ICCDA 2020: The 4th International Conference on Compute and Data Analysis International Technological University, USA Silicon Valley, IA, United States, March 9-12, 2020 |
Conference website | http://iccda.org/ |
Submission link | https://easychair.org/conferences/?conf=iccda2020 |
Submission deadline | January 15, 2020 |
The International Conference on Compute and Data Analysis (ICCDA), is an annual conference hold each year in United States. It is an international forum for academia and industries to exchange visions and ideas in the state of the art and practice of compute and data analysis.
The previous edition of ICCDA was held in Florida Polytechnic University, Lakeland, Northern Illinois University (NIU) DeKalb and University of Hawaii Maui College, Kahului. ICCDA 2020 conference will be located in International Technological University, USA, in Silicon Valley, San Jose.
Submission Guidelines
Full Paper:
The manuscript in double-column ACM format, up to 10 pages. Manuscripts must be written in English and follow the instructions at ACM Word Template for SIG Site.
To ensure the high quality of the accepted papers, all submissions will be peer-reviewed.Please only submit original material where copyright of all parts is owned by the authors declared and which is not currently under review elsewhere.
Accepted and presented papers will be published into ACM conference proceedings, will be included in the major data base, EI Compendex, Scopus, etc,the proceedings will be distributed during the Conference.
Abstracts:
For those papers do not want to be published but presenting and discussing at the conferece, you can submit an abstract first, for presentation only. Your abstract will not be published but only included in the conference program book.
List of Topics
Topics are interested but not limited to:
Foundations
- Mathematical, probabilistic and statistical models and theories
- Machine learning theories, models and systems
- Knowledge discovery theories, models and systems
- Manifold and metric learning
- Deep learning
- Scalable analysis and learning
- Non-iidness learning
- Heterogeneous data/information integration
- Data pre-processing, sampling and reduction
- Dimensionality reduction
- Feature selection, transformation and construction
- Large scale optimization
- High performance computing for data analytics
- Architecture, management and process for data science
Data analytics, machine learning and knowledge discovery
- Learning for streaming data
- Learning for structured and relational data
- Latent semantics and insight learning
- Mining multi-source and mixed-source information
- Mixed-type and structure data analytics
- Cross-media data analytics
- Big data visualization, modeling and analytics
- Multimedia/stream/text/visual analytics
- Relation, coupling, link and graph mining
- Personalization analytics and learning
- Web/online/social/network mining and learning
- Structure/group/community/network mining
- Cloud computing and service data analysis
Storage, retrieval and search
- Data warehouses, cloud architectures
- Large-scale databases
- Information and knowledge retrieval, and semantic search
- Web/social/databases query and search
- Personalized search and recommendation
- Human-machine interaction and interfaces
- Crowdsourcing and collective intelligence
Committees
Advisory Chair
- Shigang Chen, University of Florida, United States
General Chairs
- May Huang, International Technological University, United States
- Anu Gokhale, Illinois State University, United States
- Sen Zhang, State University of New York College at Oneonta, United States
Program Chairs
- Sule Yildirim Yayilgan, Norwegian University of Science and Technology, Norway
- Shawn X. Wang, California State University, USA
- Emanuel Grant, University of North Dakota, United States
Workshop Chair
- Letian Huang, University of Electronic Science and Technology of China, China
Invited Speakers
-
Alexander Zipf,Heidelberg University, GIScience Research Group, Germany
-
Anu Gokhale,Illinois State University, USA
Publication
Full Paper submitted and accepted after successful registration will be published in the ACM Proceedings (ISBN: 978-1-4503-7644-0), the content will be submitted to the indexing companies for possible indexing, Scopus, Ei compendex,etc.
Contact
Ms. Maggie Lau via
iccda_info@163.com
+86 138 8010 4517