronization complexity and conflicts increase
Solution Approach 1:
The patent introduces edge computing nodes as intermediary layers between data sources and centralized cloud providers. These edge nodes act as local data hubs that manage data storage and initial processing, reducing the need for direct synchronization between multiple cloud providers. The edge nodes handle data localization and can resolve conflicts locally before syncing with the cloud, thereby reducing overall synchronization complexity.
Solution Approach 2:
The patent implements preliminary data processing and validation at the edge computing nodes before data is synced to centralized cloud storage. By performing data cleaning, validation, and conflict detection at the edge level before synchronization occurs, the system reduces the complexity of subsequent synchronization operations and minimizes synchronization conflicts.
3Reliability
If sensitive data is stored locally to maintain privacy, then data security is improved, but cloud-based processing capabilities are reduced
Solution Approach 1:
The patent applies local quality by enabling different processing capabilities at different locations in the architecture. Edge computing nodes deployed at local sites provide cloud-based processing capabilities where data security requirements are stringent, while centralized cloud data centers provide full processing power where data can be securely stored. This localized differentiation allows sensitive data to remain secure while still benefiting from cloud processing capabilities where appropriate.
Solution Approach 2:
The patent uses edge computing nodes as intermediaries that enable secure local processing of sensitive data. These edge nodes provide cloud-based computing resources and analytics capabilities directly at the data source, allowing sensitive data to be processed locally without being transferred to centralized cloud storage. This intermediary layer maintains data security while restoring cloud-based processing capabilities.
Data Source
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AI summary
The present invention relates to a federated in-memory database system (FIMDB) and a method for operating the same. The system comprises: - A plurality of interconnected computing sites (LS), each installed with a local in-memory database instance, which is configured to connect to a FIMDB landscape instance - A central computing infrastructure, provided by a service provider (SP), which grants access to algorithms managed by the service provider (SP) for execution on local data of the respective consuming computing site (LS) - Connection means (LSG1, LSG2), which are specifically adapted for connecting local and remote computing hard- and software via a digital communication channel - Configuration means (CM), which are specifically adapted for configuring local hard- and software to connect to the FIMDB system (FIMDB) and for configuring local hard-and software in order to access algorithms managed by the service provider (SP) and execute them on the local computing hardware.