Smart Data 2018 - Smart Data: State-of-the-Art and Perspectives in Computing and Applications

Smart Data: State-of-the-Art and Perspectives in Computing and Applications
(Chapman & Hall/ CRC Big Data Series)
CRC Press, Taylor & Francis Group, USA
Important Dates
* Proposal Submission: February 1, 2018*
* Proposal (Acceptance/Rejection): February 15, 2018
* Sample Chapter (Acceptance/Rejection): April 15, 2018
* Complete Chapter Submission (to editors): June 15, 2018
* Submission of Chapters (to publisher): July 1, 2018
* Publication Time: Q4/2018 (estimated)
Big Data is being generated around us at 24/7 basis, from daily business, custom use, engineering, science activities, sensory data collected from IoTs and CPS systems, among others. Storing and owing only such massive amount of data is meaningless, as the key point is to identify, locate and extract valuable knowledge from Big Data to forecast and services support, improving quality of service and society’s value. Such extracted valuable knowledge is usually referred to Smart Data that is vital in providing suitable decision in highly on-demand business, science and engineering applications.
How to select Smart Data from Big Data, unlocking value in massive datasets? Advanced Big Data modeling and analytics are indispensable for discovering the underlying structure from retrieved data to acquire Smart Data, whereas novel computing theories as well advanced mining and learning techniques are fundamentally important to the search of such intelligent decision and predicative services support.
In this book, it is intended to invite scholars, experts and successful case participating members to contribute discussions on topics for smart data mining and management as well as applications. Not only smart data computing algorithms and architectures from the computer point of view, but also smart data applications in business issues aspects, industrial aspects and related areas, it is equally well suitable for data analysts in business and industry.
* Topics
Topics 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
Track 2: Smart Data Infrastructure and Systems
- Programming Models/Environments for Cluster/Cloud/Edge/BigData Computing
- High Performance/Throughtput Platforms for Smart/Big Data Computing
- Cloud Computing, Edge Computing and Fog Computing for Smart/Big Data
- System Architecture and Infrastructure of Smart/Big Data
- New Programming Models for Smart/Big Data beyond Hadoop/MapReduce
- Smart Data Appliance
- Smart Data Ecosystems
Track 3: Big Data Storage and Management
- Smart Data Collection, Transformation and Transmission
- Big Data Integration and Cleaning for Smart Data
- Uncertainty and Incompleteness Handling in Smart/Big Data
- Quality Management of Smart/Big Data
- Smart Data Storage Models
- Query and Indexing Technologies
- Distributed File Systems
- Distributed Database Systems
- Large-Scale Graph/Document Databases
Track 4: Smart 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 Smart/Big Data
- Brain-Inspired/Nature-Inspired Computing for Smart/Big Data
- Distributed Representation Learning of Smart Data
- Machine Learning/Deep Learning for Smart/Big Data
- Applications of Conventional Theories (e.g., Fuzzy Set, Rough Set) in Smart/Big Data
- New Models, Algorithms, and Methods for Smart/Big Data Processing and Analytics
- Exploratory Data Analysis
- Visualization Analytics for Big Data
- Smart/Big Data Aided Decision-Marking
Track 5: Smart/Big Data Applications
- Smart/Big Data Applications in Science, Internet, Finance, Telecommunications, Business, Medicine, Healthcare, Government, Transportation, Industry, Manufacture
- Smart/Big Data Applications in Government and Public Sectors
- Smart/Big Data Applications in Enterprises
- Security, Privacy and Trust in Smart/Big Data
- Smart/Big Data Opening and Sharing
- Smart/Big Data Exchange and Trading
- Data as a Service (DaaS)
- Standards for Smart/Big Data
- Case Studies of Smart/Big Data Applications
- Practices and Experiences of Smart/Big Data Project Deployments
- Ethic Issues on Smart/Big Data Applications
* Proposal submission
A proposal for book chapter is needed from prospective authors before the proposal *submission due date*, describing the objective, scope and structure of the proposed chapter (no more than 5 pages). Acceptance of chapter proposals will be communicated to lead chapter authors after a formal double-blind review process, to ensure relevance, quality and originality. The submission of chapter proposals should be sent directly via email to editors.
* Book Editors
Kuan-Ching Li, Providence University, Taiwan, kuancli-AT-gm.pu.edu.tw
Qingchen Zhang, St. Francis Xavier University, Canada, qzhang-AT-stfx.ca
Laurence T. Yang, St. Francis Xavier University, Canada, ltyang-AT-gmail.com
Beniamino Di Martino, Universita' della Campania "Luigi Vanvitelli", Italy, beniamino.dimartino-AT-unicampania.it
* Additional Information
Inquiries and chapter proposal submissions can be forwarded electronically by email, to:
Qingchen Zhang (email: qzhang-AT-stfx.ca), cc'ied to kuancli-AT-gm.pu.edu.tw, ltyang-AT-gmail.com and beniamino.dimartino-AT-unicampania.it