Int. J. of Spatial, Temporal and Multimedia Information Systems

Special Issue on: “Big (Spatio-Temporal) Data-Driven Science”

Guest Editor: Dr. Sugam Sharma, Iowa State University, USA

Today, science is passing through an era of transformation, where the inundation of data, dubbed ‘data deluge’, is influencing decision making. The science is driven by the data and in this internet age, advances in remote sensors, sensor networks, common business practices and the proliferation of location sensing devices in daily activities have exploded the generation of disparate, dynamic and geographically
distributed data in recent years. The volume of the data has grown beyond the exabyte magnitude, and this large, complex, structured or unstructured, and heterogeneous data in the form of big (spatio-temporal) data has gained significant attention. The rapid pace of data growth through various disparate sources has seriously challenged the data analytic capabilities of traditional databases. The velocity of the expansion of the amount of data gives rise to a complete paradigm shift in how new age spatial, temporal and spatio-temporal data is processed.
Confidence in the data engineering of the existing data processing systems is gradually fading whereas the capabilities of new scalable data management and analytical frameworks for capturing, storing, visualising, and analyzing such mammoth data are evolving.

This special issue seeks high quality original research articles from data-driven researchers and practitioners that have the potential to advance the big (spatio-temporal) data science, both theory and practice.

Subject Coverage:

Suitable topics include, but are not limited, to the following:
 Big (spatio-temporal) data algorithms, applications, and challenges
 Empowering geographic information systems (GIS) with big data
 Big data in remote sensing and photogrammetry and computing
 Big (spatio-temporal) data engineering on Hadoop ecosystem
 Big (spatio-temporal) data processing on MapReduce platform
 Customisation of Hadoop ecosystem for big (spatio-temporal) data management
 NoSQL solutions for big (spatio-temporal) data
 Geo-spatial intelligence for big data
 Big (spatio-temporal) data analytics issues and challenges
 Scalable geospatial analytics for satellite and aerial imagery
 Scalable geospatial analytics for smart and precision agriculture
 Rich and interactive visual and media analytics for big (spatio-temporal) data
 Knowledge development, discovery and decision making from big (spatio-temporal) data
 Query advancements for big (spatio-temporal) data
 Cloud computing support for big (spatio-temporal) data
 As-a-service cloud evolution for big (spatio-temporal) data
 Scalable big (spatio-temporal) data management and modelling
 Geomatics, spatial analysis and decision aids for big data
 Big data aspect for moving objects
 Internet of things (IoT) evolution and big (spatio-temporal) data
 Interesting applications of big (spatio-temporal) data

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 rewritten 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.

If you have any queries concerning this special issue, please email the Guest Editor at sugamsha@iastate.edu

Important Dates
Submission Deadline: 30, December, 2016
Notification of First Review: 30 January 30, 2017
Submission of Revised Manuscript: 28 February, 2017
Notification of Final Acceptance: 15 March, 2017
Final Manuscript Due: 15 April, 2017