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LISSU: Integrating Semantic Web Concepts Into SOA Frameworks LISSU: Integrating Semantic Web Concepts into SOA Frameworks Johannes Lipp1;3 a, Siyabend Sakik1 b, Moritz Kroger¨ 2 c and Stefan Decker1;3 d 1Chair of Information Systems, RWTH Aachen University, Aachen, Germany 2Chair for Laser Technology LLT, RWTH Aachen University, Aachen, Germany 3Fraunhofer Institute for Applied Information Technology FIT, Sankt Augustin, Germany Keywords: Semantic Web Services, Service-oriented Architecture, Semantic Web, Internet of Things. Abstract: In recent years, microservice-based architectures have become the de-facto standard for cloud-native applica- tions and enable modular and scalable systems. The lack of communication standards however complicates reliable information exchange. While syntactic checks like datatypes or ranges are mostly solved nowadays, semantic mismatches (e.g., different units) are still problematic. Semantic Web Services and their derivatives tackle this challenge but are mostly too ambitious for practical use. In this paper, we propose Lightweight Semantic Web Services for Units (LISSU) to support Semantic Web experts in their collaboration with do- main experts. LISSU allows developers specify semantics for their services via URI ontology references, and automatically validates these before initiating communication. It automatically corrects unit mismatches via conversions whenever possible. A real-world demonstrator setup in the manufacturing domain proves that LISSU leads to a more predictable communication. 1 INTRODUCTION We see our work in the area of SWS but have a smaller scope, because we aim for concrete and feasi- In the last decades, the Semantic Web (Berners-Lee ble solutions. Our goal is to resolve real-world prob- et al., 2001) has gained much popularity. Its extension lems in an example scenario from the engineering do- called Semantic Web Services (McIlraith et al., 2001), main, in particular when client and server have a se- or SWS in short, captures many types of information mantic mismatch (e.g., different units). State-of-the- for services, such as data, metadata, properties, ca- art solutions do not solve such mismatches and de- pabilities, interface, and pre- or post-conditions. Re- velopers try to tackle this via unstructured, human- searchers proposed a variety of challenges and so- readable data such as comments or documentations. lutions related to these goals especially during its Existing syntax validations at service interfaces must golden age starting around 2007. Most of the solu- be extended with machine-readable semantic ones to tions were never properly used in practice because make communication more predictable. other challenges had to be solved there first. Service- We propose Lightweight Integration of Seman- oriented architectures (SOA) and its sub-field remote tic Web Services for Units (LISSU) as a backwards- procedure calls (RPC) are promising application areas compatible extension to RPC frameworks that, in ad- for SWS, but had to tackle issues with connectivity, dition to syntactic details like datatypes, allows con- data availability, and syntax validation at interfaces figuration of semantic information including units. during that time. We argue on the one hand that the Our extended validation workflow detects unit mis- SOA research community has overcome most of these matches between client and server and even corrects basic challenges and needs semantic information now. these via automatic conversions if possible. This pro- On the other hand, the design of SWS is too complex vides an interoperable and consistently predictable and needs to be simplified to be used in practice. communication among all components, which we prove in a real-world demonstrator setup with ma- a https://orcid.org/0000-0002-2639-1949 chine components of a laser system. b https://orcid.org/0000-0002-5190-5753 The remainder of our paper is structured as fol- c https://orcid.org/0000-0001-7476-6226 lows. The next section gives a motivating example d https://orcid.org/0000-0001-6324-7164 and defines our goals. Section 3 investigates related 855 Lipp, J., Sakik, S., KrÃ˝uger, M. and Decker, S. LISSU: Integrating Semantic Web Concepts into SOA Frameworks. DOI: 10.5220/0010481408550865 In Proceedings of the 23rd International Conference on Enterprise Information Systems (ICEIS 2021) - Volume 1, pages 855-865 ISBN: 978-989-758-509-8 Copyright c 2021 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved ICEIS 2021 - 23rd International Conference on Enterprise Information Systems work with a special focus on ontologies and SWS. Section 4 presents our approach and Section 5 shows a real-world implementation. We conduct an evalua- Client tion based on this setup in Section 6 via a demonstra- tor and finally conclude in Section 7 with a summary of our work and possible future work in this field. Position = 2 2 MOTIVATING EXAMPLE This section motivates the extension of SOA with Scanner Scanner semantic capabilities based on SWS by presenting Service (old) Service (new) an example from USP laser system development and showcasing concrete challenges that we tackle in this work, particularly unit mismatches. Move 2 mm Move 2 cm 2.1 Semantic Mismatch (Units) Within the research project ”Internet of Production” (Pennekamp et al., 2019), Semantic Web experts and laser experts collaboratively build a ultrashort pulse USP (USP) laser system based on SOA. This includes to assemble machine parts from different manufacturers and subsequently achieve an interoperable communi- Figure 1: Unit mismatch we observed during a component cation between these. The very basic idea of USP swap at a USP laser system. The new server implementation internally uses different units and hence moves the laser for could be referred to as ”revers 3D printing”, which 2 centimeters instead of 2 millimeters. incrementally removes material with high amplitude laser pulses to form a final product. A USP laser in the network. This allows the serialization of data is divided into multiple components such as scan- with Protobuf, a Data Description Language made by ner, movement system, and camera. Client applica- Google (Google, 2015b). Additionally, the system tions (e.g., a central controller) call remote services uses Bazel (Google, 2015a) as the utilized build tool on these components to configure the laser and exe- to manage dependencies, build the source files and ex- cute actions. The syntax of service calls is validated ecute the code. The Protobuf files contain syntactical with the help of Protocol Buffers (Protobuf), but se- descriptions for all the relevant service and messages mantic mismatches are not detected and therefore ig- for the client server communication by strictly defin- nored. ing input and output parameters of service calls and A so-called scanner moves the laser to (x,y) the data types of these parameters in so called mes- coordinates based on received float values for sage definitions. In the process of utilizing gRPC, position.x and position.y, but the interpretation these files will then be compiled into new files in the of the units is left to the respective implementation of desired programming language like Python or Java for that service. Figure 1 illustrates a dangerous scenario the corresponding application, allowing the client and of a component change: The former scanner hardware server to access the definitions made in Protobuf files. and its respective service interprets position values as There is currently no way to properly include seman- millimeter and thus moves the laser to an x-position of tics like units into these definitions. 2 millimeter. A new component and its respective new Developers in this research project have cho- service however could interpret the same data value sen comments and variable names in their Proto- as 2 centimeter and thus move the laser wrongly or buf files as temporary solutions so that the units be- even damage the product. Other examples from that come visible to the reader. However, this approach use-case are laser heating temperatures (e.g., Celsius is error-prone and also not machine-readable. The vs. Fahrenheit) or laser speeds (e.g., millimeter per above-mentioned motivating examples demonstrates seconds vs. kilometer per hour). the need to avoid semantic mismatches by properly The details of that real-world motivating exam- defining and validating units at service interfaces. ple are the following. The currently existing project uses gRPC (Google, 2016) for the communication 856 LISSU: Integrating Semantic Web Concepts into SOA Frameworks 2.2 Requirements and Goals 3.1.1 Sensor Ontologies The ultimate goal of this work is to overcome se- The article (Schlenoff et al., 2013) reviews existing mantic mismatches in SOA frameworks by integrat- sensor data ontologies to decide if they can be reused ing Semantic Web components into these. In partic- for a manufacturing perception sensor ontology. The ular, we aim to extend the existing syntax-only val- outcome of their work is a review and also an ontol- idation to also cover semantics and units in partic- ogy. Relevant ontologies mentioned are the Sensor ular. Developers must be able to configure this in- Data Ontology (SDO) (Eid et al., 2007), which uses formation for both clients and servers to make ser- SUMO (Niles and Pease, 2001) as upper ontology, vice calls more predictable. These need to automat- the OntoSensor ontology (Russomanno et al., 2005) ically validate the given semantic information before and especially the Semantic Sensor Network (SSN) initiating the actual communication. This additional ontology (Compton et al., 2012). The SSN ontology verification must be backwards-compatible, because appears promising for our work and has multiple fea- SOA frameworks are usually distributed systems, and tures: Data discovery and linking, device discovery one developer can only modify some of the services. and selection, provenance and diagnosis, and device All interactions with the novel solution must be eas- operation, tasking, and programming (Compton et al., ily accessible for domain experts, which are typically 2012). It allows to focus on sensors, observed data, non-experts in the Semantic Web.
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