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The 13th IEEE International Conference on Big Data Science and Engineering (IEEE BigDataSE-19) Rotorua, New Zealand,5-8 August 2019 INTRODUCTION ---------------------------------------- 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 have been proposed to support the handling of big data such as massively parallel processing databases, scalable storage systems, cloud computing platforms, and MapReduce. Big data is more than simply a matter of size. It is an opportunity to find insights in new and emerging types of data and content, to make business more agile, and to answer questions that were previously considered beyond our reach. SCOPE ---------------------------------------- The 13th IEEE International Conference on Big Data Science and Engineering (IEEE BigDataSE-19) will be held in Rotorua, New Zealand during August 5th - August 8th, 2019. IEEE BigDataSE aims to provide an international forum for presenting and discussing emerging ideas and trends in Big Data from both of the academia and industry. Topics of interest include, but are not limited to: Systems, models, standards, algorithms and applications for Big Data Big Data mining and analytics Big Data Infrastructure, MapReduce and Cloud Computing Big Data visualization Big Data semantics, scientific discovery and intelligence Security, privacy, trust, and legal issues to big data Large data stream processing and 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 Big Data Storage and computation management of Big Data Large-scale big data workflow management Sensor network and social network for Big Data Edge processing and intelligence for Big Data SUBMISSION GUIDELINES ---------------------------------------- Submitted papers must not substantially overlap with papers that have been published or that are simultaneously submitted to a journal or a conference with proceedings. All papers need to be submitted electronically through the conference submission website (https://edas.info/N25994) with PDF format. The materials presented in the papers should not be published or under submission elsewhere. Each paper is limited to 8 pages including figures and references using IEEE Computer Society Proceedings Manuscripts style (two columns, single-spaced, 10 fonts). You can confirm the IEEE Computer Society Proceedings Author Guidelines at the following web page: http://www.computer.org/web/cs-cps/. Manuscript Templates for Conference Proceedings can be found at https://www.ieee.org/conferences_events/conferences/publishing/templates.html. ORGANIZING COMMITTEE ---------------------------------------- General Chairs: Sherman Shen, University of Waterloo, Canada Yaoxue Zhang, Central South University, China TPC Chairs: Ju Ren, Central South University, China Xiaokang Zhou, Shiga University, Japan Steering Committee Chairs: Laurance T. Yang, St. Francis Xavier University, Canada Jinjun Chen, Swinburne University of Technology, Australia IMPORTANT DATES ---------------------------------------- Paper Submission Deadline: 1 April, 2019 Authors Notification: 30 April, 2019 Camera-Ready Paper Due: 20 May, 2019 Early Registration Due: 20 May, 2019 Conference Date: 5-8 August, 2019 -- Xiaokang Zhou (周 暁康), Ph.D. Faculty of Data Science, The Center for Data Science Education and Research, Shiga University 1-1-1 Banba, Hikone, Shiga 522-8522, Japan Email: [email protected]<mailto:[email protected]> Phone: +81-749-27-1290
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