DDS-DBMS Bridge for Seamless Data Integration
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Solution Overview
Problem
Current technologies do not integrate Relational Database Management Systems (DBMS) and Data Distribution Service (DDS) seamlessly, limiting the ability to automatically store and access data across both systems via SQL or DDS interfaces, and vice versa.
Innovation Solution
A bridge is established to map the global data space representation between DDS and DBMS, utilizing bridge components for data translation and management, enabling automatic data caching, persistence, distribution, and transfer between disparate DBMS systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If data is distributed using DDS, then data distribution performance and speed are improved, but automatic storage and access via DBMS SQL interfaces is lost
Solution Approach 1:
A bridge component acts as an intermediary between DDS and DBMS systems. The bridge-DDS-DBMS-pub component subscribes to DDS topics and automatically stores published data in the DBMS, while the bridge-DBMS-DDS-sub component enables SQL-based access to distributed data through the DBMS interface, thus maintaining both high-speed DDS distribution and ease of DBMS access.
Solution Approach 2:
The bridge system provides multi-functionality by simultaneously supporting DDS publish/subscribe operations and DBMS SQL access operations. It enables the same data to be accessed through multiple interfaces (DDS APIs and SQL interfaces) without sacrificing the performance benefits of either system.
2Reliability
If data is stored in DBMS, then data persistence and structured management are improved, but automatic distribution via DDS is lost
Solution Approach 1:
The bridge-DDS-DBMS-sub component acts as an intermediary that monitors the DBMS for data changes and automatically publishes updated data through DDS topics. This enables persistent DBMS storage to automatically trigger distributed data propagation without requiring manual intervention or complex integration logic.
Solution Approach 2:
The bridge system implements feedback mechanisms where DBMS data changes are detected and trigger automated DDS publication events. This feedback loop ensures that persistent storage operations automatically result in distributed data updates, maintaining synchronization between local and distributed data copies.
3Adaptability or versatility
If bridge components are added to integrate DDS and DBMS, then data access versatility is improved, but system complexity increases
Solution Approach 1:
The integration system is segmented into distinct, independently deployable bridge components: bridge-DDS-DBMS-pub for publishing DDS data to DBMS, bridge-DDS-DBMS-sub for storing subscribed DDS data in DBMS, bridge-DBMS-DDS-pub for distributing DBMS changes via DDS, and bridge-DBMS-DDS-sub for updating DBMS from DDS subscriptions. This modular segmentation allows selective deployment based on specific needs, managing overall system complexity.
Solution Approach 2:
The bridge components serve as lightweight intermediaries that handle the complexity of protocol translation and data mapping between DDS and DBMS. By isolating integration logic in these intermediary components, the complexity is contained and does not propagate to the core DDS or DBMS systems, maintaining their individual simplicity while achieving integrated functionality.
Data Source
AI summary
A bridge to integrate representation of global data space in Data Distribution Service (DDS) and Data Management Systems (DBMS). The DDS concept of a keyed topic and a type is mapped to the DBMS notion of a keyed table and schema representing data-object instances. Rules are specified for translating between a DBMS table record and the DDS wire format representation. Four bridge components are possible: Bridge-DDS-DBMS-Publication to store outgoing published data; Bridge-DDSDBMS-Subscription to store incoming subscribed data; Bridge-DBMS-DDS-Publication to publish changes to a DBMS table; and Bridge-DBMS-DDS-Subscription to update a DBMS Table based on received data subscriptions from the network. Mechanisms are provided for preventing publication of data seen by DDS, and for preventing application of changes already made in a DBMS table. Use cases include: data caching in a database; automatic persistency of all (or selected) global data-objects; distributing database contents; and multi-vendor database content transfer.