日本データベース学会の皆様： 関西学院大学の角谷です． 7月末にカナダのハリファックスで開催されるSmartData-2018のCFPを お送り致します． IEEE International Conference on Smart Data (SmartData-2018) Halifax, Canada July 30 - August 03, 2018 http://cse.stfx.ca/~SmartData2018/ Paper Submission Deadline: March 01, 2018 Workshop Proposal Due: February 10, 2018 投稿をご検討ください．よろしくお願い致します． -- 角谷 和俊 (Kazutoshi SUMIYA) 関西学院大学 総合政策学部 メディア情報学科 社会情報デザイン研究室 http://www.kgsocinfo.org/sumiya/index.html Smart Data aims to filter out the noise and produce the valuable data, which can be effectively used by enterprises and governments for planning, operation, monitoring, control, and intelligent decision making. Although unprecedentedly large amount of sensory data can be collected with the advancement of the Cyber Physical Social (CPS) systems recently, the key is to explore how Big Data can become Smart Data and offer intelligence. Advanced Big Data modeling and analytics are indispensable for discovering the underlying structure from retrieved data in order to acquire Smart Data. The goal of this conference is to promote community-wide discussion identifying the Computational Intelligence technologies and theories for harvesting Smart Data from Big Data. The goal of the 2018 IEEE International Conference on Smart Data (SmartData-2018) is to provide a forum for scientists, engineers and researchers to discuss and exchange novel ideas, results, experiences and work-in-process on all aspects of Smart Data. *Accepted papers will be included in the conference proceedings published by the IEEE Computer Society Press (indexed by EI) and IEEE Xplore.* At least one of the authors of any accepted paper must register and present the paper. Best Paper Awards will be presented at the conference. *High quality papers will be nominated to publish at various SCI-indexed journal special issues (e.g., Information Sciences, Future Generation Computer Systems, IEEE Access, Security and Communication Networks).* Topics of interest include, but are not limited to the following: Track 1: Data Science and Its Foundations Foundational Theories for Data Science Theoretical Models for Big Data Foundational Algorithms and Methods for Big Data Interdisciplinary Theories and Models for Smart Data Data Classification and Taxonomy Data Metrics and Metrology Statistical inference for Big Data/Smart Data Statistical inference for Massive, Complex Data Track 2: Big Data Infrastructure and Systems Cloud/Cluster Computing for Big Data Programming Models/Environments for Cluster/Cloud Computin High Performance/Throughtput Platforms for Big Data Computing Parallel Computing for Big Data Open Source Big Data Systems (e.g., including Hadoop, Spark, Flink and Storm) System Architecture and Infrastructure of Big Data New Programming Models for Big Data beyond Hadoop/MapReduce Big Data Appliance Big Data Ecosystems Track 3: Big Data Storage and Management Big Data Collection, Transformation and Transmission Big Data Integration and Cleaning Uncertainty and Incompleteness Handling in Big Data/Smart Data Quality Management of Big Data/Smart Data Big Data Storage Models Query and Indexing Technologies Distributed File Systems Distributed Database Systems Large-Scale Graph/Document Databases NewSQL/NoSQL for Big Data Track 4: Big Data Processing and Analytics Smart Data Search, Mining and Drilling from Big Data Semantic Integration and Fusion of Multi-Source Heterogeneous Big Data In-Memory/Streaming/Graph-Based Computing for Big Data/Smart Data Brain-Inspired/Nature-Inspired Computing for Big Data/Smart Data Distributred Representation Learning of Smart Data Machine Learning/Deep Learning for Big Data/Smart Data Applications of Conventional Theories (e.g., Fuzzy Set, Rough Set, and Soft Set) in Big Data New Models, Algorithms, and Methods for Big/Smart Data Processing and Analytics Exploratory Data Analysis Visualization Analytics for Big Data Big Data Based Prediction Methods Big Data Aided Decision-Marking Applications of Statistical Theories (e.g., Probability Models) in Big Data Statistical Approaches for Processing or Analyzing Big Data/Smart Data Statistical Modelling for Big Data/Smart Data Theories or Models Associated with the Analysis of Massive, Complex Data Track 5: Big/Smart Data Applications Big/Smart Data Applications in Science, Internet, Finance, Telecommunications, Business, Medicine, Healthcare, Transportation, Industry, Manufacture Big/Smart Data Applications in Government and Public Sectors Big/Smart Data Applications in Enterprises Security, Privacy and Trust in Big Data Big Data Opening and Sharing Big Data Exchange and Trading Data as a Service (DaaS) Standards for Big/Smart Data Case Studies of Big/Smart Data Applications Practices and Experiences of Big Data Project Deployments Ethic Issues about Big Data Applications Big/Smart Data Applications in Agriculture, Engineering Important Dates Regular/Workshop/Special Session/Poster/Demo papers: Submission Due: March 01, 2018 Acceptance Notification Due: April 15, 2018 All Paper Registration Due: May 15, 2018 Camera-ready Manuscript Due: May 15, 2018 General Chairs - Jason Gu, Dalhousie University, Canada - Carson K. Leung, University of Manitoba, Canada - Ladjel Bellatreche, ISAE - ENSMA, France Program Chairs - Jinjun Chen, Swinburne University of Technology, Australia - Ching-Hsien Robert Hsu, Chung Hua University, Taiwan - Reda Alhajj, University of Calgary, Canada Workshop Chair - Mukesh Mohania, IBM Australia, Australia Publicity Chairs - Wookey Lee, Inha University, South Korea - Junqiang Liu, Zhejiang Gongshang University, China - Elio Masciari, ICAR-CNR, Italy - Kazutoshi Sumiya, Kwansei Gakuin University, Japan Publication Chair - Min-Yuh Day, Tamkang University, Taiwan