Marta Sabou

Supporting the engineering of cyber-physical production systems with the AutomationML analyzer

The engineering phase of Cyber-Physical Production Systems (CPPS) is a multi-disciplinary process in which representatives of diverse engineering disciplines collaborate to deliver a complex CPPS. To ensure optimal project management as well as to avoid risks of inconsistencies between engineering models created by engineers from different disciplines, support is needed for integrating and subsequently analyzing diverse engineering data. AutomationML is an emerging data exchange format for engineering data which makes the first step towards the easier exchange of engineering data. Yet, there is a lack of tool support for integrating, making sense of and analyzing AML files. In this paper, we explore the use of Semantic Web and Linked Data technologies to provide extended functionality on top of AML that allows advanced data analytics on engineering data such as intuitive browsing of interlinked engineering models and queries for project-wide verification and validation activities. As a result of these investigations, we present the AutomationML Analyzer prototypical implementation to showcase some of the functionalities made possible by Semantic Web and Linked Data technologies in this context.

Automating Cross-Disciplinary Defect Detection in Multi-disciplinary Engineering Environments

Multi-disciplinary engineering (ME) projects are conducted in complex heterogeneous environments, where participants, originating from different disciplines, e.g., mechanical, electrical, and software engineering, collaborate to satisfy project and product quality as well as time constraints. Detecting defects across discipline boundaries early and efficiently in the engineering process is a challenging task due to heterogeneous data sources. In this paper we explore how Semantic Web technologies can address this challenge and present the Ontology-based Cross-Disciplinary Defect Detection (OCDD) approach that supports automated cross-disciplinary defect detection in ME environments, while allowing engineers to keep their well-known tools, data models, and their customary engineering workflows. We evaluate the approach in a case study at an industry partner, a large-scale industrial automation software provider, and report on our experiences and lessons learned. Major result was that the OCDD approach was found useful in the evaluation context and more efficient than manual defect detection, if cross-disciplinary defects had to be handled.

Modeling AutomationML: Semantic Web technologies vs. Model-Driven Engineering

Modeling engineering knowledge explicitly and representing it by means of standardized modeling languages and in machine-understandable form enables advanced engineering processes in industrial and factory automation. This affects positively both process and product quality. In this paper we explore how the AutomationML format, an emerging data exchange standard, that supports the Industry 4.0 vision, can be represented by means of two established modeling approaches - Model-Driven Engineering (MDE) and Semantic Web. We report observed differences w.r.t. resulting model features and model creation process and, additionally, present the application possibilities of the developed models for engineering process improvement in a production system engineering context.

Linked Data for Cross-Domain Decision-Making in Tourism

In today’s global economy, tourism managers need to consider a range of factors when making important decisions. Besides traditional tourism indicators (such as arrivals or bednights) they also need to take into account indicators from other domains, for example, economy and sustainability. From a technology perspective, building decision support systems that would allow inspecting indicators from different domains in order to understand their (potential) correlations, is a challenging task. Indeed, tourism (and other indicators), while mostly available as open data, are stored using database centric technologies that require tedious manual efforts for combining the data sets. In this paper we describe a Linked Data based solution to building an integrated dataset as a basis for a decision support system capable of enabling cross-domain decision-making. Concretely, we have exposed tourism statistics from TourMIS, a core source of European tourism statistics, as linked data and used it subsequently to connect to other sources of indicators. A visual dashboard explores this integrated data to offer cross-domain decision support to tourism managers.

The semantic model editor: efficient data modeling and integration based on OWL ontologies

Semantic Web and Linked Data are widely considered as effective and powerful technologies for integrating heterogeneous data models and data sources. However, there is still a gap between promising research results and prototypes and their practical acceptance in industry contexts. In context of our industry partners we observed a lack of tool-support that (a) enables efficient modeling of OWL ontologies and (b) supports querying and visualization of query results also for non-experts. The selection and application of existing semantic programming libraries and editors is challenging and hinders software engineers, who are familiar with modeling approaches such as UML, in applying semantic concepts in their solutions. In this paper we introduce the Semantic Model Editor (SMEd) to support engineers who are non-experts in semantic technologies in designing ontologies based on well-known UML class diagram notations. SMEd -- a Web-based application -- enables an efficient integration of heterogeneous data models, i.e., designing, populating, and querying of ontologies. First results of a pilot application at industry partners showed that SMEd was found useful in industry context, leveraged the derivation of reusable artifacts, and significantly accelerated development and configuration of data integration scenarios.

Improving Model Inspection Processes with Crowdsourcing: Findings from a Controlled Experiment

The application of best-practice software inspection processes for early defect detection requires considerable human effort. Crowdsourcing approaches can support inspection activities (a) by distributing inspection effort among a group of human experts and (b) by increasing inspection control. Thus, the application of crowdsourcing techniques aims at making inspection processes more effective and efficient. In this paper, we present a crowdsourcing-supported model inspection (CSI) process and investigate its defect detection effectiveness and efficiency when inspecting an Extended Entity Relationship (EER) model. The CSI process uses so-called Expected Model Elements (EMEs) to guide CSI inspectors during defect detection. We conducted a controlled experiment on defect detection effectiveness, efficiency, and false positives. While CSI effectiveness and efficiency is lower for CSI inspectors, the number of false positives decreases. However, CSI was found promising for increasing the control of defect detection and supports the inspection of large-scale engineering models.

Semantic Web Technologies for Data Integration in Multi-Disciplinary Engineering

A key requirement in supporting the work of engineers involved in the design of Cyber-Physical Production Systems (CPPS) is offering tools that can deal with engineering data produced across the various involved engineering disciplines. Such data is created by different discipline-specific tools and is represented in tool-specific data models. Therefore, due to this data heterogeneity, it is challenging to coordinate activities that require project-level data access. Semantic Web technologies (SWTs) provide solutions for integrating and making sense of heterogeneous data sets and as such are a good solution candidate for solving data integration challenges in multi-disciplinary engineering (MDE) processes specific for the engineering of cyber-physical as well as traditional production systems. In this chapter, we investigate how SWTs can support multi-disciplinary engineering processes in CPPS. Based on CPPS engineering use cases, we discuss typical needs for intelligent data integration and access, and show how these needs can be addressed by SWTs and tools. For this, we draw on our own experiences in building Semantic Web solutions in engineering environments.

Improving model inspection with crowdsourcing

Traditional Software Inspection is a well-established approach to identify defects in software artifacts and models early and efficiently. However, insufficient method and tool support hinder efficient defect detection in large software models. Recent Human Computation and Crowdsourcing processes may help to overcome this limitation by splitting complex inspection artifacts into smaller parts including a better control over defect detection tasks and increasing the scalability of inspection tasks. Therefore, we introduce a Crowdsourcing-Based Inspection (CSI) process with tool support with focus on inspection teams and the quality of defect detection. We evaluate the CSI process in a feasibility study involving 63 inspectors using the CSI process and 12 inspectors using a traditional best-practice inspection process. The CSI process was found useful by the participants. Although the preliminary results of the study were promising, the CSI process should be further investigated with typical large software engineering models.

An Introduction to Semantic Web Technologies

The process of engineering cyber-physical production systems (CPPS) relies on the collaborative work of multiple and diverse teams of engineers who need to exchange and synchronize data created by their domain-specific tools. Building applications that support the CPPS engineering process requires technologies that enable integrating and making sense of heterogeneous datasets produced by various engineering disciplines. Are Semantic Web technologies suitable to fulfill this task? This chapter aims to answer this question by introducing the reader to key Semantic Web concepts in general and core Semantic Web technologies (SWT) in particular, including ontologies, Semantic Web knowledge representation languages, and Linked Data. The chapter concludes this technology overview with a specific focus on core SWT capabilities that qualify them for intelligent engineering applications. These capabilities include (i) formal and flexible semantic modeling, (ii) intelligent, web-scale knowledge integration, (iii) browsing and exploration of distributed data sets, (iv) knowledge quality assurance and (v) knowledge reuse.

Knowledge Change Management and Analysis during the Engineering of Cyber Physical Production Systems: A Use Case of Hydro Power Plants

The process of designing Cyber Physical Production Systems (CPPS), e.g., modern power plants or steel mills, typically takes place in a multi-disciplinary engineering environment, in which experts from various engineering domains and organizations work together towards creating complex engineering artifacts. The process of designing such complex engineering artifacts requires iterations and redesign phases, which lead to continuous changes of the data and knowledge. To manage changes in such environment, we have previously proposed a generic reference process for conducting Knowledge Change Management and Analysis (KCMA). This paper implements this process for the case study of a modern Hydro Power Plant by adapting the proposed generic reference process into a scientific prototype developed using Semantic Web Technologies. Finally, we conduct an evaluation to evaluate the feasibility of the proposed reference process and the developed prototype. Thus, the contribution of this paper is two-folds: (1) A tool-supported prototype for KCMA of a hydro power plant, and (2) A feasibility evaluation of this prototype that reports feedback and lessons learned for achieving KCMA in real-world case studies.