The 12th IEEE International Conference On Big Data Science and Engineering (IEEE BigDataSE-18)

August 1st-3rd, 2018, New York, USA


Important Dates

Paper Submission:  February 15th, 2018

Author Notification May 15th, 2018

Camera-Ready                        June 15th, 2018

Author Registration                June 15th, 2018

Conference Date                     August 1st-3rd, 2018


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


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