DaWaK 2020: Difference between revisions

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=== Scopes ===
=== Scopes ===
* - Parallel Processing
* Parallel Processing
* - Parallel DBMS Technology
* Parallel DBMS Technology
* - Schema-free Data Repositories
* Schema-free Data Repositories
* - Modelling diverse big data sources (e.g. text)
* Modelling diverse big data sources (e.g. text)
* - Conceptual Model Foundations for Big Data  
* Conceptual Model Foundations for Big Data  
* - Query Languages  
* Query Languages  
* - Query processing and Optimization
* Query processing and Optimization
* - Semantics for Big Data Intelligence
* Semantics for Big Data Intelligence
* - Data Warehouses, Data Lakes
* Data Warehouses, Data Lakes
* - Big Data Storage and Indexing
* Big Data Storage and Indexing
* - Big Data Analytics: Algorithms, Techniques, and Systems
* Big Data Analytics: Algorithms, Techniques, and Systems
* - Big Data Quality and Provenance Control
* Big Data Quality and Provenance Control
* - Distributed System Architectures
* Distributed System Architectures
* - Cloud Infrastructure for Big Data
* Cloud Infrastructure for Big Data
* - Scalability and Parallelization using MapReduce, Spark and Related Systems
* Scalability and Parallelization using MapReduce, Spark and Related Systems
* - Graph Analytics
* Graph Analytics
* - Visualization
* Visualization
* - Big Data Search and Discovery
* Big Data Search and Discovery
* - Big Data Management for Mobile Applications
* Big Data Management for Mobile Applications
* - Analytics for Unstructured, Semi-structured, and Structured Data
* Analytics for Unstructured, Semi-structured, and Structured Data
* - Analytics for Temporal, Spatial, Spatio-temporal, and Mobile Data
* Analytics for Temporal, Spatial, Spatio-temporal, and Mobile Data
* - Analytics for Data Streams and Sensor Data
* Analytics for Data Streams and Sensor Data
* - Real-time/Right-time and Event-based Analytics
* Real-time/Right-time and Event-based Analytics
* - Privacy and Security in Analytics
* Privacy and Security in Analytics
* - Big Data Application Deployment
* Big Data Application Deployment
* - Pre-processing and Data Cleaning
* Pre-processing and Data Cleaning
* - Integration of Data Warehousing, OLAP Cubes and Data Mining
* Integration of Data Warehousing, OLAP Cubes and Data Mining
* - Analytic Workflows
* Analytic Workflows
* - Novel Applications of Text Mining to Big Data
* Novel Applications of Text Mining to Big Data
* - Deep Learning Applications  
* Deep Learning Applications  
* - Data Science Products
* Data Science Products

Revision as of 10:12, 7 May 2020

DaWaK 2020
22nd International Conference on Big Data Analytics and Knowledge Discovery
Event in series DaWaK
Dates 2020/09/14 (iCal) - 2020/09/17
Homepage: http://www.dexa.org/dawak2020
Twitter account: https://twitter.com/DEXASociety
Location
Location: Bratislava, Slovakia
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Important dates
Submissions: 2020/04/14
Table of Contents
Tweets by https://twitter.com/DEXASociety
">Tweets by {{{Twitter account}}}


Scopes

  • 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