Conference Description
Key Takeaways
- Academic conference exploring big data analytics, knowledge discovery, and advanced database systems
- Covers scalable architectures including data lakes, data fabric, and polystore approaches
- Addresses integration of AI and machine learning with modern database technologies
- Designed for researchers, data engineers, architects, and technology professionals
- In-person event held in Graz, Austria with papers published in Springer’s Lecture Notes in Computer Science
Introduction
The 28th International Conference on Big Data Analytics and Knowledge Discovery (DAWAK 2026) convenes researchers, practitioners, and developers working at the intersection of large-scale data management and intelligent analytics. Taking place in Graz, Austria, this established academic forum addresses the growing complexity of extracting meaningful insights from heterogeneous data sources while maintaining system performance and analytical trustworthiness. As organisations across industries grapple with exponentially increasing data volumes and the pressure to operationalise machine learning at scale, the conference provides a venue for examining both theoretical foundations and practical implementation strategies.
About DAWAK 2026
Now in its 28th year, DAWAK has established itself as a significant venue for original research contributions in big data analytics. The conference bridges academic inquiry and industry application, featuring scientific talks, paper presentations, and structured networking opportunities. Accepted papers are published in Springer’s Lecture Notes in Computer Science series, with selected contributions invited for extended journal publication. This publication pathway reflects the conference’s emphasis on rigorous, peer-reviewed research that advances the field.
The event operates as an in-person gathering without hybrid or virtual participation options, prioritising direct collaboration and discussion among attendees.
Technical Focus Areas
DAWAK 2026 spans the full spectrum of big data infrastructure and analytics, from foundational storage models to advanced machine learning applications. The programme addresses modern data architectures including data lakes, data fabric, and polystore systems—approaches that have emerged in response to the limitations of traditional monolithic databases when handling diverse data types and access patterns.
Query processing and optimisation remain central themes, encompassing SQL, NoSQL, and NewSQL paradigms. These database technologies represent different trade-offs between consistency, availability, and partition tolerance, and understanding when to apply each approach is increasingly important as organisations adopt multi-model data strategies.
The conference examines parallel and distributed processing frameworks such as Apache Spark, MapReduce, and HDFS, which underpin much of contemporary big data infrastructure. Sessions also explore the integration of neural networks with database systems—a research direction that promises to reshape how queries are optimised and how unstructured data is indexed and retrieved.
Additional topics include data mining techniques for multimodal datasets, analytics on large graph structures, and processing of semi-structured formats like JSON. Cloud infrastructure, containerisation, and virtualisation feature prominently, reflecting the operational realities of deploying analytics systems at scale.
Data Quality and Trustworthy AI
A notable thread running through the conference concerns data quality, cleaning, and metadata management. These foundational concerns often determine whether analytics initiatives succeed or fail in practice, yet they receive less attention than more glamorous machine learning topics. DAWAK’s inclusion of these subjects acknowledges that robust data pipelines are prerequisites for reliable analytical outcomes.
The programme also addresses trustworthy AI in big data contexts—an area of growing regulatory and operational importance. As machine learning models are deployed in consequential decision-making scenarios, questions of explainability, fairness, and reliability become critical. The conference provides a forum for examining how these concerns intersect with the scale and complexity inherent in big data environments.
Who Should Attend
DAWAK 2026 serves academic researchers and doctoral students in computer science, data science, and information systems seeking to present original work and engage with current research directions. Industry practitioners—including data engineers, data architects, and database administrators—will find value in sessions addressing practical implementation challenges. Technology leaders and research and development professionals from organisations pursuing data-driven innovation can explore emerging approaches and establish connections with academic research groups.
Advancing Big Data Research and Practice
The conference addresses persistent challenges in the field: achieving efficient storage and processing of large-scale heterogeneous data, maintaining performance in distributed environments, and integrating machine learning capabilities with database systems in ways that are both scalable and maintainable. For organisations navigating these technical complexities, DAWAK 2026 offers exposure to current research and the opportunity to engage directly with those advancing the state of the art.