Difference between revisions of "DaWaK 2020"
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|City=Bratislava | |City=Bratislava | ||
|Country=Slovakia | |Country=Slovakia | ||
| + | |Paper deadline=2020/04/14 | ||
| + | |Notification=2020/05/20 | ||
| + | |Camera ready=2020/06/19 | ||
| + | |Submitting link=https://easychair.org/conferences/?conf=dawak2020 | ||
| + | |has general chair=Bernhard Moser | ||
| + | |has program chair=Min Song, Il-Yeol Song | ||
| + | |pageCreator=Saskia.Ernert | ||
| + | |pageEditor=Saskia.Ernert | ||
| + | |contributionType=1 | ||
}} | }} | ||
=== Scopes === | === 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 |
Latest revision as of 18:56, 1 April 2022
| 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 |
| Submitting link: | https://easychair.org/conferences/?conf=dawak2020 |
| Location | |
| Location: | Bratislava, Slovakia |
| Important dates | |
| Papers: | 2020/04/14 |
| Submissions: | 2020/04/14 |
| Notification: | 2020/05/20 |
| Camera ready due: | 2020/06/19 |
| Committees | |
| General chairs: | Bernhard Moser |
| PC chairs: | Min Song, Il-Yeol Song |
| Table of Contents | |
| Tweets by https://twitter.com/DEXASociety | |
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