Application Programming Interfaces (apis) are crucial for enabling seamless communication between software systems, allowing them to exchange data and perform tasks efficiently. They underpin essential operations acro...
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In the rapidly evolving landscape of Industry 4.0, the Asset Administration Shell (AAS) is a fundamental building block for developing digital twins. This paper presents an innovative approach to automatically generat...
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ISBN:
(纸本)9798350363029;9798350363012
In the rapidly evolving landscape of Industry 4.0, the Asset Administration Shell (AAS) is a fundamental building block for developing digital twins. This paper presents an innovative approach to automatically generate Application Programming Interfaces (api) for AAS-based systems from a formalized specification. So far, the convention was to manually specify the api for AAS by tedious translation of the abstract specification published in form of a book. We propose, instead, to formalize the abstract specification, and automatically translate it into api. We thus streamline the development process, ensure effectiveness, and minimize the risk of errors in the representation of digital twins otherwise inherent in the manual procedure. Our proposed approach places great importance on automating the development of AAS apis, using common and widespread YAML/Openapi as a serialization format due to its clarity and simplicity. Additionally, the paper examines other Interface Descriptions such as Protobuf, SOAP, GraphQL, and WSDL, emphasising the significance of clearly defined interfaces for efficient AAS api utilisation. Our method guarantees scalability and flexibility, specifically in relation to SDK generation, whereby different AAS interfaces are generated to support smooth integration within the constantly changing landscape of Industry 4.0. This comprehensive approach facilitates the entire lifecycle of digital twin development, spanning from api generation to SDK development, to ensure resilient and adaptable solutions agnostic to particular serialization formats.
Deep learning libraries provide vast apis because of the multitude of supported input data types, pre-processing operations, and neural network types and configuration options. However, developers working on one concr...
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ISBN:
(纸本)9781450394673
Deep learning libraries provide vast apis because of the multitude of supported input data types, pre-processing operations, and neural network types and configuration options. However, developers working on one concrete application typically use only a small subset of the api at any one given time. Newcomers hence have to read through tutorials and api documentation, gathering scattered information, trying to find the api that fits their needs. This is time consuming and error prone. To remedy this, we show how we modularized the api of a popular Java DL framework Deeplearning4j (DL4J) according to features. Beginner developers can interactively select desired high level features, and our tool generates the subset of the DL library api that corresponds to the selected features. We evaluate our modularization on DL4J code samples, demonstrating an average recall of 98.9% for api classes and 98.0% for api methods. The respective precision is 19.3% and 13.8%, which represents an improvement of two orders of magnitude compared to the complete DL4J api.
Developing applications on the edge is a difficult task, as issues arise when multiple nodes have to operate in unison. Common challenges comprise correctness, performance, and forensics analysis after a system crash....
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ISBN:
(纸本)9781665481403
Developing applications on the edge is a difficult task, as issues arise when multiple nodes have to operate in unison. Common challenges comprise correctness, performance, and forensics analysis after a system crash. The challenges are aggravated when both edge and fog devices get in the picture, since the limited resources of some of them exacerbate any possible issue, and create the need for a robust mechanism to collect log data to make sense of the system's behavior. The envisioned monitoring layer should collect log data regarding performance and errors, protect the data until they are delivered to an external data processing facility, and be easy to integrate into the deployed system. This paper proposes C-Mon, a resilience-focused monitoring framework for service-oriented applications that tests timing constraints against live log data. The monitoring framework is integrated with Openapi Generator, the mainstream ReST code generator we used to generate client and server interfaces and monitoring mechanisms. Log data are cached locally on the disk of the fog/edge devices and transferred to the monitoring server only when enough CPU cycles and network bandwidth are available, at the same time enabling forensics analysis.
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