DaWaK 2020 : Data Warehousing and Knowledge Discovery (DaWaK)

  in Conferences   Posted on June 4, 2020

Conference Information

Submission Deadline Tuesday 14 Apr 2020 Proceedings indexed by :
Conference Dates Sep 14, 2020 - Sep 17, 2020
Conference Address Virtual Event, Online
Conference & Submission Link http://www.dexa.org/dawak2020
Conference Organizers : ( Deadline extended ? Click here to edit )

Conference Ranking & Metrics (This is a TOP Conference)

G2R Score 0
#Contributing Top Scientists 0
#Papers published by Top Scientists 0
Google Scholar H5-index 0
Number of Editions: 22 (Since 1999)
Guide2Research Overall Ranking: 0
DBLP Page https://dblp.uni-trier.de/db/conf/dawak

Conference Call for Papers

All accepted conference papers will be published in a volume of \”Lecture Notes in Computer Science\” (LNCS) by Springer. All published papers will be indexed appropriately in all major indexes. Selected high-quality papers will be invited to be published, after revision and extension, in a special issue of Data & Knowledge Engineering (DKE).

SCOPE
The annual 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 (schema-free repositories), 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.- Theoretical Models for Extended Data Warehouses and Big Data
– Parallel Processing
– Parallel DBMS Technology
– Schema-free Data Repositories
– Modelling diverse big data sources (e.g. text)
– Conceptual Model Foundations for Big Data
– Query Languages
– Query processing and Optimization
– Semantics for Big Data Intelligence
– Data Warehouses, Data Lakes
– Big Data Storage and Indexing
– Big Data Analytics: Algorithms, Techniques, and Systems
– Big Data Quality and Provenance Control
– Distributed System Architectures
– Cloud Infrastructure for Big Data
– Scalability and Parallelization using MapReduce, Spark and Related Systems
– Graph Analytics
– Visualization
– Big Data Search and Discovery
– Big Data Management for Mobile Applications
– 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
– Big Data Application Deployment
– Pre-processing and Data Cleaning
– Integration of Data Warehousing, OLAP Cubes and Data Mining
– Analytic Workflows
– Novel Applications of Text Mining to Big Data
– Deep Learning Applications
– Data Science Products

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