Special Issue on: "Big Data and Data-Driven Science"
Dr. Sugam Sharma, Iowa State University, USA
Today, a paradigm shift is being observed in science, with the focus gradually shifting away from operation to data, which is greatly influencing decision making. This science is being driven by data and is termed as data science.
In this internet age, we are inundated with data from multiple sources in various forms, especially social media, and in the modern data science vocabulary, this large, complex, structured or unstructured and heterogeneous data is recognised as “big data”. The volume of this data has grown beyond the exabyte magnitude. The rapid pace of data growth through various disparate sources has seriously challenged the data management and analytic capabilities of traditional databases. Furthermore, the velocity of expansion of the amount of data gives rise to a complete paradigm shift in how new-age data is processed.
Confidence in the data engineering of existing data processing systems is gradually fading, whereas the capabilities of new techniques for capturing, storing, visualising and analysing data are evolving.
This special issue intends to address current data-driven research challenges, and seeks articles discussing big data and analytics from various perspectives such as design and development of new tools and techniques, comprehensive analytics, applications, intelligent decision making and so forth. Submitted research articles should present innovative findings that make substantial theoretical and empirical contributions to knowledge in data science.
Suitable topics include, but are not limited, to the following:
- Architecture, design and development of new tools and techniques for big data
- Big data analytics and associated issues and challenges
- Big data analytics for smart decision making in interdisciplinary domains
- Big data and business intelligence
- Big data and cloud-enabled analytics
- Big data and complex business applications
- Big data and next-generation innovations in business models
- Big data and rich and interactive visual and media analytics
- Big data and risk management
- Big data and smarter agriculture
- Big data and workflow management
- Big data economics
- Big data analytics for clinical care
- Medical (big) data management and mining
- Big data integration for healthcare
- Big data for enterprise, government and society
- Big data implications in enterprise models and practices
- Big data and industry standards
- Big data models and query languages
- Big data management for smart solutions
- Big data real-time analytics
- Big data and forensic science
- Big data security, privacy and trust policies
- Cloud-based big data analytics
- Big data and information quality
- Data lakes for analytics
- Evolution of big data and its knowledge implications
- Hadoop ecosystem in big data research
- Internet of Things (IOT) evolution for enterprise
- Smart data evolution for enterprise
- (Big) open data
- *-as-a-services cloud evolution and big data as-a-service
Notes for Prospective Authors
Submitted papers should not have been previously published nor be currently under consideration for publication elsewhere. (N.B. Conference papers may only be submitted if the paper has been completely re-written and if appropriate written permissions have been obtained from any copyright holders of the original paper).
All papers are refereed through a peer review process.
All papers must be submitted online. To submit a paper, please read our Submitting articles page.
Manuscripts due by: 30 November, 2016
Notification to authors: 15 February, 2017
Final versions due by: 15 March, 2017