Business process management employs process models and event logs to represent the behavior of the information systems under study. Traditional case-centric notions consider the order of activities and events in isolated process instances. The emerging field of object-centric processes challenges this assumption by putting objects in the center. Object-centric process mining and modeling approaches identify the structure of co-evolving data objects that influence the behavior of an information system to provide a comprehensive view of the system behavior. Object-centricity has been investigated independently in process modeling and in process mining, which resulted in the coexistence of seemingly contradictory assumptions and definitions. As a community effort, this research manifesto relates and aligns existing terminologies, definitions, and perspectives to provide a common ground for current and future research in object-centric business process management. Based on the current state of research, we propose a conceptualization that sets process models and event logs in relation to the information system’s behavior and the execution data it generates. The conceptualization aims at aligning different terminologies and, thus, providing a basis to model and analyze behavioral characteristics. Building on this common ground, we identify open research challenges along the most relevant research areas in object-centric process management. For each research area, its current status is investigated and an outline of the most relevant research challenges is presented.

Information and communication technology (ICT) brings about numerous advantages across various domains of our lives. However, alongside these benefits, there is a growing awareness of its potential negative ethical, social, and environmental impacts. Consequently, stakeholders ranging from conceptual modellers to policy makers often find themselves grappling with ethical considerations stemming from ICT engineering and usage. This paper presents a review of 10 ethical reasoning methods suitable for the ICT domain. We have employed a method engineering technique to author metamodels for the methods, which were subsequently subjected to validation by experts proficient in the respective methods. Following a situational method engineering approach, we have also characterised each ethical reasoning method and validated the characterisation with the experts. This has allowed us to develop a tool that helps select the method that is most suitable for a given ethical reasoning situation. Furthermore, we deliberate on the practical application of ethical reasoning methods within conceptual modelling contexts. We are confident that we have laid the groundwork for further research into ethical reasoning of ICT, with a specific emphasis on its role during conceptual modelling.

Alzheimer's disease is the most common type of dementia in the elderly. Nevertheless, there is an early onset form that is difficult to diagnose precisely. As the genetic component is the most critical factor in developing this disease, identifying relevant genetic variants is key to obtaining a more reliable and straightforward diagnosis. The information about these variants is stored in an extensive number of data sources, which must be carefully analyzed to select only the information with sufficient quality to be used in a clinical setting. This selection has become complex due to the increasing available genomic information. The SILE method was designed to systematize identifying relevant variants for a disease in this challenging context. However, several problems on how SILE identifies relevant variants were discovered when applying the method to the early onset form of Alzheimer's disease. More specifically, the method failed to address specific features of this disease such as its low incidence and familiar component. This paper proposes an improvement of the identification process defined by the SILE method to make it applicable to a further spectrum of diseases. Details of how the proposed solution has been applied are also reported. As a result of this improvement, a set of 29 variants has been identified (25 variants Accepted with a Limited Evidence and 4 Accepted with Moderate Evidence). This constitutes a valuable result that facilitates and reinforces the genetic diagnosis of the disease.

The human genome complexity is captured by many signals, representing for instance DNA variations, the expression of gene activity, or DNA’s structural rearrangements; a rich set of data types and formats is used to record these signals. Conceptual models can support the description and explanation of the genome’s elaborate structure and behavior. Among others, the Conceptual Schema of the Human Genome (CSG) provides a concept-oriented, top-down representation of the genome behavior, which is independent of data formats. The Genomic Conceptual Model (GCM) provides instead a data-oriented, bottom-up representation, targeting a well-organized, unified description of these formats. In this research, we join the two approaches to achieve PoliViews, a comprehensive model that links (1) a concepts layer, describing genome elements and their conceptual connections, with (2) a data layer, describing datasets derived from genome sequencing with specific technologies. Their dynamic connection is established when specific genomic data types are chosen in the data layer, thereby triggering the selection of a view in the concepts layer. The benefit is mutual: data records can be semantically described by high-level concepts exploiting their links and, in turn, the continuously evolving abstract model can be extended thanks to the input provided by real datasets. PoliViews enables expressing queries that employ a holistic conceptual perspective on the genome, directly translated onto data-oriented terms and organization. Here, we demonstrate the approach by linking two major genomic data types, namely DNA variation and gene expression. For each type, we consider different eminent data sources; we describe their mapping with the corresponding view in the concepts layer, enabling an intra-data-type integration. Then, leveraging on the connections available in the concepts layer, we show how the distinct data types can be interoperated, enabling an inter-data-type integration. The PoliViews approach is shown through several examples of biological interest and can be further extended to any kind of genomic information.

Although the knowledge about human genomics is available to all scientists, information about this scientific breakthrough can often be difficult to fully comprehend and share. A Conceptual Schema of the Human Genome was previously developed to assist in describing human genome-related knowledge, by representing a holistic view of the relevant concepts regarding its biology and underlying mechanisms. This model should become helpful for any researcher who works with human genomics data. We, therefore, perform the process of ontological unpacking on a portion of the model, to facilitate domain understanding and data exchange among heterogeneous systems. The ontological unpacking is a transformation of an input conceptual model into an enriched model based on a foundational ontology. The preliminary analysis and enrichment process are supported by the ontological conceptual modeling language OntoUML, which has been applied previously to complex models to gain ontological clarity. The value of the used method is first assessed from a theoretical point of view: the transformation results in significant, diverse modeling implications regarding the characterization of biological entities, the representation of their changes over time, and, more specifically, the description of chemical compounds. Since the ontological unpacking process is costly, an empirical evaluation is conducted to study the practical implications of applying it in a real learning setting. A particularly complex domain such as metabolic pathways is either described by adopting a traditional conceptual model or explained through an ontologically unpacked model obtained from a traditional model. Our research is evidence that including a strong ontological foundation in traditional conceptual models is useful. It contributes to designing models that convey biological domains better than the original models.

The digitalization of human society continues at a relentless rate. However, to develop modern information technologies, the increasing complexity of the real-world must be modeled, suggesting the general need to reconsider how to carry out conceptual modeling. This research proposes that the often-overlooked notion of “system” should be a separate, and core, conceptual modeling construct and argues for incorporating it and related concepts, such as emergence, into existing approaches to conceptual modeling. The work conducts a synthesis of the ontology of systems and general systems theory. These modeling foundations are then used to propose a CESM+ template for conducing systems-grounded conceptual modeling. Several new conceptual modeling notations are introduced. The systemist modeling is then applied to a case study on the development of a citizen science platform. The case demonstrates the potential contributions of the systemist approach and identifies specific implications of explicit modeling with systems for theory and practice. The paper provides recommendations for how to incorporate systems into existing projects and suggests fruitful opportunities for future conceptual modeling research.

Paul Johannesson; Mong Li Lee; Liddle, S.; Opdahl, A.; Pastor López, O. (2017). Special issue on conceptual modeling - 34th International Conference on Conceptual Modeling (ER 2015). Data & Knowledge Engineering. 109:1-2. doi:10.1016/j.datak.2017.03.001