The Role of Data Centricity in Smart , Connected Systems
Figure
2-1 : Message-centric architecture .
The outer blue circles each connect to a central hub and each contains a bit of communications logic shown in orange . Each added node complicates the architecture and impacts existing functionality . Message-centric architectures face challenges in systems that process much higher volumes of data at more rapid rates , while ensuring future flexibility to accommodate expansions , new technologies , and other unknown changes . Data centricity operates at a much more sophisticated level , capturing data in motion across subsystems to enable real-time communications for a new generation of AI-enabled systems . The data ’ s structure is formally defined with enough precision for a tool to generate the code which serializes the data on and off the databus , such that it is interoperable across different nodes ’ CPU architectures .
Figure
2-2 : Data-centric architecture .
Here , the nodes can easily inter-communicate via an abstract databus . Adding nodes is a simple linear extension of the databus and has minimal impact on existing behavior .
In DDS-based systems , data centricity is an abstraction for a software databus that is focused on the actual data , not the mechanics of its distribution nor the details of its transmission . To construct a data-centric system , developers first analyze and categorize what data is needed and
Journal of Innovation 5