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BigDataSE 2018 : The 12th IEEE International Conference On Big Data Science and Engineering


Link: http://Website:
When Aug 1, 2018 - Aug 3, 2018
Where New York, USA
Submission Deadline Feb 15, 2018
Notification Due Mar 15, 2018
Final Version Due Jun 15, 2018
Categories    big data novel theory   big data novel applications   big data novel algorithm   big data standards

Call For Papers

Big data is an emerging paradigm applied to datasets whose size is beyond the ability of commonly used software tools to capture, manage, and process the data within a tolerable elapsed time. Such datasets are often from various sources (Variety) yet unstructured such as social media, sensors, scientific applications, surveillance, video and image archives, Internet texts and documents, Internet search indexing, medical records, business transactions and web logs; and are of large size (Volume) with fast data in/out (Velocity). More importantly, big data has to be of high value (Value) and establish trust in it for business decision making (Veracity). Various technologies are being discussed to support the handling of big data such as massively parallel processing databases, scalable storage systems, cloud computing platforms, and MapReduce.

The 12th IEEE International Conference On Big Data Science and Engineering (IEEE BigDataSE-18) will be held in New York, USA from August 1st to 3rd, 2018. IEEE BigDataSE is an international forum for presenting and discussing emerging ideas and trends in Big Data from both the research community as well as the industry. Topics of interest include, but are not limited to:

Systems, Models and Algorithms
Big Data novel theory, algorithm and applications
Big Data standards
Big Data mining and analytics
Big Data Infrastructure, MapReduce and Cloud Computing
Big Data visualization
Big Data curation and management
Big Data semantics, scientific discovery and intelligence
Big Data performance analysis and large-scale deployment
Security, privacy, trust, and legal issues to big data
Big Data vs Big Business and Big Industry
Large data stream processing on cloud
Large incremental datasets on cloud
Distributed and federated datasets
NoSQL data stores and DB scalability
Big Data placement, scheduling, and optimization
Distributed file systems for Big Data
MapReduce for Big Data processing, resource scheduling and SLA
Performance characterization, evaluation and optimization
Simulation and debugging systems and tools for MapReduce and Big Data
Volume, Velocity, Variety, Value and Veracity of Big Data
Multiple source data processing and integration with MapReduce
Storage and computation management of Big Data
Large-scale big data workflow management
Mobility and big data
Sensor network, social network and big data
Big data applications

General Chair:
Sun-Yuan Kung, Princeton University, USA
Albert Zomaya, University of Sydney, Australia
Meikang Qiu, Pace University, USA

Program Chair:
Zheng Yan, Xidian University, China; Aalto University, Finland
Yulei Wu, University of Exeter, UK

Technical Program Committee:
Arvind Agarwal, IBM India Research Lab, India
Nour Ali, Brunel University, UK
Amparo Alonso-Betanzos, University of A Coruña, Spain
Danilo Ardagna, Politecnico di Milano, Italy
Ismailcem Arpinar, University of Georgia, USA
Martin Berzins, SCI Institute, University of Utah, USA
Rajdeep Bhowmik, Cisco Systems, Inc., USA
Mario Bravetti, University of Bologna, Italy
Vineet Chadha, Huawei Innovation Center of Research, USA
Rong Chang, IBM, USA
Lijun Chang, University of Sydney, Australia
Keke Chen, Wright State University, USA
Zhiyuan Chen, University of Maryland Baltimore County, USA
Dickson K.W. Chiu, University of Hong Kong, China
Kenneth Chiu, SUNY Binghamton, USA
Jin-Hee Cho, U.S. Army Research Laboratory, USA
Byron Choi, Hong Kong Baptist University, China Alfredo Cuzzocrea, ICAR-CNR and University of Calabria, Italy Jun Dai, California State University, Sacramento, USA Mark Embrechts, RPI, USA Simon Fong, University of Macau, Macau SAR, China
Yulong Fu, Xidian University, China
Ricardo José Gabrielli Barreto Campello, James Cook University, Australia
Keke Gai, Pace University, USA
Aris Gkoulalas-Divanis, IBM Watson Health, Cambridge, MA, USA
Clemens Grelck, University of Amsterdam, Netherlands
Lawrence Hall, University of South Florida, USA Francisco Herrera, University of Granada, Spain Jen-Wei Hsieh, National Taiwan University, Taiwan Robert Hsu, Chung Hua University, Taiwan Yaochu Jin, University of Surrey, UK
Xuyang Jing, Xidian University, China
Philippe Lenca, IMT Atlantique, France
Yi Murphey, University of Michigan, USA
Saurabh Nagrecha, Capital One, USA
Zhonghong Ou, Beijing University of Posts and Telecommunications, China
Meikang Qiu, Pace University, USA
Abdelmounaam Rezgui, New Mexico Tech, USA
Thomas A. Runkler, Siemens Corporate Technology, Germany
Frank-Michael Schleif, University of Applied Sciences Wuerzburg-Schweinfurt, Germany
Friedhelm Schwenker, Ulm University, Germany
Hong-Linh Truong, Vienna University of Technology, Austria
Alfredo Vellido, Universitat Politècnica de Catalunya, Spain
Sebastián Ventura, University of Cordoba, Spain
Lipo Wang, Nanyang Technological University, Singapore
Mingjun Wang, Xidian University, China Guandong Xu, University of Technology, Sydney, Australia Yang Yang, Northwestern University, USA Guoxian Yu, Southwest University, China Min-Ling Zhang, Southeast University, China

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