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Internet of Things (IoT) is a platform and a phenomenon that allows everything to process information, communicate data, analyze context collaboratively and in the service or individuals, organizations and businesses. In the process of doing so, a large amount of data with different formats and content has to be processed efficiently, quickly and intelligently through advanced algorithms, techniques, models and tools. This new paradigm is enabled by the maturity of several different technologies, including the internet, wireless communication, cloud computing, sensors, big data analytics and machine learning algorithms.

Big Data is another paradigm to describe processing of data to make it 'make sense' to people using IoT. Big Data has five characteristics: volume, velocity, variety, veracity and value. There are reports that businesses and research communities equipped with Big Data skills can provide additional incentives, opportunities, funding and innovation to their long-term strategies. The new knowledge, tools, practices, and infrastructures produced will enable breakthrough discoveries and innovation in physical science, engineering, mobile services, medicine, business, education, earth science, security and risk analysis.

For organizations that adopt Big Data, the boundary between the use of private clouds, public clouds, IoT is sometimes very thin to allow better access, performance and efficiency of analyzing the data and understanding the data analysis. A common approach is to develop Big Data in the IoT to deliver "Everything as a Service". In the process of doing so, innovative services known as "Emerging Services and Analytics" can be the highlight and strategic solutions to organizations adopting IoT and Big Data.

征稿信息

重要日期

2016-11-21
初稿截稿日期

征稿范围

AREA 1: BIG DATA RESEARCH

  • Big Data fundamentals – Services Computing, Techniques, Recommendations and Frameworks

  • Modeling, Experiments, Sharing Technologies & Platforms

  • SQL/NoSQL databases, Data Processing Techniques, Visualization and Modern Technologies

  • Analytics, Intelligence and Knowledge Engineering

  • Data Center Enabled Technologies

  • Sensor, Wireless Technologies, APIs

  • Networking and Social Networks

  • Data Management for Large Data

  • Security, Privacy and Risk

  • Software Frameworks (MapReduce, Spark etc) and Simulations

  • Modern Architecture

  • Volume, Velocity, Variety, Veracity and Value

  • Social Science and Implications for Big Data

AREA 2: EMERGING SERVICES AND ANALYTICS

  • Health Informatics as a Service (HIaaS) for any Type of Health Informatics, Computation and Services

  • Big Data as a Service (BDaaS) including Frameworks, Empirical Approaches and Data Processing Techniques

  • Big Data Algorithm, Methodology, Business Models and Challenges

  • Security as a Service including any Algorithms, Methodology and Software Proof-of-concepts

  • Financial Software as a Service (FSaaS) including Risk and Pricing Analysis; Predictive Modeling

  • Education as a Service (EaaS) including e-Learning and Educational Applications

  • Business Process as a Service (BPaaS) including Workflows and Supply Chain in IoT and Big Data

  • Software Engineering Approaches, including Formal Methods, Agile Methods and Theoretical Algorithms for IoT and Big Data

  • Natural Science as a Service (NSaaS) including Weather Forecasting and Weather Data Visualization

  • System Design and Architecture

  • Mobile APIs, Apps, Systems and Prototype

  • Gaming as a Service (GaaS)

  • Framework (conceptual, logical or software)

  • Analytics as a Service (AaaS) for any Types of Analytics

  • Electronic, Logic, Robotic and Electrical Infrastructure, Platforms and Applications

  • Energy-saving and Green IT Systems or Applications

  • Middleware and Agents for IoT and Big Data, Grid and Cluster Computing

  • Integration as a Service (data; service; business; federated IoT and Big Data)

  • Scheduling, Service Duplication, Fairness, Load Balance for SaaS and Analytics

  • Tenant Application Development including Customization, Verification, Simulation, and testing on SaaS and Analytics

  • IaaS, PaaS and SaaS quality of service (QoS), security, reliability, availability, service bus mechanisms

  • Social Networks and Analytics

  • User Evaluations and Case Studies

  • IaaS, PaaS and SaaS, Big Data and Analytics demonstrations and Research Discussions from Computing Scientists, Business IS Academics and Industrial Consultants

  • Wireless Systems and Applications

  • e-Government, e-Commerce, e-Science and Creative Technologies for IoT and Big Data

  • Any emerging services

AREA 3: BIG DATA AND COMPLEX BIOLOGICAL SCIENCE

  • Smart City and Transportation

  • Education and Learning

  • Business, Finance and Management

  • Large-scale Information Systems and Applications

  • Energy, Environment and Natural Science Applications

  • Social Networks Analysis, Media and e-Government

  • Proofs-of-concepts and Large-scale Experiments

  • Risk Modeling, Simulation, Legal Challenges

  • Open data: Issues, Services and Solutions

  • Earth Science Simulation and Processing

  • GPUs and Visualization

  • Case Studies of Real Adoption

  • Biomedical Experiments and Simulations

  • Healthcare Services

  • Health Informatics

  • Cancer and Tumor Studies with Big Data

  • DNA Sequencing

  • Brain, Heart and Organ Simulations and Processing

  • Volume, Velocity, Variety and Veracity for Biological Science

AREA 4: INTERNET OF THINGS (IOT) FUNDAMENTALS

  • Network Design and Architecture

  • Software Architecture and Middleware

  • Mobile Services

  • Data and Knowledge Management

  • Context-awareness and Location-awareness

  • Security, Privacy and Trust

  • Performance Evaluation and Modeling

  • Networking and Communication Protocols

  • Machine to Machine Communications

  • Intelligent Systems for IoT and Services Computing

  • Energy Efficiency

  • Social Implications for IoT

  • Future of IoT and Big Data

  • AREA 5: INTERNET OF THINGS (IOT) APPLICATIONS

  • Technological focus for Smart Environments

  • Next Generation Networks

  • Smart City Examples and Case Studies

  • Data Analysis and Visualization for Smart City, Green Systems and Transport Systems

  • Architecture for secure and interactive IoT

  • Intelligent Infrastructure and Guidance Systems

  • Traffic Theory, Modeling and Simulation

  • Sensor Networks, Remote Diagnosis and Development

  • Transportation Management

  • Pattern Recognition and Behavioral Investigations for Vehicles, Green Systems and Smart City

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重要日期
  • 会议日期

    04月22日

    2017

    04月24日

    2017

  • 11月21日 2016

    初稿截稿日期

  • 04月24日 2017

    注册截止日期

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