Diversity, Equity and Inclusion (DEI) are longstanding themes within the ADBIS community. In 2026, the ADBIS DEI Chairs launch DEI4Teach, a highly interactive workshop dedicated to DEI in the teaching of Advanced Databases and Information Systems. The topic will also be reflected in thebroader DEI initiatives of ADBIS 2026 and the DEI keynote of this year's conference. While ADBIS traditionally focuses on scientific advances, DEI4Teach shifts the attention to how we teach them and to whom. It offers an opportunity to reflect on teaching practices from a DEI perspective in a concrete and practice-oriented way. In this context, DEI is understood broadly and includes gender equity, cultural and linguistic diversity, accessibility, socio-economic background, and inclusion across different national and institutional contexts.

MADEISD - 8th Workshop on Modern Approaches in Data Engineering and Information System Design

Sonja Ristić, Ivan Luković, Slavica Kordić

For decades, in many, particularly complex organization systems, there is an open issue how to support information management process so as to produce useful knowledge and tangible business values from data being collected. Database and information systems still play one of the central roles in addressing aforementioned issue. In recent years, we are the witnesses of great movements in the area of business information management. Such movements are both of technological and methodological nature. By this, today we have a huge selection of various technologies, tools, and methods in data engineering as a discipline that helps in a support of the whole data life cycle in organization systems, as well as in information system design that supports the software process in data engineering. Despite that, one of the hot issues in practice is still how to effectively transform large amounts of daily collected operational data into the useful knowledge from the perspective of declared company goals, and how to set up the information design process aimed at production of effective software services in companies. It seems that nowadays we have great theoretical potentials for application of new and more effective approaches in data engineering and information system design. However, it is more likely that real deployment of such approaches in industry practice is far behind their theoretical potentials. The main goal of this workshop is to address open questions and real potentials for various applications of modern approaches and technologies in data engineering and information system design so as to develop and implement effective software services in a support of information management in various organization systems. We intend to address interdisciplinary character of a set of theories, methodologies, processes, architectures, and technologies in disciplines such as Data Engineering, Information System Design, Big Data, NoSQL Systems, Data Streams, Internet of Things, Cloud Systems, and Model Driven Approaches in a development of effective software services. We invite researchers from all over the world who will present their contributions, interdisciplinary approaches or case studies related to modern approaches in Data Engineering and Information System Design. We express an interest in gathering scientists and practitioners interested in applying these disciplines in industry sector, as well as public and government sectors, such as healthcare, education, public administration, or security services. Experts from all sectors and application domains are welcomed.

Fairness is a complex and multifaceted concept that has garnered increasing attention in many computer science research areas. Defining and measuring fairness is often context-dependent, as different applications may require tailored approaches to address specific ethical concerns. Understanding the relationships between involved groups can be facilitated by defining an ontology or identifying key properties. However, achieving fairness demands ongoing evaluations, transparent decision-making, and adaptability to evolving contexts. In this effort, another crucial challenge is managing and analyzing heterogeneous data types - data from diverse sources, formats, and structures. To achieve fairness, there are several possible approaches: (i) Establishing standardized definitions for similar data fields promotes consistency; (ii) Identifying potential biases and designing mechanisms to reduce them; (iii) Handling missing data with appropriate imputation techniques and augmenting data to guarantee that representation of different groups; (iv) Promoting diverse representation to support an equal decision-making system. Ultimately, guaranteeing fairness in computational systems depends not only on data management but also on the careful design and evaluation of algorithms at various stages, including pre-processing, modeling, and post-processing. Incorporating fairness into these stages is critical for mitigating biases and fostering equity in data-driven systems. This workshop aims to serve as a small step forward in a field as vast as it is significant to the scientific community. We welcome articles with a particular focus on descriptive ontologies, fairness metrics, and properties, as well as frameworks for managing data heterogeneity to ensure fairness, and fairness-aware algorithms. We are also open to discussing other fair-related topics that are not explicitly mentioned.

ERGA - 2nd International Workshop on Entity Resolution and Graph Alignment

Georgia Koloniari, Alexandros Karakasidis

In the era of a connected world and abundant data, entity resolution and graph alignment have come to play an increasingly important role for ensuring data consistency, accuracy, and quality. Entity resolution identifies and links records referring to the same real-world entities (e.g., people, organizations) despite inconsistencies and has been a cornerstone of data integration with applications in finance, healthcare and various other domains. Graph alignment maps nodes and edges across different graphs taking into account the relationships and structures between the entities with applications such as social network analysis, bioinformatics and knowledge graphs. Both of these tasks share common challenges, particularly in handling large-scale data, requiring efficient solutions and deploying techniques from a great variety of research areas such as database management, big data and parallel processing, machine learning and AI- based techniques, and others. Recognizing the common ground between entity resolution and graph alignment, the goal of this workshop is to bring together researchers, practitioners and experts in both fields to introduce new methods and techniques, discuss open challenges, and explore future research directions. The workshop aims to facilitate the exchange of new ideas in each of the two fields, while also highlighting their shared goals and provide new opportunities for collaborations.