This architecture also must be able to leverage data-response processing, as well
as the cognitive computing that exists at the network edge and is therefore
closer to the sensors and devices that are being monitored and controlled. Furthermore, for a more responsive use of IoT data, the architecture must bind data
directly to rules, policies, and behaviors and be able to drive desired behavior
back to the devices (for example, self-healing issues with a jet engine in flight).
In addition, such a system should provide automated learning of data patterns to
automatically augment the rules, policies, and behaviors (for instance, the ability
to determine the likelihood of a jet engine failure through shared learning models that have experienced thousands of failures and patterns leading up to them).
Moreover, we need to leverage common security and governance procedures
and models, as well as common management and usage-based management
using inexpensive commodity cloud and non-cloud technology. Finally, this
architecture might use containers to encapsulate architecture components.
The resulting benefits will be high-performance data processing, direct behavior interactions, and automated learning that will constantly improve the IoT
system’s value.
Physical Database Layer
Devices
Data Response,
Process, Behavior
Learning
Model
Physical or Virtual
Resource, Public Cloud,
Private Cloud
Virtual
Databases
Security & Governance
Micro Services
Service/ API
Mgmt. &
Development
Use-Based Resource Tracking
Applications
Communications Bindings
Consumer Pool
Abstract Data
Bound to PreDefined Behavior
Network Edge
Devices
Logical Data
Binding
Physical
Databases
OLTP
Analytical
External
Device
Data
Special
Purpose
Figure 1: Responsive Data Architecture for the Internet of Things and the
public cloud. OLTP (Online Transaction Processing)
An RDA (shown in Figure 1) uses a common set of SQL and nonSQL (object) data
base technology to provide transactional online transaction processing; OLTP)
FALL 2016 | THE DOPPLER | 47