DaWaK: Data Warehousing and Knowledge Discovery

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Past:   Proceedings on DBLP

Future:  Post a CFP for 2020 or later   |   Invite the Organizers Email

 
 

All CFPs on WikiCFP

Event When Where Deadline
DaWaK 2019 21st International Conference on Big Data Analytics and Knowledge Discovery
Aug 26, 2019 - Aug 29, 2019 Linz, Austria Mar 18, 2019 (Mar 11, 2019)
DaWaK 2017 19th International Conference on Big Data Analytics and Knowledge Discovery
Aug 28, 2017 - Aug 31, 2017 Lyon, France Mar 27, 2017 (Mar 20, 2017)
DaWaK 2015 17th International Conference on Big Data Analytics and Knowledge Discovery - DaWaK 2015
Sep 1, 2015 - Sep 4, 2015 Valencia, Spain Mar 31, 2015 (Mar 17, 2015)
DaWaK 2014 16th International Conference on Data Warehousing and Knowledge Discovery
Sep 1, 2014 - Sep 5, 2014 Munich, Germany Mar 17, 2014
DaWaK 2013 15th International Conference on Data Warehousing and Knowledge Discovery
Aug 26, 2013 - Aug 29, 2013 Prague, Czech Republic Mar 26, 2013 (Mar 19, 2013)
DaWaK 2012 14th International Conference on Data Warehousing and Knowledge Discovery
Sep 3, 2012 - Sep 7, 2012 Vienna, Austria Mar 26, 2012 (Mar 19, 2012)
DaWaK 2010 12th International Conference on Data Warehousing and Knowledge Discovery
Aug 30, 2010 - Sep 3, 2010 Bilbao, Spain Mar 27, 2010 (Mar 19, 2010)
DaWaK 2009 11th International Conference on Data Warehousing and Knowledge Discovery
Aug 31, 2009 - Sep 4, 2009 Linz, Austria Mar 27, 2009 (Mar 20, 2009)
 
 

Present CFP : 2019

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 modeling), big data management (tables + text + files), query languages (SQL and beyond), parallel systems technology (Spark, MapReduce, HDFS), theoretical foundations and applications, bringing together active researchers from the database systems, cloud computing, programming languages and data science communities worldwide.

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 modeling), big data management (tables + text + files), query languages (SQL and beyond), parallel systems technology (Spark, MapReduce, HDFS), theoretical foundations and applications, bringing 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
Modeling diverse big data sources (e.g. text)
Conceptual Model Foundations for Big Data
Query Languages
Query processing and Optimization
Cost Models for advanced 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
Exploiting hardware to accelerate processing: multicore CPUs, cache memory, GPUs
Cloud Infrastructure to manage big data
Scalability and Parallelization using MapReduce, Spark and related systems
Graph analytics, including social networks and the Internet
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
Analytics for Big Multimedia Data
Real-time/Right-time and Event-based Analytics
Privacy and Security in Analytics
Big Data Application Deployment
Pre-processing and data cleaning to build analytic data sets
Integration of Data Warehousing, OLAP Cubes and Data Mining
Analytic workflows
 

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