Conference Information

DEXA 2026: International Conference on Database and Expert Systems Applications

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Submission Date:
2026-03-15
Notification Date:
2026-05-15
Conference Date:
2026-08-11
Location:
Graz, Austria
Years:
37
CCF: C   ICORE: C   QUALIS: B1   Viewed: 48743   Tracked: 77   Attend: 22

Conference Partner Index (CP-I)

68.1 / 100
Ranked #353 of 5,645 conferences · Top 7%

#22 of 336 in Data Mining & Databases #29 of 734 in Artificial Intelligence & Machine Learning

Academic recognition (35%)
78
Submission selectivity (20%) No data - scored at the neutral baseline of 50 —
Editions held (20%)
98
Community attention (10%)
60
Public record completeness (15%)
35

Inputs used: Listed as CCF C, ICORE C, QUALIS B1 · Editions on record: 37 · Researchers following it here: 77 · Researchers who opened this page in the past 24 months: 10

Missing from the public record: Historical acceptance rates (+4.5) · Past editions (+3.0) · Best-paper records (+2.3)
Organizers can add these from this page after claiming the conference; scores are recomputed nightly. How to raise this score

Confidence 80% - the share of the score backed by observed data rather than the neutral baseline. How this score is calculated · Browse the ranking · Algorithm version 1.1 · Computed 2026-08-31

Call For Papers

DEXA 2026 (International Conference on Database and Expert Systems Applications) is a CCF C / ICORE C / QUALIS B1 conference held in Graz, Austria on 2026-08-11. The paper submission deadline is 2026-03-15. Acceptance notifications are sent on 2026-05-15.

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
Last updated by Dou Sun on

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