Alexandra Mazak

Towards Liquid Models: An Evolutionary Modeling Approach

Today, we recognize a discrepancy between design time models concentrating on the desired behavior of a system and its real world correspondents reflecting deviations taking place at runtime. In order to close this gap, design time models must not be static, but evolutionary artifacts so called liquid models. Such liquid models are the cornerstone of our future research project CDL-MINT: Model Integrated Smart Production. In this position paper, we present an early result of this project: the liquid models architecture for linking design models to runtime concerns, which are derived from distributed and heterogeneous systems during operation. We elaborate on the proposed technologies for the respective architecture layers and identify the research challenges ahead.

A standards framework for value networks in the context of Industry 4.0

The German initiative Industry 4.0 will involve amongst other issues networking and integration of several different parties (e.g., manufacturing companies, suppliers, customers, sub-contractors) through value networks. This initiative underpins that this collaborative partnership will only be feasible if standardization and open standards are available. For this purpose a reference architecture is needed to provide a technical description of these standards. In this context interoperability plays a major role for the seamless exchange of data and information among partners in these value networks. Interoperability involves the interaction of different systems and their users. Information modeling is a key concept for providing interoperability. In this paper, we present a standards framework that highlights how existing standards intertwine to establish value networks in an Industry 4.0 context.

iweightings: Enhancing Structure-based Ontology Alignment by Enriching Models with Importance Weighting

Structural ontology matching methods analyze mainly two factors: entity labels and relationships among entities. We propose to additionally consider an importance and relevance factor, which is determined by two indicators automatically calculated by a (simple) weighting method. This weighting factor represents the importance of a concept based on its information significance in the modeling context and, additionally, its relevance for structure-based alignment depending on the number of relationships this concept participates in quantified by the rweighting indicator. The method starts via a manually weighting annotation of relationships among concepts conducted by ontology engineers during the ontology development process. Our approach is an assistance mechanism to improve the ontology alignment process and to enhance the cognitive support for users. Thus, ontology alignment becomes already important ex ante when the ontology development process starts, unlike other alignment techniques, which consider only ex post knowledge.

HoVer: A modeling framework for horizontal and vertical integration

The German working committee for “Industrie 4.0” identified the horizontal integration throughout value networks and the vertical integration of networked manufacturing systems as key issues in the context of smart factories. For this purpose we aim for a universal model-driven industrial engineering framework spanning over production chains and value networks. Thereby, we build up on the Resource Event Agent (REA) business ontology (ISO/IEC 15944-4) to describe external activities requiring horizontal integration with business partners and internal activities serving for vertical integration within a manufacturing enterprise. We plan to apply the ISA-95 industry standard (ANSI/ISA-95; DIN EN 62264) to describe the vertical integration within an enterprise and its decentralized, networked production plants. As a first step, presented in this paper, we extend the REA ontology by useful concepts known from ISA-95 towards an integrating modeling framework.

From Business Functions to Control Functions: Transforming REA to ISA-95

In the context of smart factories, a seamless information exchange between information systems on the same layer (horizontal integration) and between information system son different layers (vertical integration) is a key issue. For this purpose we aim for an integrated modeling framework spanning over production chains and value networks. In building this framework, we first concentrate on the layers realizing the business functions and the manufacturing control functions. Thereby, we build up on the Resource Event Agent (REA)business ontology (ISO/IEC 15944-4) to describe external activities requiring horizontal integration with business partners and internal activities serving as a hook for vertical integration within a manufacturing enterprise. Furthermore, we base our framework on the ISA-95 industry standard (ANSI/ISA-95, IEC62264) to describe the vertical integration within an enterprise. In this paper, we demonstrate how information given in REA models is transformed to corresponding ISA-95 skeletons. In other words, we show how a model describing the main business functions of an enterprise is used to derive essential concepts relevant to the manufacturing execution system.

Model-Driven Systems Engineering: Principles and Application in the CPPS Domain

To engineer large, complex, and interdisciplinary systems, modeling is considered as the universal technique to understand and simplify reality through abstraction, and thus, models are in the center as the most important artifacts throughout interdisciplinary activities within model-driven engineering processes. Model-Driven Systems Engineering (MDSE) is a systems engineering paradigm that promotes the systematic adoption of models throughout the engineering process by identifying and integrating appropriate concepts, languages, techniques, and tools. This chapter discusses current advances as well as challenges towards the adoption of model-driven approaches in cyber-physical production systems (CPPS) engineering. In particular, we discuss how modeling standards, modeling languages, and model transformations are employed to support current systems engineering processes in the CPPS domain, and we show their integration and application based on a case study concerning a lab-sized production system. The major outcome of this case study is the realization of an automated engineering tool chain, including the languages SysML, AML, and PMIF, to perform early design and validation.

Using Signifiers for Data Integration in Rail Automation.

In Rail Automation, planning future projects requires the integration of business-critical data from heterogeneous data sources. As a consequence, data quality of integrated data is crucial for the optimal utilization of the production capacity. Unfortunately, current integration approaches mostly neglect uncertainties and inconsistencies in the integration process in terms of railway specific data. To tackle these restrictions, we propose a semi-automatic process for data import, where the user resolves ambiguous data classifications. The task of finding the correct data warehouse classification of source values in a proprietary, often semi-structured format is supported by the notion of a signifier, which is a natural extension of composite primary keys. In a case study from the domain of asset management in Rail Automation we evaluate that this approach facilitates high-quality data integration while minimizing user interaction.

On the Need for Temporal Model Repositories

Current model repositories often rely on existing versioning systems or standard database technologies. These approaches are sufficient for hosting different versions of models. However, the time dimension is often not explicitly represented and accessible. A more explicit presentation of time is needed in several use cases going beyond the classical system design phase support of models such as in simulation and runtime environments.

Combining Prediction Methods for Hardware Asset Management.

As wrong estimations in hardware asset management may cause serious cost issues for industrial systems, a precise and efficient method for asset prediction is required. We present two complementary methods for forecasting the number of assets needed for systems with long lifetimes: (i) iteratively learning a well-fitted statistical model from installed systems to predict assets for planned systems, and using this regression model (ii) providing a stochastic model to estimate the number of asset replacements needed in the next years for existing and planned systems. Both methods were validated by experiments in the domain of rail automation.

Big Data Semantics

Big Data technology has discarded traditional data modeling approaches as no longer applicable to distributed data processing. It is, however, largely recognized that Big Data impose novel challenges in data and infrastructure management. Indeed, multiple components and procedures must be coordinated to ensure a high level of data quality and accessibility for the application layers, e.g., data analytics and reporting. In this paper, the third of its kind co-authored by members of IFIP WG 2.6 on Data Semantics, we propose a review of the literature addressing these topics and discuss relevant challenges for future research. Based on our literature review, we argue that methods, principles, and perspectives developed by the Data Semantics community can significantly contribute to address Big Data challenges.