The scientific literature related to business ecosystems is growing. However, the major part of this literature is devoted to so-called Digital Business Ecosystems (DBE) that are largely related to only the networking part of the business ecosystems, i.e., exchange of values among their participants. Other parts related to the work of a participating enterprise, as well as its suppliers and regulators, are often left outside. In this work, we take a different approach, namely, to model an ecosystem that exists around a specific enterprise. The work is based on the concepts transferred to business from biological cybernetics, such as structural coupling and autopoiesis. In this work, the Fractal Enterprise Model (FEM) is used for modeling business ecosystems. The goal is to suggest some patterns expressed in the modeling language that can help an enterprise build a model of the ecosystem of which the enterprise is in focus.

The International Conference on Perspectives in Business Informatics Research (BIR) is dedicated to business informatics, business information systems and information systems engineering. It aims to support knowledge exchange between experienced and junior researchers, practitioners and industry professionals by considering both fundamental research and application-driven studies.

The 24th BIR conference, hosted by Riga Technical University, focuses on the theme “Bridging knowledge, process, and systems for responsible digital transformation” It explores how integrating knowledge management, process modeling, and information systems—with AI and smart technologies—can drive innovation and efficiency while ensuring ethical, sustainable, and inclusive practices. The conference traditionally attracts co-located workshops which encourage exchanging ideas and foster collaboration on topics related to business information systems.

Individuals suffering from long-term cognitive impairments following COVID-19 engage in a wide range of activities throughout their cognitive rehabilitation journey. This journey may include diagnostic assessments, therapeutic interventions, administrative coordination, and self-managed rehabilitation tasks. To effectively support and enhance such multifaceted rehabilitation processes, a digiphysical approach – which integrates digital and physical components – offers a promising solution. This article presents a process-oriented prototype based on the digiphysical approach, designed to bridge clinical and administrative tasks while combining digital and physical interventions. The article also reports findings from an initial evaluation of the prototype conducted with key rehabilitation professionals as respondents. Furthermore, five reusable solution patterns for implementing digiphysical rehabilitation are presented. These patterns are intended to be applicable not only in the studied context but also to support other care providers and organizations aiming to develop or enhance digiphysical rehabilitation services.

Custom CRISPR screens are powerful tools for rapid, hypothesis-driven discovery, but their design is often complex and time-consuming. Green Listed v2.0 simplifies this process with an intuitive workflow for designing custom CRISPR spacer libraries and supports downstream analysis for all users, irrespective of their computational experience. The web application features a user-friendly graphical interface freely accessible at https://greenlisted.cmm.se. Version 2.0 includes significant upgrades to the original 2016 version that were implemented based on user feedback. This includes a new gene synonym tool, expanded library options, optimized output lists, performance improvements, and linked scripts for the rational design of custom CRISPR screen gene sets.

The vast and growing web of rules and regulations that govern modern organisations is increasingly difficult to comprehend and manage. One technique for understanding and managing rules among stakeholders is enterprise modelling of organisational rules. However, rule models are usually flat, lacking hierarchical structure that allows for effective abstraction and reduced complexity. The aim of this paper is to examine the different aspects of hierarchy among organisational rules. A model pattern is proposed, based on the main constructs rule description, norm hierarchy, scope, and citation. The model pattern is demonstrated by instantiating it in a concrete case, the European Union’s General Data Protection Regulation (GDPR), using Wikibase Cloud to structure the data and Kumu.io to visualise it.

Rules and regulations grow into complex webs that are difficult even for experts to overview and comprehend. Particularly in cross-organisational collaborations in heavily regulated practices, e.g. health information exchange, rules and actors may form multilevel, adaptive, organisational rule systems. One potential way to analyse these complex structures is by utilising enterprise modelling and visualisations. This paper proposes a conceptual model for the description of rules in multilevel adaptive collaborations. The model is demonstrated with a case of collaboration for health data exchange, representing how actors at different levels adapt their rules according to their goals and how these goals must be balanced against the goals of the collaboration.

Human rights groups of all sizes and specializations gather evidence of human rights violations. Those with sufficient resources commonly use dedicated databases to manage the intricate web of sources, events, and analyses that build a strong case. However, developing databases for the needs of each organization remains a challenge. Effective database design requires a data model, which functions as a blueprint for how the data is structured and related. A most basic example of what a data model can specify is that a human rights violation is committed by at least one perpetrator, and that the perpetrator’s attributes, such as name or date of birth, should be recorded. A data model that is shared and generic reduces the need to reinvent the wheel since its design can be reused for several databases. In the information systems field, such shared models are called ontologies. Despite the critical nature of the matter, no ontology for human rights violations documentation exists. The present note, therefore, will present the design of the first ontology of this kind. It was developed in association with the human rights group HURIDOCS, which specializes in information management. The requirements elicitation included unstructured interviews with HURIDOCS, document analysis of human rights manuals, and a survey with practitioners. The resulting ontology, named OntoRights, is freely available online with an open license. The evaluation of OntoRights suggests that OntoRights could be highly useful for case databases.

Health care is a complex domain containing large amounts of data, including clinical and administrative data. Furthermore, the domain includes advanced decision-making utilising the collected data. Various IT systems based on AI technologies, such as machine learning, have been promoted as a way to improve both the quality and efficiency of health care. So far, the focus has been on supporting quite narrow and data-intensive activities carried out by a single actor, such as interpreting X-ray images and performing triage. However, providing health care for a single patient can involve a comprehensive process with numerous actors, ranging from home care and primary care to specialist care. In this paper, we examine how existing AI solutions can support a complex care process involving several collaborating actors. We base the examination on a health care case from Swedish elderly care. The case is used to identify multiple problem areas, which are then compared to existing AI solutions