ICBDSDE’18 2018 - International Conference on Big Data and Smart Digital Environment

Big Data Analytics
• Storage, Data, and Analytics Clouds
• Federal Big Data Cloud Configuration, Performance, and Capacity
• Big Data Search
• Big Data applications: Bioinformatics, Multimedia, Smartphones, etc.
• Data mining, graph mining and data science
• Cloud and grid computing for Big Data
• Hardware/software infrastructure for Big Data
• Role of Big Data issues in Pervasive Systems
• Data Mining Techniques in Wireless Sensor Networks
• Big Data Analytics applied to Smart Cities
• Role of Big Data issues in Vehicle Ad Hoc Networks ..
• Challenges and opportunities for the electric energy industry
• Big Data advantages for electric energy trading
• Big Data and Energy Systems Integration
• Big Data and Energy Efficiency
• Big Data and Electricity Demand Forecasting
• Big Data and Energy Management
•IA and information systems
• ICT integration in education
• Information and knowledge management
• Information hiding and watermarking
• Information technologies
• Information theory/coding
• Architectures for Big Data
• The role of Big Data in improving power system operation and protection
• Smart electrical grid with Big Data
• Image Processing and Reconstruction
•Signal Processing
• Video Processing and Reconstruction
•Architecture, management and process for data science
•Cloud computing and service data analysis
•Data warehouses, cloud architectures
•Mathematical Issues in Data Science
•Big Data Issues and Applications
•Large-scale databases
•High performance computing for data analytics
•Large scale optimization
•Data-driven Scientific Research
•Security, trust and risk in big data
•Privacy and protection standards and policies
•Data Quality
•Evaluation and Measurement in Data Science
•Big Data Mining and Knowledge Management
•Case Study of Data Science
•trends and innovative applications of Business Intelligence
•digitalization and big data
•benefits of predictive and advanced analytics
•BI organization – best practices and no-goes.
•Develop a foundation of business trust through data quality, security, privacy, and governance
•Make analytics and insight part of the DNA for every person, action, business process and decision
•Build and execute a world-class, disruptive data and analytics strategy
•Drive innovation through leading technologies – AI, machine learning, blockchain, Virtual/Augmented Reality, IoT and digital twins
•Modernize your infrastructure and adopt new architectural approaches to support digital transformation
•Exploit diverse datasets, diverse teams and diverse thinking to innovate business models
•Lead by enabling the right culture, people, skills and organization
•Accelerate the adoption of new skills, new roles, (such as Chief Data Officer), new ways of working and new data-driven thinking
• 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 and Architectures 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 DataSearch
•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
•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 GeneralReal-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