The 26th International Workshop on Design, Optimization, Languages and Analytical Processing of Big Data (DOLAP 2024), the premier workshop in this area, will take place co-located with EDBT 2024 in Paestum (Italy) on March 25th 2024.

Submissions are now open at https://cmt3.research.microsoft.com/DOLAP2024
Official website: https://dolapworkshop.github.io/dolap-2024/

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- Paper submission: November 30, 2023 (Abstract: November 23, 2023)
- Authors notification: January 22, 2024
- Workshop date: March 25, 2024
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DOLAP accepts short and long paper submissions, moreover this year for the first time we will invite extended abstracts on visions, challenges, and opportunities for artificial intelligence in data analytics.

The best papers presented at DOLAP will be invited to a special issue of Information Systems.

Long papers include novel and mature research, industrial, or survey work. Long papers of good quality but not mature enough might be accepted to the workshop as short papers.
Short papers include (ongoing) novel research works with preliminary results and vision/position papers outlining research issues for future work.
Extended abstract will be invited to present in the interactive panel session, and include initial controversial ideas and visions, reports on early (or negative) results, or reflections on existing and future challenges on the theme of the interactive session.

The page limit is 8 pages for full papers, 4 pages for short papers, and 2 pages for extended abstracts (in CEUR format, double-column, excluding references). Each submission will be reviewed by 3 members of the program committee, the review process is single-blind, and thus authors must include their names and affiliations in submissions.
Extended abstracts are short papers with an abstract, a main body, and references but have only 2 standard pages of content references included.

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Interactive Panel Session on: Artificial Intelligence in Data Analytics

For DOLAP 2024, we solicit an extended abstract about perspectives on exciting and controversial issues of broad interest to the community around the topic of artificial intelligence in data analytics. Extended abstract on this topic can present a vision as well as early or negative results, or comments that can influence recent advancements and research directions.

The panel session will feature short presentations followed by extensive and interactive discussions on the presented topics. We encourage the authors to propose topics and perspectives that will engage the audience and ignite debate among the participants. Ultimately, the goal is to tap into one of the original functions of workshops as a forum for discussion, where researchers come together to brainstorm and contribute to paving the way for future research directions.

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Research topics include, but are not limited to:

Design and Language
- Data management fundamentals: architectures, design, ETL/ELT, reverse ETL, modeling, data integration, database design for big data, query processing, maintenance, evolution, security, personalization, and privacy in decision support systems.
- Data Variety: unstructured data (e.g., text), semi-structured data (e.g., XML, JSON), multimedia, spatial, temporal, and spatio-temporal data, stream and sensor data, semantic web, data lakes, data spaces, data quality, graph data, multistore and polystore solutions, multi-model data warehouse
- Explainable, trustworthy, and interpretable analytics: bias in big data and how to mitigate it; data quality and data cleaning; FAIRness (Findability, Accessibility, Interoperability and Reusability) in OLAP

Optimization
- Coping with Volume: physical organization, performance optimization and tuning, scalability, MapReduce and Spark for data analytics, performance optimization of ETL/ELT.
- Coping with Velocity: Deployment on parallel machine, database clusters, cloud infrastructures and serverless architectures, active/real-time analytics, real-time queries.

Analytical Processing and Applications
- Analytics and Value: OLAP, data exploration through visualization, recommendation, reformulation, approximate query-answering, personalization, result presentation, data storytelling, graph analytics, process mining, advanced visualization for business contexts.
- Analytics and Veracity: heterogeneous data integration for analytics, quality aspects of data analysis, exploration outcome and end-user experience, fairness of data analysis, analytics and data driven decision making for the data enthusiasts.
- Integration of analytics with machine learning, data mining, information retrieval, search engines, data science, predictive and prescriptive analytics.
- Big Data applications: smart city, smart health, smart energy, smart grid, smart agriculture.