[UAI] IEEE BigData 2019 Call for Papers
2019 IEEE International Conference on Big Data (IEEE Big Data 2019) http://cci.drexel.edu/bigdata/bigdata2019/index.html December 9-12, 2019, Los Angeles, CA, USA In recent years, “Big Data” has become a new ubiquitous term. Big Data is transforming science, engineering, medicine, healthcare, finance, business, and ultimately our society itself. The IEEE Big Data conference series started in 2013 has established itself as the top tier research conference in Big Data. * The first conference IEEE Big Data 2013 had more than 400 registered participants from 40 countries ( http://cci.drexel.edu/bigdata/bigdata2013/) and the regular paper acceptance rate is 17.0%. * The IEEE Big Data 2017 ( http://cci.drexel.edu/bigdata/bigdata2017/ , regular paper acceptance rate: 17.8%) was held in Boston, MA, Dec 11-14, 2017 with close to 1000 registered participants from 50 countries. * The IEEE Big Data 2018 ( http://cci.drexel.edu/bigdata/bigdata2018/ , regular paper acceptance rate: 19.7%) was held in Seattle, WA, Dec 10-13, 2018 with close to 1100 registered participants from 47 countries. The 2019 IEEE International Conference on Big Data (IEEE BigData 2019) will continue the success of the previous IEEE Big Data conferences. It will provide a leading forum for disseminating the latest results in Big Data Research, Development, and Applications. We solicit high-quality original research papers (and significant work-in-progress papers) in any aspect of Big Data with emphasis on 5Vs (Volume, Velocity, Variety, Value and Veracity), including the Big Data challenges in scientific and engineering, social, sensor/IoT/IoE, and multimedia (audio, video, image, etc.) big data systems and applications. The conference adopts single-blind review policy. We expect to have a very high quality and exciting technical program at Los Angeles this year. 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 *
[UAI] IEEE Big Data 2019 - Call for Papers
2019 IEEE International Conference on Big Data (IEEE Big Data 2019) http://cci.drexel.edu/bigdata/bigdata2019/index.html December 9-12, 2019, Los Angeles, CA, USA In recent years, “Big Data” has become a new ubiquitous term. Big Data is transforming science, engineering, medicine, healthcare, finance, business, and ultimately our society itself. The IEEE Big Data conference series started in 2013 has established itself as the top tier research conference in Big Data. * The first conference IEEE Big Data 2013 had more than 400 registered participants from 40 countries ( http://cci.drexel.edu/bigdata/bigdata2013/) and the regular paper acceptance rate is 17.0%. * The IEEE Big Data 2017 ( http://cci.drexel.edu/bigdata/bigdata2017/ , regular paper acceptance rate: 17.8%) was held in Boston, MA, Dec 11-14, 2017 with close to 1000 registered participants from 50 countries. * The IEEE Big Data 2018 ( http://cci.drexel.edu/bigdata/bigdata2018/ , regular paper acceptance rate: 19.7%) was held in Seattle, WA, Dec 10-13, 2018 with close to 1100 registered participants from 47 countries. The 2019 IEEE International Conference on Big Data (IEEE BigData 2019) will continue the success of the previous IEEE Big Data conferences. It will provide a leading forum for disseminating the latest results in Big Data Research, Development, and Applications. We solicit high-quality original research papers (and significant work-in-progress papers) in any aspect of Big Data with emphasis on 5Vs (Volume, Velocity, Variety, Value and Veracity), including the Big Data challenges in scientific and engineering, social, sensor/IoT/IoE, and multimedia (audio, video, image, etc.) big data systems and applications. The conference adopts single-blind review policy. We expect to have a very high quality and exciting technical program at Los Angeles this year. 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 *
[UAI] Call for Papers - IEEE BigData 2019
Call for Papers 2019 IEEE International Conference on Big Data (IEEE BigData 2019) http://bigdataieee.org/BigData2019/ December 10-13, 2019, Los Angeles, CA, USA In recent years, “Big Data” has become a new ubiquitous term. Big Data is transforming science, engineering, medicine, healthcare, finance, business, and ultimately our society itself. The IEEE Big Data conference series started in 2013 has established itself as the top tier research conference in Big Data. * The first conference IEEE Big Data 2013 had more than 400 registered participants from 40 countries ( http://bigdataieee.org/BigData2013/) and the regular paper acceptance rate is 17.0%. * The IEEE Big Data 2017 ( http://bigdataieee.org/BigData2017/ , regular paper acceptance rate: 17.8%) was held in Boston, MA, Dec 11-14, 2017 with close to 1000 registered participants from 50 countries. * The IEEE Big Data 2018 ( http://bigdataieee.org/BigData2018/ , regular paper acceptance rate: 19.7%) was held in Seattle, WA, Dec 10-13, 2018 with close to 1100 registered participants from 47 countries. The 2019 IEEE International Conference on Big Data (IEEE BigData 2019) will continue the success of the previous IEEE Big Data conferences. It will provide a leading forum for disseminating the latest results in Big Data Research, Development, and Applications. We solicit high-quality original research papers (and significant work-in-progress papers) in any aspect of Big Data with emphasis on 5Vs (Volume, Velocity, Variety, Value and Veracity), including the Big Data challenges in scientific and engineering, social, sensor/IoT/IoE, and multimedia (audio, video, image, etc.) big data systems and applications. The conference adopts single-blind review policy. We expect to have a very high quality and exciting technical program at Seattle this year. Example topics of interest includes but is not limited to the following: 1. Big Data Science and Foundations a. Novel Theoretical Models for Big Data b. New Computational Models for Big Data c. Data and Information Quality for Big Data d. New Data Standards 2. Big Data Infrastructure a. Cloud/Grid/Stream Computing for Big Data b. High Performance/Parallel Computing Platforms for Big Data c. Autonomic Computing and Cyber-infrastructure, System Architectures, Design and Deployment d. Energy-efficient Computing for Big Data e. Programming Models and Environments for Cluster, Cloud, and Grid Computing to Support Big Data f. Software Techniques and Architectures in Cloud/Grid/Stream Computing g. Big Data Open Platforms h. New Programming Models for Big Data beyond Hadoop/MapReduce, STORM i. Software Systems to Support Big Data Computing 3. Big Data Management a. Search and Mining of variety of data including scientific and engineering, social, sensor/IoT/IoE, and multimedia data b. Algorithms and Systems for Big DataSearch c. Distributed, and Peer-to-peer Search d. Big Data Search Architectures, Scalability and Efficiency e. Data Acquisition, Integration, Cleaning, and Best Practices f. Visualization Analytics for Big Data g. Computational Modeling and Data Integration h. Large-scale Recommendation Systems and Social Media Systems i. Cloud/Grid/Stream Data Mining- Big Velocity Data j. Link and Graph Mining k. Semantic-based Data Mining and Data Pre-processing l. Mobility and Big Data m. Multimedia and Multi-structured Data- Big Variety Data 4. Big Data Search and Mining a. Social Web Search and Mining b. Web Search c. Algorithms and Systems for Big Data Search d. Distributed, and Peer-to-peer Search e. Big Data Search Architectures, Scalability and Efficiency f. Data Acquisition, Integration, Cleaning, and Best Practices g. Visualization Analytics for Big Data h. Computational Modeling and Data Integration i. Large-scale Recommendation Systems and Social Media Systems j. Cloud/Grid/StreamData Mining- Big Velocity Data k. Link and Graph Mining l. Semantic-based Data Mining and Data Pre-processing m. Mobility and Big Data n. Multimedia and Multi-structured Data- Big Variety Data 5. Ethics, Privacy and Trust in Big Data Systems a. Techniques and models for fairness and diversity b. Experimental studies of fairness, diversity, accountability, and transparency c. Techniques and models for transparency and interpretability d. Trade-offs between transparency and privacy e. Intrusion Detection for Gigabit Networks f. Anomaly and APT Detection in Very Large Scale Systems g. High Performance Cryptography h. Visualizing Large Scale Security Data i. Threat Detection using Big Data Analytics j. Privacy Preserving Big Data Collection/Analytics k. HCI Challenges for Big Data Security & Privacy l. Trust management in IoT and other Big Data Systems 6. Hardware/OS Acceleration for Big Data a. FPGA/CGRA/GPU accelerators for Big Data applications b. Operating system support and runtimes for hardware accelerators c. Programming models and platforms for accelerators d. Domain-specific and heterogeneous architectures e
[UAI] Call for Papers - IEEE BigData 2019
Call for Papers 2019 IEEE International Conference on Big Data (IEEE BigData 2019) http://bigdataieee.org/BigData2019/ December 10-13, 2019, Los Angeles, CA, USA In recent years, “Big Data” has become a new ubiquitous term. Big Data is transforming science, engineering, medicine, healthcare, finance, business, and ultimately our society itself. The IEEE Big Data conference series started in 2013 has established itself as the top tier research conference in Big Data. * The first conference IEEE Big Data 2013 had more than 400 registered participants from 40 countries ( http://bigdataieee.org/BigData2013/) and the regular paper acceptance rate is 17.0%. * The IEEE Big Data 2017 ( http://bigdataieee.org/BigData2017/ , regular paper acceptance rate: 17.8%) was held in Boston, MA, Dec 11-14, 2017 with close to 1000 registered participants from 50 countries. * The IEEE Big Data 2018 ( http://bigdataieee.org/BigData2018/ , regular paper acceptance rate: 19.7%) was held in Seattle, WA, Dec 10-13, 2018 with close to 1100 registered participants from 47 countries. The 2019 IEEE International Conference on Big Data (IEEE BigData 2019) will continue the success of the previous IEEE Big Data conferences. It will provide a leading forum for disseminating the latest results in Big Data Research, Development, and Applications. We solicit high-quality original research papers (and significant work-in-progress papers) in any aspect of Big Data with emphasis on 5Vs (Volume, Velocity, Variety, Value and Veracity), including the Big Data challenges in scientific and engineering, social, sensor/IoT/IoE, and multimedia (audio, video, image, etc.) big data systems and applications. The conference adopts single-blind review policy. We expect to have a very high quality and exciting technical program at Seattle this year. Example topics of interest includes but is not limited to the following: 1. Big Data Science and Foundations a. Novel Theoretical Models for Big Data b. New Computational Models for Big Data c. Data and Information Quality for Big Data d. New Data Standards 2. Big Data Infrastructure a. Cloud/Grid/Stream Computing for Big Data b. High Performance/Parallel Computing Platforms for Big Data c. Autonomic Computing and Cyber-infrastructure, System Architectures, Design and Deployment d. Energy-efficient Computing for Big Data e. Programming Models and Environments for Cluster, Cloud, and Grid Computing to Support Big Data f. Software Techniques and Architectures in Cloud/Grid/Stream Computing g. Big Data Open Platforms h. New Programming Models for Big Data beyond Hadoop/MapReduce, STORM i. Software Systems to Support Big Data Computing 3. Big Data Management a. Search and Mining of variety of data including scientific and engineering, social, sensor/IoT/IoE, and multimedia data b. Algorithms and Systems for Big DataSearch c. Distributed, and Peer-to-peer Search d. Big Data Search Architectures, Scalability and Efficiency e. Data Acquisition, Integration, Cleaning, and Best Practices f. Visualization Analytics for Big Data g. Computational Modeling and Data Integration h. Large-scale Recommendation Systems and Social Media Systems i. Cloud/Grid/Stream Data Mining- Big Velocity Data j. Link and Graph Mining k. Semantic-based Data Mining and Data Pre-processing l. Mobility and Big Data m. Multimedia and Multi-structured Data- Big Variety Data 4. Big Data Search and Mining a. Social Web Search and Mining b. Web Search c. Algorithms and Systems for Big Data Search d. Distributed, and Peer-to-peer Search e. Big Data Search Architectures, Scalability and Efficiency f. Data Acquisition, Integration, Cleaning, and Best Practices g. Visualization Analytics for Big Data h. Computational Modeling and Data Integration i. Large-scale Recommendation Systems and Social Media Systems j. Cloud/Grid/StreamData Mining- Big Velocity Data k. Link and Graph Mining l. Semantic-based Data Mining and Data Pre-processing m. Mobility and Big Data n. Multimedia and Multi-structured Data- Big Variety Data 5. Ethics, Privacy and Trust in Big Data Systems a. Techniques and models for fairness and diversity b. Experimental studies of fairness, diversity, accountability, and transparency c. Techniques and models for transparency and interpretability d. Trade-offs between transparency and privacy e. Intrusion Detection for Gigabit Networks f. Anomaly and APT Detection in Very Large Scale Systems g. High Performance Cryptography h. Visualizing Large Scale Security Data i. Threat Detection using Big Data Analytics j. Privacy Preserving Big Data Collection/Analytics k. HCI Challenges for Big Data Security & Privacy l. Trust management in IoT and other Big Data Systems 6. Hardware/OS Acceleration for Big Data a. FPGA/CGRA/GPU accelerators for Big Data applications b. Operating system support and runtimes for hardware accelerators c. Programming models and platforms for accelerators d. Domain-specific and heterogeneous architectures e
