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The proposed big data pipeline employs an open and extendable system architecture that is capable of indiscriminately interacting with legacy and smart devices on automation networks, as well as exposing a common data interface for industrial analytics applications to consume measured data.
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The Unstructured Information Management Architecture UIMAA) (Ferruci, 2006) is a framework for integrating such tools into a common data representation and interface.
Common data structures and interfaces will allow developers and end users of additive manufacturing technologies to simplify, coordinate, validate, and verify end-to-end digital implementations.
Another framework, the Unstructured Information Management Architecture UIMAA) (3), promotes the interoperability of data processing components by defining common data structures and interfaces.
The well established Unstructured Information Management Architecture is a framework that addresses interoperability by defining common data structures and interfaces.
The architecture is an OASIS standard (http://www.oasis-open.org/committees/uima) for ensuring interoperability of individual processing components by defining common data structures and interfaces.
This paper describes the implementation of a highly flexible, pluggable and distributed architecture solution, focusing on several building blocks, particularly a distributed middleware, a common data model and standard interfaces and technological adapters, which can be used for connecting legacy systems (such as databases) with simulation, visualization and reconfiguration tools.
The Application Programming Interface, written in Java, enables rapid tool creation by providing a robust, pluggable programming interface and common data model.
Tools of the CADDSuite share a common binary interface, a common data exchange format and thus easily integrate into distributed computing environments.
Here we describe the CPCTR information management system architecture, common data element (CDE) development, query interfaces, data curation, and quality control.
This involves extending the caTRIP interface and distributed query engine to support aggregation of data from services that expose common data elements.
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