5th International Workshop on Big Data, Nanjing, China

Abbrevation

Big Data

Link

http://www.cyberc.org

City

Nanjing

Country

China

Deadline Paper

2017-04-15

Start Date

2017-10-12

End Date

2017-10-14

Abstract

Scope: Big Data addresses the challenge to store, search, share, learn, visualize, and analyze massive datasets in forms of structured or unstructured data. The effective process, learn, and analysis of the Big Data has been evidenced for bringing us substantial commercial benefits for a variety of applications. The conventional methods are limited to provide desirable operations on effective Big Data analytics. The Big Data workshop 2017 (2017 BigData) is to promote the research works in this emerging area of Big Data–inspired computing, networks, systems, learning, and applications. 2017 BigData, held in conjunction with CyberC 2017, aims to provide an informative forum for R&D discussions and presentation of new ideas and research results on the broad topics of Big Data Research, Development, and Applications. It solicits high–quality papers that illustrate novel Big Data models, machine learning, architecture and infrastructure, management, search and processing, security and privacy, applications, surveys and industrial experiences. Authors of 2017 BigData are promoted to freely enjoy CyberC 2017 and Big Data Summit. Both of the events are co–sponsored and participated by several Big Data related industry companies. Authors are cordially invited to submit original research papers in any aspects of Big Data with emphasis on but are not limited to the following topics: Big Data Theory and Foundation · Theoretical and Computational Models for Big Data · Information Quantitative and Qualitative for Big Data · Theories and Methodologies for Big Data Processing · Architectures and Designs of Big Data Processing Systems · Machine Learning Theory for Big Data Big Data Infrastructure · Big Data Streaming Platforms for real–time data analytics · Cloud/Grid/Stream Computing for Big Data · High Performance/Parallel Computing Platforms for Big Data · System Architectures, Platforms, Design, and Deployment for Big Data · Energy–efficient Computing for Big Data · Programming Models and Environments for Cluster, Cloud, and Grid Computing Big Data Management · Advanced Database and Web Applications for Big Data · Data Model and Structure for Big Data · Data Preservation and Provenance · Interfaces to Database Systems and Analytics Software · Data and Information Integration and Fusion for Big Data · Data Management for Mobile, Pervasive and Grid Computing · Scientific and Social Data Management and Workflow Optimization Big Data Machine Learning and Processing · Advanced Big Data Machine Learning architecture and algorithms · Big Data Search Architecture, Scalability, and Efficiency · Algorithms and Architectures for Big Data Search, Mining and Processing · Search, Store and Process Big Data in Distributed, Grid and Cloud Systems · Semantic–based Big Data Analytics and Processing · Multi–Structured Multi–Domain Big Data Fusion and Integration · Ontology Representations and Processing in Big Data · Automatic and Machine Learning Methods for Big Data · Hadoop and MapReduce–based Approaches for Big Data Processing Big Data Protection, Security, and Privacy · Threat and Intrusion Detection for High–Speed Networks · High Performance and Efficiency Data Cryptography · Privacy Threats Analysis for Big Data Systems · Visualizing Large–Scale Security Data · Security and Risk in Big Data Processing · Trust, Reputation and Recommendation Systems for Big Data Systems · Privacy and Security Preservation for Multi–Level Security (MLS) Cross–domain Big Data Computing System Big Data Applications · Big Data Applications and Software in Science, Engineering, Healthcare, Finance, Business, Transportation, Telecommunications, etc. · Big Data Analytics in Small Business Enterprises, Public Sector and Government. · Big Data Industry Standards · Development and Deployment Experiences with Big Data Systems.