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*Example topics of interest includes but is not limited to the following: *1. 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 * *2. 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 * *3. 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 * *4. 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 * *5. Ethics, Privacy and Trust in Big Data Systems *Techniques and models for fairness and diversity *Experimental studies of fairness, diversity, accountability, and transparency *Techniques and models for transparency and interpretability *Trade-offs between transparency and privacy *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 Preserving Big Data Collection/Analytics *HCI Challenges for Big Data Security & Privacy *Trust management in IoT and other Big Data Systems * *6. Hardware/OS Acceleration for Big Data *FPGA/CGRA/GPU accelerators for Big Data applications *Operating system support and runtimes for hardware accelerators *Programming models and platforms for accelerators *Domain-specific and heterogeneous architectures *Novel system organizations and designs *Computation in memory/storage/network *Persistent, non-volatile and emerging memory for Big Data *Operating system support for high-performance network architectures * *7. 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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