Context. Understandability is one of the most important quality criteria in business process models (BPMs). While the experimental study of the factors that affect understandability is an ongoing research, current initiatives are focused on a limited set of factors. An open challenge is to explore the relationships among several of these factors using automated statistical techniques. Machine Learning (ML) has been applied to generate statistical models, based on the combination of multiple factors, and to find relationships to predict indicator’s values. Objective. This thesis addresses the design of a method to assess the understandability of BPMs based on ML in order to predict whether a model could be understandable. This method will be implemented in an assisted modelling tool. Method. Using the design science methodology, the research aims to identify the factors that influence the understandability, their relationship, and how to measure them. This way we can correlate these factors and know which of them most affect the com- prehensibility of the BPMs. Our final target is to provide an automatic evaluation of understandability. Results. The expected contributions are 1. the design of an understandability automatic evaluation model and 2. an assisted modelling tool that incorporates the evaluation model to provide real-time guidance for more understandable models. Conclu sion. We aim to demonstrate that ML techniques can be used to predict BPMs understandability automatically.
Discussant: Pnina Soffer, University of Haifa
For predictive and experimental methods alike, discovering the structure and biological mechanisms of proteins is vital to our fundamental understanding of life. Driven by the vast number of solved protein structures through x-ray crystallography and Nuclear Magnetic Resonance, as well as advances in machine learning and neural networking that enable us to predict a protein's structure based solely on its amino acid sequence, this project lays the groundwork for predicting what would be the observed experimental NMR data of a protein based on its structure. Outlined is our ongoing conceptual model, implemented as relational databases, used for our work ow-based approach to solving the Forward Modeling problem of NMR. This approach will support ongoing machine learning approaches in predicting protein-ligand binding mechanisms and other kinetic studies.
Discussant: Oscar Pastor, Universitat Politècnica de València
A quintillion bytes of data are created every day. Reusing the collected data for different purposes is a better option in many cases than gathering new data. However, preparing existing data to match the requirements of new uses can be difficult. This research aims to give some guidelines for designing a dataset which is more repurposable. As conceptual modeling is the heart of designing an Information System, I will focus on how defining the self-defining concepts could help datasets to be reused in other concepts and to improve the datasets connect ability to other datasets.
Discussant: Wolfgang Maass, Saarland University
Over the past decennia, the fields of enterprise modelling, enterprise engineering, and enterprise architecture (EMEA) have provided interesting fields of application for conceptual modelling. Enterprise engineering & architecting rely on the use of (conceptual) enterprise models to represent different aspects of the existing/desired design of an enterprise (including companies, government agencies, smart cities, etc). The models used, typically range (at least) across the entire value-proposition-to-business-services-to-business-processes-to-information-systems-to-IT stack of an enterprise.
Meanwhile, the EMEA fields seem to be "under pressure". New enabling information technologies such as AI, Digital Twins, Block Chain, IoT, etc, appear to have a stronger interest and appreciation from funding sources. At the same time, there is evidence, from industrial practice, that the coherence, coordination and integration oriented perspectives of EMEA (captured in conceptual models) is direly needed. Now more than ever. The goal of this panel discussion is to explore the future role of EMEA as a field of research, and the role of conceptual modelling with(in) it.
This panel discussion is actually part of an ongoing discussion on the future of EMEA. The kick-off of this discussion took place during this year's IEEE CBI conference. At ER 2022, we plan to focus the discussion on the role of (conceptual) enterprise models. The broader discussion on the future of EMEA will be continued during working sessions at the IEEE EDOC conference and the IFIP 8.1 PoEM working conference later this year.
If you are active in the E.M.E.A. field, and care about its future, and the role of conceptual modelling within it, then please join the discussion.