SmartData 2018 - 2018 IEEE International Conference on Smart Data (SmartData-2018)

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.
Topics
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.
Topics of interest include, but are not limited to:
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
Track 2: Big Data Infrastructure and Systems
Cloud/Cluster Computing for Big Data
  • Programming Models/Environments for Cluster/Cloud Computing
  • 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
Track 5: Big/Smart Data Applications
  • Big/Smart Data Applications in Science, Internet, Finance, Telecommunictions, Business, Medicine, Healthcare, Government, 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