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BigDataSE 2018 : The 12th IEEE International Conference On Big Data Science and Engineering | |||||||||||||||
Link: http://Website: http://www.cloud-conf.net/BigDataSE18/index.html | |||||||||||||||
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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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