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[EN] Background Understanding the genome, with all of its components and intrinsic relationships, is a great challenge. Conceptual modeling techniques have been used as a means to face this challenge. The heterogeneity and idiosyncrasy of genomic use cases mean that conceptual modeling techniques are used to generate conceptual schemes that focus on too specific scenarios (i.e., they are species-specific conceptual schemes). Our research group developed two different conceptual schemes. The first one is the Conceptual Schema of the Human Genome, which is intended to improve Precision Medicine and genetic diagnosis. The second one is the Conceptual Schema of the Citrus Genome, which is intended to identify the genetic cause of relevant phenotypes in the agri-food field. Methods Our two conceptual schemes have been ontologically compared to identify their similarities and differences. Based on this comparison, several changes have been performed in the Conceptual Schema of the Human Genome in order to obtain the first version of a species-independent Conceptual Schema of the Genome. Identifying the different genome information items used in each genomic case study has been essential in achieving our goal. The changes needed to provide an expanded, more generic version of the Conceptual Schema of the Human Genome are analyzed and discussed. Results This work presents a new CS called the Conceptual Schema of the Genome that is ready to be adapted to any specific working genome-based context (i.e., species-independent). Conclusion The generated Conceptual Schema of the Genome works as a global, generic element from which conceptual views can be created in order to work with any specific species. This first working version can be used in the human use case, in the citrus use case, and, potentially, in more use cases of other species., This work was supported by the Spanish Ministry of Science and Innovation through Project DataME (ref: TIN2016-80811-P) and the Generalitat Valenciana through project GISPRO (PROMETEO/2018/176).

[EN] Precision medicine has emerged as a disrupting medical model to transform a historically reactive medicine into a proactive one that focuses on delivering individualized treatment. A relevant challenge of precision medicine is to integrate the large amount of omics data that exists. This data has a high degree of heterogeneity, dispersion, and isolation. In addition, there is a lack of a solid ontological commitment regarding domain concepts and definitions, and a unified guideline of how to transform data into knowledge is missing. In this work, we report our experience applying conceptual modeling to deal with these challenges in a specific genomics dimension, i.e., Precision Medicine. To do so, we have applied conceptual modeling techniques. The use of these techniques allows us to create representations of the world (i.e., conceptual schemes) that can be used for the purposes of understanding, communicating, and problem-solving. They also help to establish a common ontological framework to facilitate both communication and knowledge evolution in complex domains. We identify a set of limitations that emerged after working in a precision medicine context, and we describe how we have solved them using conceptual modeling. Thus, the main contribution of this work is to present the subsequent Conceptual Schema that allowed us to overcome these limitations, which provides a better representation of proteomics data and eases its integration., This work was supported in part by the Spanish State Research Agency and the Generalitat Valenciana under Project PROMETEO/2018/176, Project PDC2021-121243-I00, and Project INNEST/2021/57; and in part by the European Regional Development Fund (ERDF) and the European Union NextGenerationEU/PRTR.

In computer science, models are made explicit to provide formality and a precise understanding of small, contingent universes (e.g., an organization), as constructed from stakeholder requirements. Conceptual modeling is a fundamental discipline in this context whose main concerns are identifying, analyzing and describing the critical concepts of a universe of discourse. In the information systems field, one of the reasons why projects fail is an inability to capture requirements in a way that can be technically used to configure a system. This problem of requirements specification is considered to have deficiencies in theory. We apply a recently developed model called the Thinging Machine (TM) model which uniformly integrates static and dynamic modeling features to this problem of requirements specification. The object-Oriented (OO) approach to modeling, as applied in Unified Modeling Language, is by far the most applied and accepted standard in software engineering; nevertheless, new notions in the field may enhance and facilitate a supplementary understanding of the OO model itself. We aim to contribute to the field of conceptual modeling by introducing the TM model s philosophical foundation of requirements analysis. The TM model has only five generic processes of things (e.g., objects), in which genericity indicates generality, as in the generic Aristotelian concepts based on abstraction. We show the TM model s viability by applying it to a real business system., 15 pages, 16 figures

The article discusses the possibility of using the method of conceptual modeling for revision of the Concept development of the agricultural insurance, implemented with state support, for the period up to 2020.

Software systems have an increasing value in our lives, as our society relies on them for the numerous services they provide. However, as our need for larger and more complex software systems grows, the risks involved in their operation also grows, with possible consequences in terms of significant material and social losses. The rational management of software defects and possible failures is a fundamental requirement for a mature software industry. Standards, professional guides and capability models directly emphasize how important it is for an organization to know and to have a well-established history of failures, errors and defects as they occur in software activities. The problem is that each of these reference models employs its own vocabulary to deal with these phenomena, which can lead to a deficiency in the understanding of these notions by software engineers, causing potential interoperability problems between supporting tools, and, consequently, a poorer adoption of these standards and tools in practice. In this paper, we address this problem of the lack of a consensual conceptualization in this area by proposing two reference conceptual models: an Ontology of Software Defects, Errors and Failures (OSDEF), which takes into account an ecosystem of software artifacts, and a Reference Ontology of Software Systems (ROSS), which characterizes software systems and related artifacts at different levels of abstraction. Moreover, we use OSDEF and ROSS to perform an ontological analysis of the impact of defects, errors and failures of software systems from a risk analysis perspective. To do that, we employee an existing core ontology, namely, the Common Ontology of Value and Risk (COVR). The ontologies presented here are grounded on the Unified Foundational Ontology (UFO) and based on well-known and widely-accepted standards, professional and scientific guides and capability models. We demonstrate how this approach can suitably promote conceptual clarification and terminological harmonization in this area.

Traditional manufacturing businesses lack the standards, skills, processes, and technologies to meet today's challenges of Industry 4.0 driven by an interconnected world. Enterprise Integration and Interoperability can ensure efficient communication among various services driven by big data. However, the data management challenges affect not only the technical implementation of software solutions but the function of the whole organization. In this paper, we bring together Enterprise Integration and Interoperability, Big Data Processing, and Industry 4.0 in order to identify synergies that have the potential to enable the so-called “Fourth Industrial Revolution.” On this basis, we propose an architectural framework for designing and modeling Industry 4.0 solutions for big data-driven manufacturing operations. We demonstrate the applicability of the proposed framework through its instantiation to predictive maintenance, a manufacturing function that increasingly concerns manufacturers due to the high costs, safety issues, and complexity of its application.

We present a framework to support creative design of emergency management scenarios. By creative design of scenarios we mean the process of imagining situations and describing them through models and stories. The framework supports the tasks of gathering and organizing knowledge about emergency management situations by automatically generating conceptual models, related to fragments of emergency scenarios. It leverages semantics-based techniques to enable a computational creativity approach. A software application was defined to support the activities of modeling scenarios by permitting to generate, organize, and query sets of these conceptual models, which we name mini-stories, and that can be adopted to inspire the activity of creative design. Selected mini-stories are blueprints for more detailed user scenario descriptions and models that can be used, for instance, for analysis or simulation. As a case study, we consider emergency management in smart cities. This is a challenging domain because smart cities are characterized by interconnected physical and virtual services forming complex ecosystems, which provide sophisticated services to the population and to institutions, manage public resources in a optimal way, and involve citizens in decisional processes. As a consequence, smart city ecosystems can be threatened by several hazards spanning from natural disasters, as tsunami and earthquakes, to anthropic events, as terrorist attacks. Ability of service providers and institutional operators to face and manage emergency situations is therefore a relevant issue. Simulation and analysis of both crisis events and executions of management plans are a promising approach to deal with these articulated problems. However, manual definition of models to base the analysis is a demanding activity due to the huge number of different situations to consider. It requires knowledge related to the crisis and emergency domains, to the context (e.g., a specific city and its current regulations) and ability in modeling tasks. All these aspects demand for tools to support modeling activities, and our proposal aims at fulfilling this need. In particular, the discussed framework uses in a integrated way three types of knowledge: structural knowledge, to support the construction of models based on design patterns; domain knowledge, here related to smart cities and emergency management and represented by means of ontologies; and contextual knowledge, related to specific aspects (e.g., localization) of the considered scenario and represented as rules. We validated the presented approach by means of experiments performed by real city planners. © 2018 Elsevier Ltd

New blockchain-based projects do appear every day. The technology has indeed been popularized by cryptocurrencies but is now gaining interest in various domains and new types of applications are evaluated constantly. Understanding the impact of blockchain adoption on the organization and the internals of blockchain-related behavior nevertheless remains a challenge for managers but also for IT professionals. This article studies how two existing organizational and software modeling languages can be fit to document a blockchain development project in Supply Chain Management (SCM) at its earliest stages. These two frameworks are i* on the one side and the Unified Modeling Language (UML) use case and sequence diagrams on the other side. The real life project used as a case study in this application is ‘Farm-to-Fork’ where a blockchain solution for the Supply Chain (SC) of farm animals is developed. The application of the frameworks is intended to identify their strengths and weaknesses. An extension of i* is proposed to deal with blockchain privacy issues as well as laws and norms. We finally point to the complementarity of i* and UML use case and sequence diagrams in a Blockchain-Oriented Software Engineering (BOSE) context. The i* framework indeed supports early requirements to understand the impact of the project on stakeholders while UML use case and sequence diagrams support the late requirements and the design by depicting the use of blockchain and some of its behavioral mechanisms. ispartof: Complex Systems Informatics and Modeling Quarterly issue:26 pages:26-45 status: published

Responding to the recent COVID-19 outbreak, several organizations and private citizens considered the opportunity to design and publish online explanatory data visualization tools for the communication of disease data supported by a spatial dimension. They responded to the need of receiving instant information arising from the broad research community, the public health authorities, and the general public. In addition, the growing maturity of information and mapping technologies, as well as of social networks, has greatly supported the diffusion of web-based dashboards and infographics, blending geographical, graphical, and statistical representation approaches. We propose a broad conceptualization of Web visualization tools for geo-spatial information, exceptionally employed to communicate the current pandemic, to this end, we study a significant number of publicly available platforms that track, visualize, and communicate indicators related to COVID-19. Our methodology is based on (i) a preliminary systematization of actors, data types, providers, and visualization tools, and on (ii) the creation of a rich collection of relevant sites clustered according to significant parameters. Ultimately, the contribution of this work includes a critical analysis of collected evidence and an extensive modeling effort of Geo-Online Exploratory Data Visualization (Geo-OEDV) tools, synthesized in terms of an Entity-Relationship schema. The COVID-19 pandemic outbreak has offered a significant case to study how and how much modern public communication needs spatially related data and effective implementation of tools whose inspection can impact decision-making at different levels. Our resulting model will allow several stakeholders (general users, policy-makers, and researchers/analysts) to gain awareness on the assets of structured online communication and resource owners to direct future development of these important tools.

We study the usage of negation in JSON Schema data modeling. Negation is a logical operator rarely present in type systems and schema description languages, since it complicates decision problems: many software tools, but also formal frameworks for working with JSON Schema, do not fully support negation. This motivates us to study whether negation is actually used in practice, for which aims, and whether it could—in principle—be replaced by simpler operators. We have collected a large corpus of 80k open source JSON Schema documents from GitHub. We perform a systematic analysis, quantify usage patterns of negation, and also qualitatively analyze schemas. We show that negation is indeed used, albeit infrequently, following a stable set of patterns.

Since the first version of the Entity–Relationship (ER) model proposed by Peter Chen over forty years ago, both the ER model and conceptual modeling activities have been key success factors for modeling computer-based systems. During the last decade, conceptual modeling has been recognized as an important research topic in academia, as well as a necessity for practitioners. However, there are many research challenges for conceptual modeling in contemporary applications such as Big Data, data-intensive applications, decision support systems, e-health applications, and ontologies. In addition, there remain challenges related to the traditional efforts associated with methodologies, tools, and theory development. Recently, novel research is uniting contributions from both the conceptual modeling area and the Artificial Intelligence discipline in two directions. The first one is efforts related to how conceptual modeling can aid in the design of Artificial Intelligence (AI) and Machine Learning (ML) algorithms. The second one is how Artificial Intelligence and Machine Learning can be applied in model-based solutions, such as model-based engineering, to infer and improve the generated models. For the first time in the history of Conceptual Modeling (ER) conferences, we encouraged the submission of papers based on AI and ML solutions in an attempt to highlight research from both communities. In this paper, we present some of important topics in current research in conceptual modeling. We introduce the selected best papers from the 37th International Conference on Conceptual Modeling (ER’18) held in Xi’an, China and summarize some of the valuable contributions made based on the discussions of these papers. We conclude with suggestions for continued research. The research reported in this paper was partially funded by the ECLIPSE-UA (RTI2018-094283-B-C32) and the AETHER-UA (PID2020-112540RB-C43) Projects from the Spanish Ministry of Science and Innovation.

Telos is a conceptual modeling language intended to capture software knowledge, such as software system requirements, domain knowledge, architectures, design decisions and more. To accomplish this, Telos was designed to be extensible in the sense that the concepts used to capture software knowledge can be defined in the language itself, instead of being built-in. This extensibility is accomplished through powerful metamodeling features, which proved very useful for interrelating heterogeneous models from requirements, model-driven software engineering, data integration, ontology engineering, cultural informatics and education. We trace the evolution of ideas and research results in the Telos project from its origins in the late eighties. Our account looks at the semantics of Telos, its various implementations and its applications. We also recount related research by other groups and the cross-influences of ideas thereof. We conclude with lessons learnt. Published: 12 March 2020

[EN] Understanding the human genome is a big research challenge. The huge complexity and amount of genome data require extremely effective and efficient data management policies. A first crucial point is to obtain a shared understanding of the domain, which becomes a very hard task considering the number of different genome data sources. To make things more complicated, those data sources deal with different parts of genome-based information: we not only need to understand them well, but also to integrate and intercommunicate all the relevant information. The protein perspective is a good example: rich, well-known repositories such as UniProt provide a lot of valuable information that it is not easy to interpret and manage when we want to generate useful results. Proteomes and basic information, protein-protein interaction, protein structure, protein processing events, protein function, etc. provide a lot of information is that needs to be conceptually characterized and delimited. To facilitate the essential common understanding of the domain, this paper uses the case of proteins to analyze the data provided by Uniprot in order to make a sound conceptualization work for identifying the relevant domain concepts. A conceptual model of proteins is the result of this conceptualization process, explained in detail in this work. This holistic conceptual model of proteins presented in this paper is the result of achieving a precise ontological commitment. It establishes concepts and their relationships that are significant in order to have a solid basis to efficiently manage relevant genome data related to proteins., This work was supported in part by the Spanish State Research Agency under Grant TIN2016-80811-P, and in part by the Generalidad Valenciana under Grant PROMETEO/2018/176, co-financed with ERDF.

[EN] Data Warehousing applied in Big Data contexts has been an emergent topic of research, as traditional Data Warehousing technologies are unable to deal with Big Data characteristics and challenges. The methods used in this field are already well systematized and adopted by practitioners, while research in Big Data Warehousing is only starting to provide some guidance on how to model such complex systems. This work contributes to the process of designing conceptual data models for Big Data Warehouses proposing a method based on rules and design patterns, which aims to gather the information of a certain application domain mapped in a relational conceptual model. A complex domain that can benefit from this work is Genomics, characterized by an increasing heterogeneity, both in terms of content and data structure. Moreover, the challenges for collecting and analyzing genome data under a unified perspective have become a bottleneck for the scientific community, reason why standardized analytical repositories such as a Big Genome Warehouse can be of high value to the community. In the demonstration case presented here, a genomics relational model is merged with the proposed Big Data Warehouse Conceptual Metamodel to obtain the Big Genome Warehouse Conceptual Model, showing that the design rules and patterns can be applied having a relational conceptual model as starting point., This work has been supported by FCT - Fundação para a Ciên-cia e Tecnologia within the Project Scope: UID/CEC/00319/2019, the Doctoral scholarship PD/BDE/135100/2017 and European Structural and Investment Funds in the FEDER component, through the Operational Competitiveness and Internationalization Programme (COMPETE 2020) [Project nº 039479; Funding Reference: POCI-01-0247-FEDER-039479]. We also thank both the Spanish State Research Agency and the Generalitat Valenciana under the projects DataME TIN2016-80811-P, ACIF/2018/171, and PROMETEO/2018/176. Icons made by Freepik, from www.flaticon.com.

General ontology is a prominent theoretical foundation for information technology analysis, design, and development. Ontology is a branch of philosophy which studies what exists in reality. A widely used ontology in information systems, especially for conceptual modeling, is the BWW (Bunge–Wand–Weber), which is based on ideas of the philosopher and physicist Mario Bunge, as synthesized by Wand and Weber. The ontology was founded on an early subset of Bunge’s philosophy; however, many of Bunge’s ideas have evolved since then. An important question, therefore, is: do the more recent ideas expressed by Bunge call for a new ontology? In this paper, we conduct an analysis of Bunge’s earlier and more recent works to address this question. We present a new ontology based on Bunge’s later and broader works, which we refer to as Bunge’s Systemist Ontology (BSO). We then compare BSO to the constructs of BWW. The comparison reveals both considerable overlap between BSO and BWW, as well as substantial differences. From this comparison and the initial exposition of BSO, we provide suggestions for further ontology studies and identify research questions that could provide a fruitful agenda for future scholarship in conceptual modeling and other areas of information technology.

[EN] Currently, data management in oncology department is complex and requires advanced Information Systems (ISs) to process data where "omic" information should be integrated together with patient's clinical data to improve data analysis and decision-making process. This research paper reports a practical experience in this context. A Conceptual Model (CM) has been designed to develop an Information System (IS) in order to manage clinical, pathological, and molecular data in a holistic way at the oncology department of two main Hospitals in Paraguay. Additionally, model-based archetypes have been proposed to specify the selected user interaction strategy. The CM and its associated archetypes are the basis to develop a clinical IS in order to load -firstly- and manage -secondly- all the clinical data that the domain requires, showing how feasible the approach is in practice, and how much the corresponding clinical data management is improved. In this work, we want to reinforce with this real experience how using a CM along with archetypes correctly helps to design, develop and manage better information systems, emphasizing the relevance of the selected clinical domain., [ES] Actualmente, la gestión de datos en el departamento de oncología es compleja y requiere sistemas de información avanzados para procesar datos donde la información "ómica" debe integrarse junto con los datos clínicos del paciente para mejorar el análisis de datos y el proceso de toma de decisiones. Este trabajo de investigación presenta una experiencia práctica en este contexto. Se ha diseñado un Modelo Conceptual (MC) para desarrollar un Sistema de Información (SI) con el fin de gestionar datos clínicos, patológicos y moleculares de manera integral en el departamento de oncología de dos hospitales principales en Paraguay. Además, se han propuesto arquetipos basados en modelos para especificar la estrategia de interacción del usuario. El MC y los arquetipos asociados son la base para desarrollar un SI clínico con el fin de cargar -primero- y gestionar -segundo- todos los datos clínicos que requiere el dominio, mostrando cuán factible es el enfoque en la práctica y cuánto se mejora la gestión de datos. En este trabajo, queremos reforzar con esta experiencia real, cómo el uso correcto de un MC junto con los arquetipos ayuda a diseñar, desarrollar y administrar mejores sistemas de información, enfatizando la relevancia del dominio clínico seleccionado., This work has been developed in the framework of the project PINV115-149 with the financial support of the Consejo Nacional de Ciencia y Tecnología (CONACYT)- Paraguay, with resources del FEEI and with the financial support of the Spanish State Research Agency, the Generalitat Valenciana and "Multicenter Project of Multidisciplinary Training in Cancer and Application of Electronic Health Record (EHR)", and co-financed with ERDF.

The conceptual modeling of reparative regeneration during surgical treatment of diaphyseal fractures was carried out. A systematic analysis of preoperative, operative and postoperative factors of influence on reparative regeneration was used. Before planning surgical intervention and choosing osteosynthesis method and bone fixator, a systematic analysis of factors influencing reparative regeneration in accordance with the nature of fracture should be carried out. If there is insufficient or even good contact between the bone fragments while stabilizing them with intramedullary nails or locking compression plates, after the resorption of non-vascularized sections of bone fragments, the access to the main fragments is blocked. Therefore, the development of pseudoarthrosis is possible. The analysis of factors influencing reparative regeneration during surgical treatment of fractures is very important in preventing possible disorders and complications. Moreover, the conceptual modeling of reparative regeneration during surgical treatment of diaphyseal fractures is necessary in expanding the understanding of biology of fracture healing process., Проведено концептуальное моделирование репаративной регенерации при оперативном лечении диафизарных переломов. Использован системный анализ дооперационных, операционных и послеоперационных факторов влияния на репаративную регенерацию. Перед планированием оперативного вмешательства и выбором метода остеосинтеза и фиксатора должен проводиться системный анализ факторов влияния на репаративную регенерацию, в соответствии с характером перелома. После стабилизации невправленных фрагментов создаются условия для развития псевдоартроза. Достаточно часто процесс осложняется плохой васкуляризацией участков кости либо сдавливанием серкляжами. Анализ факторов влияния на репаративную регенерацию при оперативном лечении переломов имеет большое значение в предвидении их возможных расстройств и осложнений. Концептуальное моделирование репаративной регенерации расширяет понимание биологии процесса сращения отломков., Проведено концептуальне моделювання репаративної регенерації при оперативному лікуванні діафізарних переломів. Використаний системний аналіз доопераційних, операційних і післяопераційних чинників впливу на репаративну регенерацію. Перед плануванням оперативного втручання та вибором методу остеосинтезу й фіксатора повинен проводитися системний аналіз факторів, що впливають на репаративну регенерацію, відповідно до характеру перелому. Після стабілізації невправлених фрагментів створюються умови для розвитку псевдоартрозу. Досить часто процес ускладнюється поганою васкуляризацією ділянок кістки при її скелетизації або здавлюванні серкляжами. Аналіз факторів впливу на репаративну регенерацію при оперативному лікуванні переломів має велике значення щодо передбачення можливих розладів і ускладнень. Концептуальне моделювання репаративної регенерації розширює розуміння біології процесу зрощення відламків.

In recent years, there has been a dramatic increase in both practical and research applications of unmanned aerial vehicles (UAVs). According to the literature, there is a need in this area to develop a more refined model of UAV system architecture, in other words, a conceptual model that defines the system s structure and behavior. The existing models mostly are fractional and do not account for the entire important dynamic attributes. Progress in this area could reduce ambiguity and increase reliability in the design of such systems. This paper aims to advance the modeling of UAV system architecture by adopting a conceptual model called a thinging (abstract) machine in which all of the UAV s software and hardware components are viewed in terms of the flow of things and five generic operations. We apply this model to a real case study of a drone. The results, an integrated conceptual representation of the drone, support the viability of this approach., Comment: 10 pages, 15 figures

This paper presents an empirical study that investigates the extent to which the pragmatic quality of ontology-driven models is influenced by the choice of a particular ontology, given a certain understanding of that ontology. To this end, we analyzed previous research efforts and distilled three hypotheses based on different metaphysical characteristics. An experiment based on two foundational ontologies (UFO and BORO) involving 158 participants was then carried out, followed by a protocol analysis to gain further insights into the results of experiment. We then extracted five derivations from the results of the empirical study in order to summarize our findings. Overall, the results confirm that the choice of a foundational ontology can lead to significant differences in the interpretation and comprehension of the conceptual models produced. Moreover, the effect of applying a certain foundational ontology can cause considerable variations in the effort required to comprehend these models.

In their daily practice, most enterprises collect, store, and manage personal information for customers in order to deliver their services. In such a setting, privacy has emerged as a key concern as companies often neglect or even misuse personal data. In response to this, governments around the world have enacted laws and regulations for privacy protection. These laws dictate privacy requirements for any system that acquires and manages personal data. Unfortunately, these requirements are often incomplete and/or inaccurate as many RE practitioners might be unsure of what exactly are privacy requirements and how are they different from other requirements, such as security. To tackle this problem, we developed a comprehensive ontology for privacy requirements. To make it comprehensive, we base our ontology on a systematic review of the literature on privacy requirements. The contributions of this work include the derivation of an ontology from a previously conducted systematic literature review, an implementation using an ontology definition tool (Protege), a demonstration of its coverage through an extensive example on Ambient Assisted Living, and a validation through a competence questionnaire answered by lexical semantics experts as well as privacy and security researchers.

This issue of the journal on Complex Systems Informatics and Modeling Quarterly contains publications that present extended papers from the workshops of the 18th International Conference on Perspectives in Business Informatics Research (BIR 2019), organised in Katowice, Poland, 23-25 September 2019. The theme of the conference was Responsibilities of Digitalisation – Responsible Designing and Shaping the Future of Technology for Digital Preservation, Global Data Storage and Cost-Effective Management. The BIR 2019 workshops captured important and novel topics on security analytics, managed complexity, and information logistics and digital transformation. In this issue we also include one paper from the main BIR conference.

The article is aimed to draw the attention to the essential features of the business processes and business systems in the context of their modeling. We follow the root ideas of cybernetics in order to explain the concept of intentionality and its consequences in business system modeling as well as its impact on the business process modeling methodology and language. Possible way of refl ecting these ideas in the business processes modeling methodology is outlined using the example of the process meta-model and business process patterns from the MMABP methodology. Then the concept of causality in business systems is explained and relevant informatics theories and techniques for modeling the real world causality are introduced in this context. Particular attention is paid to the topic of relationships between both basic types of the business system models: intentional business process and causal ontology model. General rules and principles of the consistency of models are discussed together with their methodological consequences. Basic types of the consistency of models – completeness and correctness – are identifi ed and also the specifi c topic of ‘structural consistency’ is introduced in this context. In the conclusions section the needed refl ection of these essential features in the modeling languages and methodology is analyzed and the basic insuffi ciencies of the contemporary approaches to the business process modeling are pointed out together with the outline of possible ways of their overcoming. As the main challenges in the fi eld of business process modeling methodology and languages we particularly identify the need for respecting all consequences of the fact that business process has to be always taken as a purposeful process as well as the need for implementing the self-perception of the process in order to allow it achieving the higher orders of prediction.

System thinking has become an effective strategy when dealing with complex systems. Such systems are characterized by mutual interactions, causality and inter-dependency between system components. A typical example is the cooperation between governmental organizations and stakeholder interaction. The complexity of developing an e-government system suggests a more fundamental approach, where the roles of domain expert and system analyst are clearly separated. The main focus of this article is (1) to propose a linguistically-based systematic approach to the construction of models for the dynamics of complex systems, and (2) to propose extended causal diagrams. Our research methodology is based on Design Science. We start from a conceptual language developed for the application domain at hand and use this to define the dynamic factors. Then, we show how the resulting extended causal diagram is transformed into a framework for System Dynamics. We have demonstrated this approach by using a basic form of an e-Government as a running example. Our intention is to use this approach as a basis for a systematic step-wise introduction of e-Government in Zanzibar. Besides, this method is useful for modeling any complex system, especially for the description and evaluation of intended policies.

Within an enterprise, various stakeholders create different conceptual models, such as process, data, and requirements models. These models are fundamentally based on similar underlying enterprise (domain) concepts, but they differ in focus, use different modelling languages, take different viewpoints, utilize different terminology, and are used to develop different enterprise artefacts; as such, they typically lack consistency and interoperability. This issue can be solved by enterprise-specific ontologies, which serve as a reference during the conceptual model creation. Using such a shared semantic repository makes conceptual models interoperable and facilitates model integration. The challenge to accomplish this is twofold: on the one hand, an up-to-date enterprise-specific ontology needs to be created and maintained, and on the other hand, different modellers also need to be supported in their use of the enterprise-specific ontology. In this article, we propose to tackle these challenges by means of a recommendation-based conceptual modelling and an ontology evolution framework, and we focus in particular on ontology-based modelling support. To this end, we present our framework for Business Process Modelling Notation (BPMN) as a conceptual modelling language, and focus on how modellers can be assisted during the modelling process and how this impacts the semantic quality of the resulting models. Subsequently, we present a first, large-scale explorative experiment involving 140 business students to evaluate the BPMN instantiation of our framework. The experiments show promising results with regard to incurred overhead, intention of use and model interoperability.

[EN] This paper conducts an empirical study that explores the differences between adopting a traditional conceptual modeling (TCM) technique and an ontology-driven conceptual modeling (ODCM) technique with the objective to understand and identify in which modeling situations an ODCM technique can prove beneficial compared to a TCM technique. More specifically, we asked ourselves if there exist any meaningful differences in the resulting conceptual model and the effort spent to create such model between novice modelers trained in an ontology-driven conceptual modeling technique and novice modelers trained in a traditional conceptual modeling technique. To answer this question, we discuss previous empirical research efforts and distill these efforts into two hypotheses. Next, these hypotheses are tested in a rigorously developed experiment, where a total of 100 students from two different Universities participated. The findings of our empirical study confirm that there do exist meaningful differences between adopting the two techniques. We observed that novice modelers applying the ODCM technique arrived at higher quality models compared to novice modelers applying the TCM technique. More specifically, the results of the empirical study demonstrated that it is advantageous to apply an ODCM technique over an TCM when having to model the more challenging and advanced facets of a certain domain or scenario. Moreover, we also did not find any significant difference in effort between applying these two techniques. Finally, we specified our results in three findings that aim to clarify the obtained results. (C) 2018 Elsevier Ltd. All rights reserved., This research has been funded by the Ghent University Special Research Fund (BOF 01N02014) and the National Bank of Belgium.

Trust is a key component of relationships in social life. It is commonly argued that trust is the \glue" that holds families, societies, organizations and companies together. In the literature trust is frequently considered as a strategic asset for organizations. Having a clear understanding of the notion of trust and its components is paramount to both trust assessment and trust management. Although much progress has been made to clarify the ontological nature of trust, the term remains overloaded and there is not yet a shared or prevailing, and conceptually clear notion of trust. In this paper we address this issue by means of an in-depth ontological analysis of the notion of trust, grounded in the Unified Foundational Ontology. As a result, we propose a concrete artifact, namely, the Reference Ontology for Trust, in which we characterize the general concept of trust and distinguish between two types of trust, namely, social trust and institution-based trust. We also represent the emergence of risk from trust relations. In addition, we make a comparative analysis of our Reference Ontology to other trust ontologies. To validate and demonstrate the contribution of our approach, we apply it to model two application examples.

Multi-stakeholder partnerships (MSPs) have become a widespread means for deploying policies in a whole of society strategy to address the complex problem of childhood obesity. However, decision-making in MSPs is fraught with challenges, as decision-makers are faced with complexity, and have to reconcile disparate conceptualizations of knowledge across multiple sectors with diverse sets of indicators and data. These challenges can be addressed by supporting MSPs with innovative tools for obtaining, organizing and using data to inform decision-making. The purpose of this paper is to describe and analyze the development of a knowledge-based infrastructure to support MSP decision-making processes. The paper emerged from a study to define specifications for a knowledge-based infrastructure to provide decision support for community-level MSPs in the Canadian province of Quebec. As part of the study, a process assessment was conducted to understand the needs of communities as they collect, organize, and analyze data to make decisions about their priorities. The result of this process is a “portrait”, which is an epidemiological profile of health and nutrition in their community. Portraits inform strategic planning and development of interventions, and are used to assess the impact of interventions. Our key findings indicate ambiguities and disagreement among MSP decision-makers regarding causal relationships between actions and outcomes, and the relevant data needed for making decisions. MSP decision-makers expressed a desire for easy-to-use tools that facilitate the collection, organization, synthesis, and analysis of data, to enable decision-making in a timely manner. Findings inform conceptual modeling and ontological analysis to capture the domain knowledge and specify relationships between actions and outcomes. This modeling and analysis provide the foundation for an ontology, encoded using OWL 2 Web Ontology Language. The ontology is developed to provide semantic support for the MSP process, defining objectives, strategies, actions, indicators, and data sources. In the future, software interacting with the ontology can facilitate interactive browsing by decision-makers in the MSP in the form of concepts, instances, relationships, and axioms. Our ontology also facilitates the integration and interpretation of community data, and can help in managing semantic interoperability between different knowledge sources. Future work will focus on defining specifications for the development of a database of indicators and an information system to help decision-makers to view, analyze and organize indicators for their community. This work should improve MSP decision-making in the development of interventions to address childhood obesity.

Enterprises have been challenged to adopt practices of sustainability to benefit shareholders and society with goods standing much beyond monetary profit or required by law. In combination with environmental and economic concerns, Corporate Social Responsibility (CSR) has become an option to leverage businesses with good reputation and to attract sustainability-aware market segments. In line with such a demand, this paper presents a systematic literature review of conceptual modeling studies referring explicitly to certifications, laws or norms of CSR. The more specific research goal of this work is to discover ontologies for representing CSR best practices, design patterns or policies. In total, 921 peer-reviewed papers were analyzed, from which only 17 were considered relevant for data extraction. The main result of this work is the identification of a research gap in explicit knowledge representation of CSR practices for Information Systems design, which ought to be filled to complement the (dominant) economic perspective on sustainability.

As system complexity is on the rise, there is a growing need for standardized building blocks to increase the likelihood of systems’ success. Conceptual modeling is the primary activity required for engineering systems to be understood, designed, and managed. Modern modeling languages enable describing the requirements and design of systems in a formal yet understandable way. These languages use stereotypes to standardize, clarify the model semantics, and extend the meaning of model elements. An Internet of things (IoT) system serves as an example to show the significant contributions of stereotypes to model construction, comprehension, error reduction, and increased productivity during design, simulation, and combined hardware–software system execution. This research emphasizes stereotype features that are unique to Object-Process Methodology (OPM) ISO 19450, differentiating it from stereotypes in other conceptual modeling languages. We present the implementation of stereotypes in OPCloud, an OPM modeling software environment, explore stereotype-related problems, propose solutions, and discuss future enhancements.

The business is an abstraction of the way in which value is created and delivered. The concrete representation is the business model, expressed by a group of artifacts built with different languages. It serves to describe, explain, analyze, design, and evaluate the business. The set of concepts, construction rules, artifacts, and languages required to express it, are defined by a Meta-Business Model (MBM). Multiple authors have proposed different MBMs, each one with a specific motivation and objective. Some of these MBMs are widely recognized and have been applied in contexts like innovation and entrepreneurship. Due to new challenges, such as sustainability, being faced by businesses and given new ways of producing and delivering value, like the sharing economy, Novel Complex Businesses (NCBs) are emerging. NCBs are businesses characterized by circular structures made out of numerous inter-related components, and by creating value out of the product/service schema. While existing MBMs fulfill certain purposes, they do not have the expressiveness required to describe NCBs precisely enough to describe and analyze them. This paper introduces an MBM with the concepts, construction rules, and graphical notation needed to represent NCBs. We also illustrate an NCB and present the results of the validation for our MBM.

A core activity in information systems development involves building a conceptual model of the domain that an information system is intended to support. Such models are created using a conceptual-modeling (CM) grammar. Just as high-quality conceptual models facilitate high-quality systems development, high-quality CM grammars facilitate high-quality conceptual modeling. This paper provides a new perspective on ways to improve the quality of the semantics of CM grammars. For many years, the leading approach to this topic has relied on ontological theory. We show, however, that the ontological approach captures only half the story. It needs to be coupled with a logical approach. We explain how the ontological quality and logical quality of CM grammars interrelate. Furthermore, we outline three contributions that a logical approach can make to evaluating the quality of CM grammars: a means of seeing some familiar conceptual-modeling problems in simpler ways; the illumination of new problems; and the ability to prove the benefit of modifying existing CM grammars in particular ways. We demonstrate these benefits in the context of the Entity-Relationship grammar. More generally, our paper opens up a new area of research with many opportunities for future research and practice.

Fast advancement of technology has led to an increased interest for using information technology to provide feedback based on learning behavior observations. This work outlines a novel approach for analyzing behavioral learner data through the application of process mining techniques specifically targeting a complex problem solving process. We realize this in the context of one particular learning case, namely, domain modeling. This work extends our previous research on process-mining analysis of domain modeling behavior of novices by elaborating with new insights from a replication study enhanced with an extra observation on how novices verify/validate models. The findings include a set of typical modeling and validation patterns that can be used to improve teaching guidance for domain modeling courses. From a scientific viewpoint, the results contribute to improving our knowledge on the cognitive aspects of problem-solving behavior of novices in the area of domain modeling, specifically regarding process-oriented feedback as opposed to traditional outcome feedback (is a solution correct? Why (not)?) usually applied in this type of courses. Ultimately, the outcomes of the work can be inspirational outside of the area of domain modeling as learning event data is becoming readily available through virtual learning environments and other information systems. publisher: Elsevier articletitle: Process-mining enabled feedback: “Tell me what I did wrong” vs. “tell me how to do it right” journaltitle: Computers in Human Behavior articlelink: http://dx.doi.org/10.1016/j.chb.2015.12.040 content_type: article copyright: Copyright © 2015 Elsevier Ltd. All rights reserved. ispartof: Computers in Human Behavior vol:57 pages:352-376 status: published

The individuals are modeled by the elements of variable domains. The primitive frame to detect the individual migration from domain to domain is proposed. The supporting computational model is based on a separation of individuals into actual, possible and virtual ones. As was shown, this leads to an adoption of the stage-by-stage cognition model with a pair of evolvents to capture dynamics of the domains – the 2-dimensions model. The first evolvent reflects the generation of the individuals in a domain, the beginning of and canceling out their existence in a domain. The second evolvent reflects the shifts in properties of the individuals. As awaited this unified data model will have the applications to a wide range of models in computer science and Information Technologies.

In this work, a conceptual model-based optimization is performed for designing two waste solvent treatment technologies: i) hybrid process distillation assisted by pervaporation and ii) distillation/incineration.This task was performed resorting to conceptual models or shortcuts for each unit operation involved, which are characterized by their abilities to capture the essence of the process at a low computational cost. Life cycle assessment (LCA) was applied to evaluate the environmental impact of both alternatives. Optimal values for the utilities, i.e., cooling water, electricity and steam needed for distillation and pervaporation were used as the input data to perform the environmental analysis with LCA. Ecosolvent v.1.0.1 was used to calculate inventories data related to the incineration technology (waste solvent incineration and cement kiln) of the first cut of the distillation step for the case distillation/incineration and for treatment of column residues of both alternatives investigated.Applied to the mixture isopropyl alcohol-water, optimal values for per-pass IPA recovery and distillate composition were determined for the alternative distillation/incineration and distillation/pervaporation, respectively. From the analysis of the economic figures, it was concluded that solvent recovery through the hybrid process is the better option mainly due to the savings in the cost of fresh solvent. The LCA results, performed in SimaPro 7.3, show that the main impact is related to the solvent production. Thus, solvent recovery is the best option if minimization of the use of resources is paramount. Nevertheless incineration in a cement kiln becomes relevant in terms of human health for the avoided use of fuels.Furthermore, it was concluded that both process optimization with the aid of conceptual models and LCA are key tools in the decision-making process to determine which technology is the most appropriate for the treatment of waste solvents. Fil: Meyer, Regina Noemí. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; Argentina Fil: Figueroa Paredes, Danilo Alexander. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; Argentina Fil: Fuentes, Mauren. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; Argentina Fil: Amelio, Antonio. Universite Catholique de Louvain; Bélgica Fil: Morero, Betzabet del Valle. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; Argentina Fil: Luis, Patricia. Universite Catholique de Louvain; Bélgica Fil: Van Der Bruggen, Bart. Universite Catholique de Louvain; Bélgica Fil: Espinosa, José. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; Argentina. Universidad Nacional del Litoral; Argentina

This article presents the dimensional analysis conceptual modeling (DACM) framework, intended as a conceptual modeling mechanism for lifecycle systems engineering. DACM is a novel computer-aided method originally developed for military projects, but it's now available for other applications, too. The DACM framework is a powerful approach for specifying, discovering, validating, and reusing building blocks as well as analyzing system behavior in early development stages. This framework is based on dimensional analysis combined with causal graphs to represent the interactions and interdependencies among system variables. The framework's algorithms are codified into software applications to facilitate its use. This article provides a practical presentation of the steps that encompass the transformation from problem to solution space, key system variables extraction, causal ordering, clustering of variables, and qualitative analyses. The authors provide two examples that cover in detail the DACM's mathematic machinery for deriving a system's behavioral laws from a causal graph. The entire DACM approach is supported by a computer-based application that integrates all the steps of the framework presented in this article.

Part 7: Teaching Challenges; International audience; OntoREA© is a specification of the Accounting and Finance domain in the OntoUML language [1]. In a previous article [2] the authors use a forward contract financial derivative instrument to demonstrate the validity of the OntoREA© model within the design science research methodology (DSRM) [3]. A forward contract does not change over time and therefore can be modelled as static hedge portfolio composition. However, it is of interest if the OntoREA© model can also hold true for dynamic hedge portfolio compositions, as induced by option contract financial derivative instruments. This article investigates on that and delivers proof that the OntoREA© model is suitable for option contracts as well. Through adequately refining the platform specific database model (PSM) the policy’s dynamic nature can be demonstrated. Moreover, including a Plan/Do/Check/Act (PDCA) process model for the specification of the option contract replication also demonstrates the information processing in the REA accounting infrastructure. The proposed approach is implemented into an R/Shiny software prototype where the 3-tier-architecture is used to integrate the database and the PDCA process model at the R/Shiny implementation specific model (ISM) level. The presented hedge portfolio representation of derivatives can be useful for business analysts in the finance and accounting domain as well as for teaching financial derivative instruments.

Part 4: Reviews and Analyses of Modeling Methods; International audience; Metamodels play a pivotal role in conceptual modeling as they manifest the abstraction level applied when creating conceptual models. Consequently, design decisions made by the metamodel developer determine utility, capabilities, and expressiveness of the conceptual modeling language - and eventually the created models. However, only limited research defines and applies metrics for analyzing the structure and capabilities of a metamodel, and eventually support the development of new metamodels. This not only concerns general-purpose modeling languages, but also domain-specific ones, which usually undergo shorter update cycles. The paper at hand introduces a generic analysis framework to syntactically analyze modeling languages. The framework is applied to 40 metamodels of domain-specific conceptual modeling languages (DSML). This research establishes a foundation to support metamodel development in the future. The contribution of this paper is threefold: (i) an analysis framework for conceptual modeling method metamodels is proposed, (ii) results from applying this framework to 40 ADOxx-based DSML metamodels are presented, and (iii) a human-based reasoning after comparison of these results with Ecore-based metamodels is conducted.

P. 107-125 Different approaches have been used in order to represent and build control engineering concepts for the computer. Software applications for these fields are becoming more and more demanding each day, and new representation schemas are continuously being developed. This paper describes a study of the use of knowledge models represented in ontologies for building Computer Aided Control Systems Design (CACSD) tools. The use of this approach allows the construction of formal conceptual structures that can be stated independently of any software application and be used in many different ones. In order to show the advantages of this approach, an ontology and an application have been built for the domain of design of lead/lag controllers with the root locus method, presenting the results and benefits found.

For over a decade now, a community of researchers has contributed to the development of the Unified Foundational Ontology (UFO) - aimed at providing foundations for all major conceptual modeling constructs. This ontology has led to the development of an Ontology-Driven Conceptual Modeling language dubbed OntoUML, reflecting the ontological micro-theories comprising UFO. Over the years, UFO and OntoUML have been successfully employed in a number of academic, industrial and governmental settings to create conceptual models in a variety of different domains. These experiences have pointed out to opportunities of improvement not only to the language itself but also to its underlying theory. In this paper, we take the first step in that direction by revising the theory of types in UFO in response to empirical evidence. The new version of this theory shows that many of the meta-types present in OntoUML (differentiating Kinds, Roles, Phases, Mixins, etc.) should be considered not as restricted to Substantial types but instead should be applied to model Endurant Types in general, including Relator types, Quality types and Mode types. We also contribute a formal characterization of this fragment of the theory, which is then used to advance a metamodel for OntoUML 2.0. Finally, we propose a computational support tool implementing this updated metamodel.

How do syntax errors emerge? What is the earliest moment that potential syntax errors can be detected? Which evolution do syntax errors go through during modeling? A provisional answer to these questions is formulated in this paper based on an investigation of a dataset containing the operational details of 126 modeling sessions. First, a list is composed of the different potential syntax errors. Second, a classification framework is built to categorize the errors according to their certainty and severity during modeling (i.e., in partial or complete models). Third, the origin and evolution of all syntax errors in the dataset are identified. This data is then used to collect a number of observations, which form a basis for future research.

Conceptual modeling continues to evolve as researchers and practitioners reflect on the challenges of modeling and implementing data-intensive problems that appear in business and in science. These challenges of data modeling and representation are well-recognized in contemporary applications of big data, ontologies, and semantics, along with traditional efforts associated with methodologies, tools, and theory development. This introduction contains a review of some current research in conceptual modeling and identifies emerging themes. It also introduces the articles that comprise this special issue of papers from the 32nd International Conference on Conceptual Modeling (ER 2013). This article was supported, in part, by the J. Mack Robinson College of Business at the Georgia State University, the Marriott School of Management at Brigham Young University (EB-201313), and by the GEODAS-BI (TIN2012-37493-C03-03) project from the Spanish Ministry of Education and Competitivity.

In this paper, we assess the performance of a given hydrophobic membrane from the conceptual design of a hybrid process formed by the hydrophobic membrane itself and the separation train located downstream. To this end, a single pervaporation experiment with a model ethanol?water mixture is needed to estimate the minimum area requirement of the hydrophobic membrane. Short-cut methods, on the other hand, can be used to estimate the minimum number of stages and reflux ratio of the distillation column. Estimation of the minimum area requirement for a hydrophilic membrane, which is considered to overcome the azeotropic composition, requires the integration of a spatially one-dimensional isothermal mass transfer model of the unit until the desired biofuel purity is achieved in the corresponding retentate stream.The idea behind the approach is that the performance of a given membrane must be measured taking into account the overall hybrid process given that the hydrophobic membrane itself performs only a part of the desired separation.The hybrid process is then assessed on the basis of a cost estimate using the minimum membrane areas of the two membrane units together with minimum number of stages and minimum reflux ratio of the distillation column among other structural and operating variables.The outcome allows for the screening of pervaporation membranes, and yields valuable insights into the nature of the process as well as the constraints that a hybrid process may face. Membranes can be assessed based on their overall process performance by this method; only the subset of membranes presenting the best economic figures can be considered for a further analysis. Fil: Sosa, María Angélica. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; Argentina Fil: Figueroa Paredes, Danilo Alexander. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; Argentina Fil: Basilico, Juan Carlos. Universidad Nacional del Litoral; Argentina Fil: Van der Bruggen, Bart. Department of Chemical Engineering, ProcESS – Process Engineering for Sustainable Systems; Bélgica Fil: Espinosa, Hector Jose Maria. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; Argentina

The notion of time is an important element in such systems as real-time embedded systems. Real-time systems have strict timing constraints, and their complexity is continuously increasing, making their design very challenging. This paper concerns a very high level of requirements specification used for system understanding and communication among stakeholders and as a base for development. It introduces a diagrammatic description of functional behavior of a system with nonfunctional constraints including timing plan. Specifically, this paper explores the presentation of time at this level of system description. The usability and feasibility of the proposed method are illustrated by applying it to examples.