- Expressing Strategic Variability and Flexibility of Processes: The Map Process Modeling Approach, Rébecca Deneckère, Centre de Recherche en Informatique, Université Paris 1 Panthéon Sorbonne, Paris, France. (Mercado Modelo I)
- Data-Driven Requirements Engineering, Xavier Franch, Universitat Politècnica Catalunya (UPC-BarcelonaTech), Barcelona, Spain. (Mercado Modelo I)
- Multi-Level Modeling with Powertypes: What you can do, What you can’t do, and How to achieve more with a foundational theory, João Paulo A. Almeida, Ontology & Conceptual Modeling Research Group (NEMO), Federal University of Espírito Santo (UFES), Vitória, Brazil. (Mercado Modelo II)
- Knowledge acquisition for ontology development, Mara Abel, Computing Systems for Petroleum Exploration Production Research Group, Informatics Institute, Federal University of Rio Grande do Sul, Porto Alegre, Brazil. (Mercado Modelo II)
Expressing Strategic Variability and Flexibility of Processes: The Map Process Modeling Approach
Centre de Recherche en Informatique
Université Paris 1 Panthéon Sorbonne, Paris, France
Abstract. Typical workflow-style process modeling languages have limits when designing processes at strategic level and reasoning on their variability, flexibility and adaptability. The Map process modeling formalism was defined to overcome this difficulty. It offers a new vision of process modelling by adding a strategic level and enabling intention-driven perspective. Indeed, Map focuses on what the process is intended to achieve – the intentions, and on how these intentions can be achieved – the strategies. Various strategies can be defined to reach an intention, allowing in this way to express the variability and flexibility in process enactment. Formally, Map is a process representation system based on a nondeterministic ordering of intentions and strategies. Il also provides guidelines on how to progress in the process map, i.e. guidelines to select the next intention and strategy and then to execute the underpinning activity. At enactment time, the order of execution of process activities is not predefined; the process is constructed dynamically considering various contextual criteria and the choices done by the actors executing the process. The applicability and usefulness of the Map approach was validated in several areas: requirement engineering, method engineering, decision making, enterprise knowledge development, IS engineering, and so on. The tutorial presents and illustrates the Map process modeling approach and invites the attendees to collaboratively create a map process model for a given case.
Rébecca Deneckère is Associate Professor at the Centre de Recherche en Informatique of the University of Paris 1 – Panthéon-Sorbonne. Her researches interests are mainly method engineering (specifically situational method engineering), decision making in information systems and intentional process mining. Her work obtained a best paper award at RCIS 2013. She regularly serves as reviewer for several information science conferences. She served as Program Co-chair for INFORSID 2012 and RCIS 2014. She is active in organizing national and international conferences and played the role of Organization Chair of ME 2011, INFORSID 2013, RCIS 2013 and currently of EDOC 2019. She is a member of the IFIP workgroup 8.1 (IFIP WG 8.1: Design and Evaluation of Information Systems).
Data-Driven Requirements Engineering
Abstract. In the last years, we are witnessing the advent of data-driven approaches in almost every single software engineering activity, and requirements engineering is not the exception to the rule. The exploitation of data coming from several sources may indeed become an extremely useful input to requirements elicitation and management but it does not come for free. Techniques such as natural language processing and machine learning are difficult to master and require high-quality data, whilst their generalization remains as a challenge. In this tutorial, I introduce the main concepts behind data-driven requirements engineering, then I provide an overview of the state of the art and report on results from some collaborative projects in which I’m participating, and last recapitulate lessons learned and open challenges. The research reported has been possible thanks to the support of the OpenReq (contract 732463) and Q-Rapids (contract 732253) H2020 projects supported by the European Union.
Xavier Franch is professor at the Universitat Politècnica de Catalunya (UPC-BarcelonaTech), Spain. He is the leader of the GESSI research group (https://gessi.upc.edu/en) composed by more of 20 researchers. He has authored more than 200 peer-reviewed papers in international journals and conferences, among them DKE, IS, SoSyM, ER and CAiSE. He belongs to the editorial board of IST, REJ, Computing and IJCIS, and acts as Deputy Editor for IET Software and Journal First chair for JSS. He is also member of the steering committee of the CAiSE and RE conferences. He has been General Chair of PROFES’19 and RE’08, Program Chair of RE’16, ICSOC’14, CAiSE’12 and REFSQ’11 (among others) and played several chair positions (Doctoral Consortium, Workshops, Tutorials, Education Track, etc.) in conferences like ER, CAiSE, RE and RCIS. He has a long track of participation in FP7/H2020 projects: project coordinator of RISCOSS (FP7; STREP; 2012-2015) and Q–Rapids (H2020; RIA; 2016-2019), scientific manager of SUPERSEDE (H2020; RIA; 2015-2018) and UPC representative of OPENREQ (H2020; RIA; 2017-2019) and S-CUBE (FP7; NoE; 2008-2012). He is Full Member and Council Member of IREB.
Multi-Level Modeling with Powertypes: What you can do, What you can’t do, and How to achieve more with a foundational theory
João Paulo A. Almeida
Ontology & Conceptual Modeling Research Group (NEMO)
Federal University of Espírito Santo (UFES), Vitória, Brazil
Abstract. In most approaches, a conceptual model is stratified into entities in two levels: (i) a level of types (or classes), and (ii) a level of instances (or objects). This conventional two-level scheme reveals its limitations whenever categories of categories are part of the domain of inquiry. In this case, multiple levels of classification are required—individuals, types, types of types, etc. For decades now, “powertypes” have been employed in such scenarios. Despite their wide applicability, powertypes are not capable of expressing a full multi-level scheme in which types have a dual type-instance nature. The goal of this tutorial is to explore recent advances in powertype-based multi-level representation strategies. Attendees will understand the historical mechanisms, appreciate the limitations of the support for powertype patterns in mainstream languages (such as UML) and will be provided strategies to circumvent these limitations based on a foundational theory for multi-level modeling. The representation strategies include a number of pattern variants accompanied with easy-to-follow (well-founded) modeling rules (most of which can be checked automatically by a modeling tool). Current research on advanced multi-level mechanisms related to the powertype pattern will also be discussed (e.g., concerning attributes of powertypes and their relation to the phenomenon known as deep characterization). Open issues and an agenda for further research will be outlined.
João Paulo A. Almeida is Associate Professor at the Federal University of Espírito Santo (UFES), Brazil, Senior Member of the Ontology and Conceptual Modeling Research Group (NEMO) and CNPq Productivity Research Fellow. Since 2007, he has been working on the application of ontologies in enterprise architecture and conceptual modeling at NEMO/UFES, having had a key role in the development of the OntoUML language and associated model-driven tools, as well as the MLT(*) multi-level modeling theories, the ML2 multi-level modeling language and associated tools. He served as a member of the Executive Committee of the International Association for Ontology and its Applications (IAOA) (2016) and as chair of the IEEE EDOC Conference Steering Committee (2014–2016). He currently serves in the Editorial Board of the Journal of Applied Ontology. His work on Multi-Level Modeling has been recognized with a Best Paper Award in ER 2015 and a Best Student Paper Award in ER 2018 (with student Claudenir Moraes Fonseca). He is a senior member of IEEE and ACM.
Knowledge acquisition for ontology development
Computing Systems for Petroleum Exploration & Production Research Group
Federal University of Rio Grande do Sul, Porto Alegre, Brazil
Abstract: Before designing a software application, the engineer needs to define the conceptual model in order to understand and simplify reality. The model does not represent the reality itself but the intention of the modeler in designing the application, according to his/her previous conceptualization. In order to guarantee that the software can be further integrated and understood, this conceptualization needs to be shared among the community of users. Ontologies are being developed and applied for explicating the semantics of the models and providing a framework for data interoperability among several applications. They differ from data models in the sense that the semantic is not based on the software application itself, but in the common acquaintance of the terminology among a community along with the philosophical view about world understanding. This tutorial explains the distinction between knowledge models and ontologies as a motivation for applying distinctive approaches for knowledge acquisition and modeling. It introduces practical steps and techniques for knowledge acquisition with the focus of producing well-founded domain ontologies. We aim the explanation is useful for general use in conceptual modeling tasks.
Mara Abel has graduated in Geology in the Geosciences Institute and got Master and Doctorate degrees in Computer Science at the Informatics Institute at the Federal University of Rio Grande do Sul UFRGS, where she is now Titular Professor. She is the leader of the Computing System Petroleum E&P Research Group that applies Knowledge Engineering and ontologies to improve the semantic of database models, especially those applied to petroleum exploration data. She has produced remarkable petroleum industrial innovation, such as the Petroledge® system, an intelligent database for petroleum reservoir description and interpretation, and Strataledge® , an ontology-based system for rock core description, commercialized by the ENDEEPER Company, a spin-off of her research group. She had coordinated several national and international innovation projects to apply ontology methodologies to deal with semantic content in petroleum applications.