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Robot Programming
Published in Marina Indri, Roberto Oboe, Mechatronics and Robotics, 2020
Christian Schlegel, Dennis Stampfer, Alex Lotz, Matthias Lutz
Further important principles are composability, compositionality, and composition. Composability is the ability to (re-)combine as-is building blocks into different systems for different purposes. It requires that properties of sub-systems are invariant (“remain satisfied”) under composition. Splittability is the inverse relationship of composability. Compositionality requires that the behavior of a system is predictable from its sub-systems and from that of the composition operators. System composition is the activity of putting together a set of existing building blocks to match system needs with a focus on flexible (re-)combination (just like putting Lego bricks together). In contrast, system integration is the activity that requires effort to combine components, requiring modifications or additional actions to make them work together. Once integrated, it is difficult to put them apart again. For example, resource shares are composable, as already assigned resource shares are not affected when the remaining resource shares get assigned. Constraints are also composable.
Concepts of Time-Triggered Communication
Published in Richard Zurawski, Industrial Communication Technology Handbook, 2017
In many engineering disciplines, large systems are built from prefabricated components with known and validated properties. Components are connected via stable, understandable, and standardized interfaces. The system engineer has knowledge about the global properties of the components—as they relate to the system functions—and of the detailed specification of the component interfaces. Knowledge about the internal design and implementation of the components is neither needed nor available in many cases. Composability deals with all issues that relate to the component-based design of large systems. Composability refers to an architectural framework that supports the smooth integration and reuse of independently developed components in order to increase the level of abstraction in the design process. Architecture instantiations that are derived from a generic platform must support the constructive composition of large systems out of components and subsystems without uncontrolled emerging behavior or side effect. Composability is a concept that relates to the ease of building systems out of subsystems [ART06, p. 24]. An example is the deadlock-freedom of a component-based system.
Universal IoT Framework
Published in Monideepa Roy, Pushpendu Kar, Sujoy Datta, Interoperability in IoT for Smart Systems, 2020
There are typically two types of architectures in IoT that are suitable for the implementation of all IoT-based applications. Those are service-oriented architecture (SOA) and API-oriented architecture [2]. SOA ensures interoperability between heterogeneous devices. In generic SOA, we generally have four layers, with distinguished functionalities. At first, a sensing layer that is combined with the hardware artifacts available to detect the status of the actual deployed devices. Secondly, the network layer, which is the infrastructure to enable connection through either wireless or wired links between the devices. Thirdly, the service layer consists of designing and managing the services provided by users or applications. Finally, the interface layer consists of the methods of interacting with users or applications. Throughout this type of design, complex system structures are divided into subsystems that are loosely coupled and can be reused later, thereby offering a simple way to manage the whole complex system by taking care of its individual subsystems [3]. This will ensure that the rest of the system can still function normally in the event of a component failure. This is of tremendous importance for the efficient design of the IoT application architecture, where reliability is the most critical parameter. If SOA can bring these facilities to IoT architecture, then features like interoperability and scalability can be increased for IoT entities. Also, from the user's point of view, all resources are abstracted, and thus the user's burden is reduced to deal with various layers and protocols. Besides, SOA has the ability to create diverse and complex systems by using modular composability, where the execution of each task involves a sequence of service calls to all the different modules, which may be distributed over several locations.
Artificial Intelligence Governance For Businesses
Published in Information Systems Management, 2023
Johannes Schneider, Rene Abraham, Christian Meske, Jan Vom Brocke
According to International Organization for Standardization’s (ISO) Standard 9000, a process is a “set of interrelated or interacting activities, which transforms inputs into outputs” and procedures as a “specified way to carry out an activity or a process.” In the context of governance, we view procedures and processes as standardized, documented, and repeatable methods. AI processes can be considered a fundamental element of a successful AI governance implementation. They relate to data governance processes (Abraham et al., 2019) and processes tied to ML and AI systems. ML model development relies commonly on elements of the cross-industry standard process for data mining (CRISP-DM; Wirth & Hipp, 2000). Coding guidelines in software engineering can also serve as a governance mechanism. Similarly, developing and enforcing model (and system) architectural principles, documentation, and coding guidelines with the goal of better composability and maintainability might also be a governance instrument for AI to foster interoperability (Stoica et al., 2017). Serban et al. (2020) view the documentation of each feature, including its rationale, as best practice.
Advanced sensor-based maintenance in real-world exemplary cases
Published in Automatika, 2020
Michele Albano, Luis Lino Ferreira, Giovanni Di Orio, Pedro Maló, Godfried Webers, Erkki Jantunen, Iosu Gabilondo, Mikel Viguera, Gregor Papa
Most of the research work is related to the implementation of CBM solutions mainly targeted the implementation of CPS-based systems thus, focused on the following main principles adapted from [25,26], namely: abstraction and architectures;decentralization, modularity and composability;interoperability;real-time capability;interconnected data and data analytics/processing.
Towards a knowledge base to support global change policy goals
Published in International Journal of Digital Earth, 2020
Stefano Nativi, Mattia Santoro, Gregory Giuliani, Paolo Mazzetti
When defining SDGs or other integrated indicators, it is of paramount importance to re-use the existing initiatives and platforms avoiding to duplicate efforts (Giuliani et al. 2011). In this context, EO and the connected initiatives and platforms can play an important role for enabling the creation of integrated environmental indicators. Interoperability and standardization are critical for distributed resources discovery, access, and use. Interoperability, in particular, must be implemented up to the semantic and composability levels. Integrated policy initiatives and programmes should include the necessary work to link (conceptually, operationally and institutionally) the ongoing efforts for resources sharing – i.e. the existing data, information, and knowledge management frameworks.