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IEEE BigData 2016 : IEEE International Conference on Big Data

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Link: http://cci.drexel.edu/bigdata/bigdata2016
 
When Dec 5, 2016 - Dec 8, 2016
Where Washington D. C.
Submission Deadline Aug 7, 2016
Notification Due Oct 9, 2016
Final Version Due Nov 5, 2016
Categories    infrastructure   data mining and search   security   applications
 

Call For Papers

IEEE Big Data 2016 Call for Papers

2016 IEEE International Confernece on Big Data(Big Data 2016)
http://cci.drexel.edu/bigdata/bigdata2016/
Nov 15-18, 2016, Washington DC, USA

In recent years, "Big Data" has become a new ubiquitous term. Big Data is transforming science, engineering, medicine, healthcare, finance, business, and ultimately our society itself. The IEEE Big Data conference series started in 2013 has established itself as the top tier research conference in Big Data:

The first conference IEEE Big Data 2013 had more than 400 registered participants from 40 countries.
(Big Data 2013 The regular paper acceptance rate: 17.0%)
The IEEE Big Data 2015 had more than 780 registered participants from 49 countries.
(Big Data 2015 The regular paper acceptance rate: 16.8%)

The 2016 IEEE International Conference on Big Data (IEEE Big Data 2016) will continue the success of the previous IEEE Big Data conferences. It will provide a leading forum for disseminating the latest results in Big Data Research, Development, and Applications.

We solicit high-quality original research papers (and significant work-in-progress papers) in any aspect of Big Data with emphasis on 5Vs (Volume, Velocity, Variety, Value and Veracity), including the Big Data challenges in scientific and engineering, social, sensor/IoT/IoE, and multimedia (audio, video, image, etc.) big data systems and applications. Example topics of interest includes but is not limited to the following::
Big Data Science and Foundations
Novel Theoretical Models for Big Data
New Computational Models for Big Data
Data and Information Quality for Big Data
New Data Standards

Big Data Infrastructure
Cloud/Grid/Stream Computing for Big Data
High Performance/Parallel Computing Platforms for Big Data
Autonomic Computing and Cyber-infrastructure, System Architectures, Design and Deployment
Energy-efficient Computing for Big Data
Programming Models and Environments for Cluster, Cloud, and Grid Computing to Support Big Data
Software Techniques andArchitectures in Cloud/Grid/Stream Computing
Big Data Open Platforms
New Programming Models for Big Data beyond Hadoop/MapReduce, STORM
Software Systems to Support Big Data Computing

Big Data Management
Search and Mining of variety of data including scientific and engineering, social, sensor/IoT/IoE, and multimedia data
Algorithms and Systems for Big Data Search
Distributed, and Peer-to-peer Search
Big Data Search Architectures, Scalability and Efficiency
Data Acquisition, Integration, Cleaning, and Best Practices
Visualization Analytics for Big Data
Computational Modeling and Data Integration
Large-scale Recommendation Systems and Social Media Systems
Cloud/Grid/Stream Data Mining- Big Velocity Data
Link and Graph Mining
Semantic-based Data Mining and Data Pre-processing
Mobility and Big Data
Multimedia and Multi-structured Data- Big Variety Data

Big Data Search and Mining
Social Web Search and Mining
Web Search
Algorithms and Systems for Big Data Search
Distributed, and Peer-to-peer Search
Big Data Search Architectures, Scalability and Efficiency
Data Acquisition, Integration, Cleaning, and Best Practices
Visualization Analytics for Big Data
Computational Modeling and Data Integration
Large-scale Recommendation Systems and Social Media Systems
Cloud/Grid/StreamData Mining- Big Velocity Data
Link and Graph Mining
Semantic-based Data Mining and Data Pre-processing
Mobility and Big Data
Multimedia and Multi-structured Data-Big Variety Data

5. Big Data Security, Privacy and Trust
Intrusion Detection for Gigabit Networks
Anomaly and APT Detection in Very Large Scale Systems
High Performance Cryptography
Visualizing Large Scale Security Data
Threat Detection using Big Data Analytics
Privacy Threats of Big Data
Privacy Preserving Big Data Collection/Analytics
HCI Challenges for Big Data Security & Privacy
User Studies for any of the above
Sociological Aspects of Big Data Privacy
Trust management in IoT and other Big Data Systems

Big Data Applications
Complex Big Data Applications in Science, Engineering, Medicine, Healthcare, Finance, Business, Law, Education, Transportation, Retailing, Telecommunication
Big Data Analytics in Small Business Enterprises (SMEs)
Big Data Analytics in Government, Public Sector and Society in General
Real-life Case Studies of Value Creation through Big Data Analytics
Big Data as a Service
Big Data Industry Standards
Experiences with Big Data Project Deployments

INDUSTRIAL Track

The Industrial Track solicits papers describing implementations of Big Data solutions relevant to industrial settings. The focus of industry track is on papers that address the practical, applied, or pragmatic or new research challenge issues related to the use of Big Data in industry. We accept full papers (up to 10 pages) and extended abstracts (2-4 pages).
Student Travel Award

IEEE Big Data 2016 will offer student travel to student authors (including post-docs)

Journal Publication

A set of papers will be selected for a fast-track review and then published at the IEEE Transactions on Big Data

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