活动简介

The manufacturing industry, which is vital to all economies, is being challenged to improve its efficiency across the product life cycle and the value chain. The critical aspect of "smartness" is enabled by advanced data analytics for understanding, prediction, and control of manufacturing systems across the product lifecycle (design analytics, production analytics, and use and post-use analytics) and to the extended networked enterprises. Continuous improvements in sensor technologies, data acquisition systems, and data mining and big data analytics allow the manufacturing industry to effectively and efficiently collect large, rapid, and diverse volumes of data and get valuable insights from this data. Big data analytics is becoming a key competitive differentiator having great potential for converting raw data into information assets for smarter decision making during design, manufacturing, use and post use.

征稿信息

重要日期

2016-09-20
初稿截稿日期

征稿范围

Big Data Science and Foundations

  • Novel Theoretical Models for Big Data

  • New Computational Models for Big Data

  • Data and Information Quality for Big Data

  • New Data Standards

Big Data Infrastructure

  • Cloud/Grid/Stream Computing for Big Data

  • High Performance/Parallel Computing Platforms for Big Data

  • Autonomic Computing and Cyber-infrastructure, System Architectures, Design and Deployment

  • Energy-efficient Computing for Big Data

  • Programming Models and Environments for Cluster, Cloud, and Grid Computing to Support Big Data

  • Software Techniques andArchitectures in Cloud/Grid/Stream Computing

  • Big Data Open Platforms

  • New Programming Models for Big Data beyond Hadoop/MapReduce, STORM

  • Software Systems to Support Big Data Computing

Big Data Management

  • Search and Mining of variety of data including scientific and engineering, social, sensor/IoT/IoE, and multimedia data

  • Algorithms and Systems for Big Data Search

  • Distributed, and Peer-to-peer Search

  • Big Data Search Architectures, Scalability and Efficiency

  • Data Acquisition, Integration, Cleaning, and Best Practices

  • Visualization Analytics for Big Data

  • Computational Modeling and Data Integration

  • Large-scale Recommendation Systems and Social Media Systems

  • Cloud/Grid/Stream Data Mining- Big Velocity Data

  • Link and Graph Mining

  • Semantic-based Data Mining and Data Pre-processing

  • Mobility and Big Data

  • Multimedia and Multi-structured Data- Big Variety Data

Big Data Search and Mining

  • Social Web Search and Mining

  • Web Search

  • Algorithms and Systems for Big Data Search

  • Distributed, and Peer-to-peer Search

  • Big Data Search Architectures, Scalability and Efficiency

  • Data Acquisition, Integration, Cleaning, and Best Practices

  • Visualization Analytics for Big Data

  • Computational Modeling and Data Integration

  • Large-scale Recommendation Systems and Social Media Systems

  • Cloud/Grid/StreamData Mining- Big Velocity Data

  • Link and Graph Mining

  • Semantic-based Data Mining and Data Pre-processing

  • Mobility and Big Data

  • Multimedia and Multi-structured Data-Big Variety Data

Big Data Security, Privacy and Trust

  • Intrusion Detection for Gigabit Networks

  • Anomaly and APT Detection in Very Large Scale Systems

  • High Performance Cryptography

  • Visualizing Large Scale Security Data

  • Threat Detection using Big Data Analytics

  • Privacy Threats of Big Data

  • Privacy Preserving Big Data Collection/Analytics

  • HCI Challenges for Big Data Security & Privacy

  • User Studies for any of the above

  • Sociological Aspects of Big Data Privacy

  • Trust management in IoT and other Big Data Systems

Big Data Applications

  • Complex Big Data Applications in Science, Engineering, Medicine, Healthcare, Finance, Business, Law, Education, Transportation, Retailing, Telecommunication

  • Big Data Analytics in Small Business Enterprises (SMEs)

  • Big Data Analytics in Government, Public Sector and Society in General

  • Real-life Case Studies of Value Creation through Big Data Analytics

  • Big Data as a Service

  • Big Data Industry Standards

  • Experiences with Big Data Project Deployments

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重要日期
  • 会议日期

    12月05日

    2016

    12月08日

    2016

  • 09月20日 2016

    初稿截稿日期

  • 12月08日 2016

    注册截止日期

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