Difference between revisions of "Event:55d6114e-2792-4372-b604-bedb1db5a488"

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(Created page with "{{Event |Acronym=ESWEEK 2020 |Title=Embedded Systems Week |Ordinal=16 |In Event Series=Event Series:Cdee022a-45e8-43ce-b77c-755268b309cf |Single Day Event=no |Start Date=2020/...")
 
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{{Event
 
{{Event
|Acronym=ESWEEK 2020
+
|Acronym=DaWaK 2020
|Title=Embedded Systems Week
+
|Title=22nd International Conference on Big Data Analytics and Knowledge Discovery
|Ordinal=16
+
|Type=Conference
|In Event Series=Event Series:Cdee022a-45e8-43ce-b77c-755268b309cf
+
|Homepage=http://www.dexa.org/dawak2020
 +
|Twitter account=https://twitter.com/DEXASociety
 +
|City=Bratislava
 +
|Country=Country:SK
 +
|Submitting link=https://easychair.org/conferences/?conf=dawak2020
 +
|has general chair=Bernhard Moser
 +
|has program chair=Min Song, Il-Yeol Song
 +
|pageCreator=User:Curator 53
 +
|pageEditor=User:Curator 53
 +
|contributionType=1
 +
|In Event Series=Event Series:DaWaK
 
|Single Day Event=no
 
|Single Day Event=no
|Start Date=2020/09/20
+
|Start Date=2020/09/14
|End Date=2020/09/25
+
|End Date=2020/09/17
 
|Event Status=as scheduled
 
|Event Status=as scheduled
|Event Mode=online
+
|Event Mode=on site
|Academic Field=Embedded Systems
 
 
}}
 
}}
 +
{{Event Deadline
 +
|Paper Deadline=2020/04/14
 +
|Notification Deadline=2020/05/20
 +
|Camera-Ready Deadline=2020/06/19
 +
|Submission Deadline=2020/04/14
 +
}}
 +
{{Event Deadline}}
 +
{{S Event}}
 +
=== 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

Revision as of 18:08, 22 September 2022

Deadlines
2020-05-20
2020-04-14
2020-06-19
2020-04-14
Deadlines
Venue
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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
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