Modeling an engineering design application using extended object-oriented concepts

This paper presents an approach to extend object-oriented data models, in which versions of an object are allowed to appear at different levels of an inheritance hierarchy, in contrast to the known approaches where they are admitted only at one leveI. This approach allows the design and instantiation of objects to beco me very natural, starting with the design of an object in a class and refining it, adding properties to the subclasses. Versions of objects can be defined in a subclass, having ascendant versions/objects associated to the superclasses. The paper also discusses how the extended model can be used to model engineering applications, fulfilling their requirements. The application is the STAR frarnework, which implements an innovative and flexible data model that allows the user to define an object schema for each design object. Design alternatives and views can be created during the design process and are represented in the object schema. Versioning appears in the STAR model not only for the real design data, but also for alternatives and views in the object schema. This requirement is not naturally modeled by the existing version models in object-oriented databases

The Application of Object-Oriented Views to an Engineering Environment.

With the increasing popularity of object-oriented technology, object-oriented database systems are being used in design environments as central repositories. In this thesis, we investigate the role of versioning and the characteristics of design databases in design environments. In an effort to improve the configuration management scheme in a design environment, we also investigate the use of database views as a possible configuration tool. We propose a unified version management scheme that facilitates cooperative team work and show that the use of database views provides a powerful configuration management scheme for a design environment

A conceptual model for unifying variability in space and time: Rationale, validation, and illustrative applications

With the increasing demand for customized systems and rapidly evolving technology, software engineering faces many challenges. A particular challenge is the development and maintenance of systems that are highly variable both in space (concurrent variations of the system at one point in time) and time (sequential variations of the system, due to its evolution). Recent research aims to address this challenge by managing variability in space and time simultaneously. However, this research originates from two different areas, software product line engineering and software configuration management, resulting in non-uniform terminologies and a varying understanding of concepts. These problems hamper the communication and understanding of involved concepts, as well as the development of techniques that unify variability in space and time. To tackle these problems, we performed an iterative, expert-driven analysis of existing tools from both research areas to derive a conceptual model that integrates and unifies concepts of both dimensions of variability. In this article, we first explain the construction process and present the resulting conceptual model. We validate the model and discuss its coverage and granularity with respect to established concepts of variability in space and time. Furthermore, we perform a formal concept analysis to discuss the commonalities and differences among the tools we considered. Finally, we show illustrative applications to explain how the conceptual model can be used in practice to derive conforming tools. The conceptual model unifies concepts and relations used in software product line engineering and software configuration management, provides a unified terminology and common ground for researchers and developers for comparing their works, clarifies communication, and prevents redundant developments

Enabling Process Variants and Versions in Distributed Object-Aware Process Management Systems

Business process variants are common in many enterprises and properly managing them is indispensable. Some process management suites already offer features to tackle the challenges of creating and updating multiple variants of a process. As opposed to the widespread activity-centric process modeling paradigm, however, there is little to no support for process variants in other process support paradigms, such as the recently proposed artifact-centric or object-aware process support paradigm. This paper presents concepts for supporting process variants in the object-aware process management paradigm. We offer insights into the distributed object-aware process management framework PHILharmonicFlows as well as the concepts it provides for implementing variants and versioning support based on log propagation and log replay. Finally, we examine the challenges that arise from the support of process variants and show how we solved these, thereby enabling future research into related fundamental aspects to further raise the maturity level of data-centric process support paradigms

Towards Automatic Parsing of Structured Visual Content through the Use of Synthetic Data

Structured Visual Content (SVC) such as graphs, flow charts, or the like are used by authors to illustrate various concepts. While such depictions allow the average reader to better understand the contents, images containing SVCs are typically not machine-readable. This, in turn, not only hinders automated knowledge aggregation, but also the perception of displayed in-formation for visually impaired people. In this work, we propose a synthetic dataset, containing SVCs in the form of images as well as ground truths. We show the usage of this dataset by an application that automatically extracts a graph representation from an SVC image. This is done by training a model via common supervised learning methods. As there currently exist no large-scale public datasets for the detailed analysis of SVC, we propose the Synthetic SVC (SSVC) dataset comprising 12,000 images with respective bounding box annotations and detailed graph representations. Our dataset enables the development of strong models for the interpretation of SVCs while skipping the time-consuming dense data annotation. We evaluate our model on both synthetic and manually annotated data and show the transferability of synthetic to real via various metrics, given the presented application. Here, we evaluate that this proof of concept is possible to some extend and lay down a solid baseline for this task. We discuss the limitations of our approach for further improvements. Our utilized metrics can be used as a tool for future comparisons in this domain. To enable further research on this task, the dataset is publicly available at https://bit.ly/3jN1pJ