[dbjapan] SmartData-2018: Call for Papers

["Sumiya, Kazutoshi" <sumiya [at] kwansei.ac.jp>]

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