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DaWaK 2024 : 26th International Conference on Big Data Analytics and Knowledge DiscoveryConference Series : Data Warehousing and Knowledge Discovery | |||||||||||||||
Link: https://www.dexa.org/dawak2024 | |||||||||||||||
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Call For Papers | |||||||||||||||
The 26th International Conference on Big Data Analytics and Knowledge Discovery (DAWAK 2024) will be held in Naples, Italy on August 26-28, 2024.
All accepted conference papers will be published in a volume of "Lecture Notes in Computer Science" (LNCS) by Springer. LNCS volumes are indexed in Scopus; EI Engineering Index; Google Scholar; DBLP; etc. and submitted for indexing in the Conference Proceedings Citation Index (CPCI), part of Clarivate Analytics’ Web of Science. TOP papers, after further revisions, will be invited for publication in a SPECIAL ISSUE of DATA & KNOWLEDGE ENGINEERING (DKE) titled "Data Engineering, Data Analytics and Data Science". SCOPE DaWaK conference is a high-quality forum for researchers, practitioners and developers in the field of Big Data Analytics, in a broad sense. The objective is to explore, disseminate and exchange knowledge in this field through scientific and industry talks. The conference covers all aspects of DaWaK research and practice, including data lakes, database design (data warehouse design, ER modelling), big data management (tables + text + files), query languages (SQL and beyond), parallel systems technology (Spark, MapReduce, HDFS), theoretical foundations and applications, text and data mining techniques, and deep learning. The conference will bring together active researchers from the database systems, cloud computing, programming languages and data science communities worldwide. Main topics include: Theoretical models for extended data warehouses and big data Conceptual model foundations for big data Modelling diverse big data sources Parallel processing Parallel DBMS technology Distributed system architectures Scalability and parallelization using Map-Reduce, Spark, and related systems Query languages Query processing and optimization Semantics for big data intelligence Data warehouse and data lake architectures Pre-processing and data cleaning Integration of data warehousing, OLAP cubes, and data mining Quantum technologies for data engineering Polystore and multistore architectures NoSQL storage systems Cloud infrastructures for big data Metadata for big data frameworks Big data storage and indexing Big data analytics: algorithms, techniques, and systems Big data quality and provenance Big data search and discovery Big data management for mobile applications Analytic workflows Graph analytics Analytics for unstructured, semi-structured, and structured data Analytics for temporal, spatial, spatio-temporal, and mobile data Analytics for data streams and sensor data Real-time/right-time and event-based analytics Privacy and security in analytics Data visualization Big data application deployment Data science products Novel applications of text mining for big data Machine learning: auto AI, deep learning applications SUBMISSION GUIDELINES Authors are invited to submit original research contributions or experience reports in English. DaWak will accept submissions of both short and full papers. * Short papers: up to 6 pages on preliminary work, vision papers or industrial applications * Full papers: up to 15 pages (including references and appendixes). Full papers are expected to be more mature, contain more theory or present a survey (tutorial style) of some hot or not yet explored topics. Papers exceeding the page limit or deviating from the formatting requirement are desk rejected. Submitted papers will be carefully evaluated based on originality, significance, technical soundness, and clarity of exposition. Duplicate submissions are not allowed and will be rejected immediately without further reviewing. Authors are expected to agree to the following terms: "I understand that the submission must not overlap substantially with any other paper that I am a co-author of or that is currently submitted elsewhere. Furthermore, previously published papers with any overlap are cited prominently in this submission." Questions about this policy or how it applies to a specific paper should be directed to the PC Co-chairs |
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