CCBD2019
The International Conference on Cloud Computing and Big Data (CCBD 2019)
Important Dates
About CCBD2019
The International Conference on Cloud Computing and Big Data (CCBD 2019) will be held in Sanya, China during January 3-5, 2019. This Conference will cover issues on Cloud Computing and Big Data. CCBD 2019 is sponsored by Engineering Information Institute, Open Access Library, Scientific Research Publishing, and 1000thinktank. It dedicates to creating a stage for exchanging the latest research results and sharing the advanced research methods.
Topics
Cloud computing, Big dataCall for Papers
Contact Us
Contact Person: Ms Vicky Email: comm_mar@engii.org Tel: +86 150 7134 3477 QQ: 3025797047 WeChat: Engii_Vivian For urgent issue, please contact ic_vicky@163.com
All the accepted papers will be published by "Journal of Computer and Communications" (ISSN:2327-5219), a peer-reviewed open access journal that can ensure the widest dissemination of your published work.
Call For Papers
Cloud computing Cloud Solution Design Patterns Cloud Application Architectures Cloud Middleware Frameworks Cloud Quality and Performance Cloud Optimization and Automation Cloud Resource Virtualization and Composition Cloud Management and Operations Cloud Resource Virtualization and Composition Intelligent Data Processing Semantic web technologies Cloud Architecture Cloud Infrastructure as a Service Cloud Platform as a Service Cloud system design Cloud federation and hybrid cloud infrastructure Programming models and systems Networking technologies for data center Security and Privacy in Cloud Authentication and authorization Cryptographic primitives Reliability and availability Trust and credential management Usability and security Security and privacy in clouds Legacy systems migration Cloud Integrity and Binding Issues Big Data Big Data Analysis Processing Big Data Analysis Analytics and Metrics Big Data Analysis Architectures Big Data Analysis Encryption Big Data Analysis Management Big Data Analysis Search and Mining Machine learning Data mining Approximate and scalable statistical methods Querying and search Data Lifecycle Management for Big Data Storage and analytic architectures Performance and debugging Hardware optimizations for Big Data Data Flow management and scheduling
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