July 2019 - sem-grd - lists.ogf.org

IEEE BigData 2019 CFP deadline: Aug 19, 2019
by CFP Conference 27 Jul '19

27 Jul '19

*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 Data Search 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/Stream Data 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. Novel system organizations and designs f. Computation in memory/storage/network g. Persistent, non-volatile and emerging memory for Big Data h. Operating system support for high-performance network architectures 7. Big Data Applications a. Complex Big Data Applications in Science, Engineering, Medicine, Healthcare, Finance, Business, Law, Education, Transportation, Retailing, Telecommunication b. Big Data Analytics in Small Business Enterprises (SMEs), c. Big Data Analytics in Government, Public Sector and Society in General d. Real-life Case Studies of Value Creation through Big Data Analytics e. Big Data as a Service f. Big Data Industry Standards g. Experiences with Big Data Project Deployments *INDUSTRIAL Track* The Industrial Track solicits papers describing implementations of Big Data solutions relevant to industrial settings. The focus of industry track is on papers that address the practical, applied, or pragmatic or new research challenge issues related to the use of Big Data in industry. We accept full papers (up to 10 pages) and extended abstracts (2-4 pages). *Student Travel Award* IEEE Big Data 2019 will offer*student travel *to student authors (including post-docs) *Paper Submission:* Please submit a full-length paper (up to *10-page IEEE 2-column format*) through the online submission system. https://wi-lab.com/cyberchair/2019/bigdata19/scripts/submit.php?subarea=BigD Papers should be formatted to IEEE Computer Society Proceedings Manuscript Formatting Guidelines (see link to "formatting instructions" below). *Formatting Instructions* 8.5" x 11" (DOC <ftp://pubftp.computer.org/press/outgoing/proceedings/instruct8.5x11x2.doc>, PDF <ftp://pubftp.computer.org/press/outgoing/proceedings/instruct8.5x11x2.pdf>) *LaTex Formatting Macros* <ftp://pubftp.computer.org/Press/Outgoing/proceedings/IEEE_CS_Latex8.5x11x2.…> *Important Dates:* Electronic submission of full papers: August 19, 2019 Notification of paper acceptance: Oct 16, 2019 Camera-ready of accepted papers: Nov 10, 2019 Conference: Dec 9-12, 2019 *Conference Co-Chairs:* Dr. Roger Barga, Amazon.com, USA Prof Carlo Zaniolo, UCLA, USA *Program Co-Chairs:* Dr. Chaitanya Baru, San Diego Supercomputer Center/UC San Diego, USA Dr, Jun (Luke) Huan, Baidu Big Data Lab, China Prof. Latifur Khan, University of Texas at Dallas, USA *Vice Chairs in Big Data Science and Foundations* Prof. Jingrui He, UIUC, USA Prof. Wenqing Hu, Missouri S&T University, USA *Vice Chairs in Big Data Infrastructure* Prof. Hanghang Tong, UIUC, USA Dr. Yinglong Xia, Huawei, USA *Vice Chairs in Big Data Management* Prof. Christopher Jermaine, Rice University, USA Prof. Zhou Yongluan, Univ. of Copenhagen, Denmark *Vice Chairs in Big Data Search and Mining* Prof. Quanquan Gu, UCLA, USA Prof. Aditya Prakash, Virginia Tech, USA *Vice Chairs in Big Data Security, Privacy and Trust* Prof. Dongwon Lee, Penn State University, USA Prof. Julia Stoyanovich, New York University, USA *Vice Chairs in Hardware/OS Accelerating for Big Data* Prof. Sang-Woo Jun, UC Irvine, USA Prof. Harry Xu, UCLA, USA *Vice Chairs in Big Data Applications* Prof. Xia Ning, Ohio State University, USA Prof. Tim Weninger, Univ. of Notre Dame, USA *Industry and Government Program Committee Co-Chairs* Dr. Ronay Ak, NVIDIA, USA Dr. Yuanyuan Tian, IBM Almaden Research Center, USA, To subscribe to this list, the user sends an email, with blank subject line, to listserv(a)lists.drexel.edu. In the text box, the user types: subscribe BIGDATA To unsubscribe from a list, the user sends an email to listserv(a)lists.drexel.edu with the message signoff BIGDATA ######################################################################## To unsubscribe from the BIGDATA list, click the following link: http://lists.drexel.edu/cgi-bin/wa?TICKET=NzM3Mjk3IHNlbS1ncmRAT0dGLk9SRyBCS…

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Call for papers: Special Issue on Language Technology and Knowledge Graphs
by Marieke van Erp 23 Jul '19

23 Jul '19

Special Issue on Language Technology and Knowledge Graphs Submission deadline: 30 September 2019 Call for Papers Language understanding and knowledge engineering are among the most active research and development areas due to the proliferation of big data. This special issue on Language Technology and Knowledge Graphs is devoted to gather and present innovative research, systems and applications that address the challenges in the broad areas of language and knowledge intelligence, presenting a platform for researchers to share their recent observations and achievements in the field. Special topics for this special issue include but are not limited to: 1. Textual Entailment and Knowledge * Textual entailment * Fact checking * Fake news detection * Argumentation mining 2. Knowledge-Guided NLP * Question answering and reading comprehension * Dialogue systems * Information Retrieval * Multilinguality * Recommender systems * Machine Translation * Knowledge-Guided Deep Learning * Complex knowledge-driven Information Extraction tasks e.g., relation extraction, event extraction * Methods and metrics for evaluation of semantic annotations with respect to ontologies * Knowledge-driven entity disambiguation and resolution 3. Contextual Knowledge Graphs and Language Technology * Extracting and modelling temporally bounded information * Dealing with culturally-aware information * Handling domain specificity of information 4. Information Extraction for Knowledge Graphs * Extraction from unstructured versus semi-structured textual sources (e.g. tables) * Dealing with the imperfections of Information Extraction techniques in the Semantic Web setting and their impact * Multi-source or multilingual Information Extraction for ontology population * Information extraction subtasks (e.g., terminology extraction, relation extraction, coreference resolution) for the Semantic Web * Methods and metrics for evaluation of Information Extraction for the Semantic Web 5. Applications and Architectures * Knowledge-based Information Extraction for specific domains and applications, e.g. business analytics, healthcare and biomedicine, cultural heritage etc. * Information Extraction for social media mining * Scalability of tools and resources * Platforms and architectures for automatic and semi-automatic semantic annotation * Tools and methodologies for building and managing complex processing workflows Types of papers Research papers describing well-identified scientific contributions which are thoroughly evaluated. Those papers are typically 15-20 pages long. System and Resource papers that focus on the description of systems or resources relevant to this special issue where the authors fully detail the design, construction, implementation and usage as well as demonstrate its usefulness. Those papers are expected to be 8-10 pages long. Please select VSI: LT&KGs in the submission system. More information and submission: https://www.journals.elsevier.com/journal-of-web-semantics/call-for-papers/… For questions contact: marieke.van.erp(a)dh.huc.knaw.nl<mailto:marieke.van.erp@dh.huc.knaw.nl> Important Dates * Submission deadline: 30 September 2019 * Author notification: 17 November 2019 * Publication: Q1 2020 Guest Editors * Marieke van Erp, KNAW Humanities Cluster, DHLab, the Netherlands * Jeff Z. Pan, University of Aberdeen, UK * Zhiyuan Liu, Tsinghua University, China -- Digital Humanities Lab / dhlab.nl<http://dhlab.nl> KNAW Humanities Cluster / huc.knaw.nl<http://huc.knaw.nl> http://www.mariekevanerp.com

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IEEE BigData 2019 Call for Papers
by CFP Conference 16 Jul '19

16 Jul '19

*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. Novel system organizations and designs f. Computation in memory/storage/network g. Persistent, non-volatile and emerging memory for Big Data h. Operating system support for high-performance network architectures 7. Big Data Applications a. Complex Big Data Applications in Science, Engineering, Medicine, Healthcare, Finance, Business, Law, Education, Transportation, Retailing, Telecommunication b. Big Data Analytics in Small Business Enterprises (SMEs), c. Big Data Analytics in Government, Public Sector and Society in General d. Real-life Case Studies of Value Creation through Big Data Analytics e. Big Data as a Service f. Big Data Industry Standards g. Experiences with Big Data Project Deployments *INDUSTRIAL Track* The Industrial Track solicits papers describing implementations of Big Data solutions relevant to industrial settings. The focus of industry track is on papers that address the practical, applied, or pragmatic or new research challenge issues related to the use of Big Data in industry. We accept full papers (up to 10 pages) and extended abstracts (2-4 pages). *Student Travel Award* IEEE Big Data 2019 will offer*student travel *to student authors (including post-docs) *Paper Submission:* Please submit a full-length paper (up to *10-page IEEE 2-column format*) through the online submission system. https://wi-lab.com/cyberchair/2019/bigdata19/scripts/submit.php?subarea=BigD Papers should be formatted to IEEE Computer Society Proceedings Manuscript Formatting Guidelines (see link to "formatting instructions" below). *Formatting Instructions* 8.5" x 11" (DOC <ftp://pubftp.computer.org/press/outgoing/proceedings/instruct8.5x11x2.doc>, PDF <ftp://pubftp.computer.org/press/outgoing/proceedings/instruct8.5x11x2.pdf>) *LaTex Formatting Macros* <ftp://pubftp.computer.org/Press/Outgoing/proceedings/IEEE_CS_Latex8.5x11x2.…> *Important Dates:* Electronic submission of full papers: August 19, 2019 Notification of paper acceptance: Oct 16, 2019 Camera-ready of accepted papers: Nov 10, 2019 Conference: Dec 9-12, 2019 *Conference Co-Chairs:* Dr. Roger Barga, Amazon.com, USA Prof Carlo Zaniolo, UCLA, USA *Program Co-Chairs:* Dr. Chaitanya Baru, San Diego Supercomputer Center/UC San Diego, USA Dr, Jun (Luke) Huan, Baidu Big Data Lab, China Prof. Latifur Khan, University of Texas at Dallas, USA *Vice Chairs in Big Data Science and Foundations* Prof. Jingrui He, UIUC, USA Prof. Wenqing Hu, Missouri S&T University, USA *Vice Chairs in Big Data Infrastructure* Prof. Hanghang Tong, UIUC, USA Dr. Yinglong Xia, Huawei, USA *Vice Chairs in Big Data Management* Prof. Christopher Jermaine, Rice University, USA Prof. Zhou Yongluan, Univ. of Copenhagen, Denmark *Vice Chairs in Big Data Search and Mining* Prof. Quanquan Gu, UCLA, USA Prof. Aditya Prakash, Virginia Tech, USA *Vice Chairs in Big Data Security, Privacy and Trust* Prof. Dongwon Lee, Penn State University, USA Prof. Julia Stoyanovich, New York University, USA *Vice Chairs in Hardware/OS Accelerating for Big Data* Prof. Sang-Woo Jun, UC Irvine, USA Prof. Harry Xu, UCLA, USA *Vice Chairs in Big Data Applications* Prof. Xia Ning, Ohio State University, USA Prof. Tim Weninger, Univ. of Notre Dame, USA *Industry and Government Program Committee Co-Chairs* Dr. Ronay Ak, NVIDIA, USA Dr. Yuanyuan Tian, IBM Almaden Research Center, USA, To subscribe to this list, the user sends an email, with blank subject line, to listserv(a)lists.drexel.edu. In the text box, the user types: subscribe BIGDATA To unsubscribe from a list, the user sends an email to listserv(a)lists.drexel.edu with the message signoff BIGDATA ######################################################################## To unsubscribe from the BIGDATA list, click the following link: http://lists.drexel.edu/cgi-bin/wa?TICKET=NzM3Mjg1IHNlbS1ncmRAT0dGLk9SRyBCS…

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