Scope

Data engineering and data analytics have been important R&D areas for years, with applications in multiple sectors (e.g., finance, energy, manufacturing, health care, security), but there still exist unsolved research and technological issues. Data engineering techniques are widely applied to store and manage data. New types of highly heterogeneous data of large volumes (referred to as big data) created every second, require a continuous development of new techniques to handle these new types of data. On the one hand, more and more frequently, these techniques need to be supported by Machine Learning (ML) algorithms, commonly called Artificial Intelligence (AI). These algorithms allow to provide solutions for difficult data management problems. Moreover, data science and data analytics rely on AI in the process of discovering dependency patterns, building complex prediction models, and processing natural language. On the other hand, data engineering techniques are indispensable for pre-processing data for AI. Thus, worlds of AI, data engineering, and data analytics are strongly interconnected.

Since 1990, DEXA has been an annual international conference which showcases state-of-the-art research activities in databases, data integration, advanced data anlytics, and knowledge systems. With the recent advances of big data and ML/AI DEXA covers these broad topics as well, to provide a forum to present research results and to examine advanced applications in the field. The conference and its associated workshops offer an opportunity for developers, scientists, and users to extensively discuss requirements, problems, and solutions in standard and just emerging research and technological topics on data and knowledge management.

Topics of Interest

DEXA 2026 invites submissions of original research papers as well as experience and survey papers on all topics related to database, information, and knowledge systems including, but not limited to:

Data mesh, data fabric, and data spaces
Data privacy and security
Cloud data management
Data quality assurance, monitoring, and management
Data governance techniques and architectures
Data integration and interoperability
Big data storage and search
Data as a Service (DaaS) technologies
Connectors as a Service (CaaS) technologies
Data pipeline automation
Data modeling
Standard and non-standard databases
Distributed ledger technologies
Real-time data processing
Descriptive, predictive, and prescriptive analytics on standard and big data
Artificial Intelligence (AI) for data management
AI for data engineering
Data engineering for AI
Large Language Models for data engineering
AI model governance
AutoML/AI solutions
Machine Learning Operations (MLOps)
AI powered data analytics
Integrating LLMs with external data sources
Text analytics
Graph management and analytics
Quantum technologies for data engineering and analytics