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ACM--ICCDA--Ei, Scopus, and ISI 2018 : ACM--2018 The 2nd International Conference on Compute and Data Analysis (ICCDA 2018)--Ei Compendex, Scopus, and ISI | |||||||||||
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Call For Papers | |||||||||||
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ICCDA 2018 The 2nd International Conference on Compute and Data Analysis
Northern Illinois University (NIU) DeKalb, USA, March 23-25, 2018. ICCDA 2018 is a not-to-be-missed opportunity that distills the most current knowledge on a rapidly advancing discipline in one conference. Join key researchers and established professionals in the field of Compute and Data Analysis as they assess the current state-of-the-art and roadmap crucial areas for future research. We aim to building an idea-trading platform for the purpose of encouraging researcher participating in this event. The papers to be presented at ICCDA 2018 address many grand challenges in modern engineering. The full papers to be presented will be peer-reviewed by expert reviewers including the whole organising committees members. *Proceedings: Conference Papers (Full Paper) will be published in the International Conference Proceedings Series by ACM, which will be archived in the ACM Digital Library, and indexed by Ei Compendex, Scopus, and submitted to be reviewed by Thomson Reuters Conference Proceedings Citation Index (ISI Web of Science). *Keynote speaker: Prof. Anu Gokhale,Illinois State University, USA; Prof. Shigang Chen, Department of Computer & Information of Science & Engineering University of Florida, USA; Prof. Sen Zhang, Department of Math, Computer Science and Statistics State University of New York College at Oneonta, NY, United States; Assoc. Prof. Donald S. Zinger,Northern Illinois University, USA. *Submission: By Email: iccda_info@163.com: By Easy Chair: https://easychair.org/conferences/?conf=iccda2018 *Contact: Ms. Maggie Lau Email: iccda_info@163.com Tel: +86 1301 822222 0 http://www.iccda.org/ *Call for Paper: General areas of interest to ICCDA include but are not limited to: Foundations Mathematical, probabilistic and statistical models and theories Machine learning theories, models and systems Knowledge discovery theories, models and systems Manifold and metric learning Deep learning Scalable analysis and learning Non-iidness learning Heterogeneous data/information integration Data pre-processing, sampling and reduction Dimensionality reduction Feature selection, transformation and construction Large scale optimization High performance computing for data analytics Architecture, management and process for data science Data analytics, machine learning and knowledge discovery Learning for streaming data Learning for structured and relational data Latent semantics and insight learning Mining multi-source and mixed-source information Mixed-type and structure data analytics Cross-media data analytics Big data visualization, modeling and analytics Multimedia/stream/text/visual analytics Relation, coupling, link and graph mining Personalization analytics and learning Web/online/social/network mining and learning Structure/group/community/network mining Cloud computing and service data analysis Storage, retrieval and search Data warehouses, cloud architectures Large-scale databases Information and knowledge retrieval, and semantic search Web/social/databases query and search Personalized search and recommendation Human-machine interaction and interfaces Crowdsourcing and collective intelligence |
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