posted by user: witarek || 1113 views || tracked by 1 users: [display]

geoBD 2016 : Special Session on Geospatial Big Data - In conjunction with the International Conference on Big Data and Advanced Wireless Technologies (BDAW'2016)

FacebookTwitterLinkedInGoogle

Link: http://bdaw.info/index.php/special-sessions
 
When Nov 10, 2016 - Nov 11, 2016
Where Blagoevgrad, Bulgaria
Submission Deadline TBD
 

Call For Papers

Special Session on Geospatial Big Data (geoBD)
In conjunction with the International Conference on Big Data and Advanced Wireless Technologies (BDAW'2016)
http://bdaw.info/index.php/special-sessions
American University in Bulgaria. Blagoevgrad, Bulgaria 10-11 November 2016
-----------------------------------------------------------------------------

Recent advances on ubiquitous computing technologies are enabling large amounts of geospatial (also spatial or geographic) data to be acquired and processed for decision-making and strategizing purposes in a wide range of applications. Geospatial Big Data was lately adopted to refer to these data, which are characterized by their volume, variety, velocity, value, and veracity.

The last few years have witnessed an increasing research and development efforts addressing this new form of data. So far, these efforts have resulted in a substantial progress in understanding the characteristics of these data, developing methods and tools for their acquisition, storage, processing, and dissemination, as well as understanding their social impact. Despite this progress, Geospatial Big Data still face many challenges, particularly related to data quality, representation, consistency (as data comes from different sources), and security. Assessing the Geospatial Big Data authenticity, validity, and uncertainty, determining the appropriate sources of data, overcoming heterogeneity barriers, and ultimately understanding what motivates individuals and social networks to contribute to Geospatial Big Data efforts are key issues that need to be addressed. To this end, a special focus needs to be given to the envisioned use of data as well as the emergent technologies (e.g., Wireless Sensor Networks, FRID, Internet of Things, etc.) being used for their acquisition.

This special track (geoBD) aims to bring leading researchers and practitioners from a variety of fields and operating on data collection, processing, storage, and visualization to present and promote their latest research and development works and discuss current trends, applications, and challenges related to Geospatial Big Data. We particularly solicit original research contributions, position papers, and surveys addressing the themes of the track below.

Topics covered by the special session: The special track will include, but not limited to, the following topics:

- Geospatial Big Data acquisition and dissemination methods
- Geospatial Data quality issues
- Geospatial Big Data life-cycle and interoperability
- Geospatial Big Data and social networks
- Geospatial Big Data representation and storage
- Geospatial Big Data and Crowdsourcing
- Geospatial Big Data analytics
- Geospatial Big Data and Business Intelligence
- Geospatial Big Data and pattern discovery
- Geospatial Big Data and related applications
- Geospatial Big Data for Smart Cities
- Geospatial Big Data and Internet of Things
- Geospatial Big Data for Cyber-Physical Systems
- Hazard management and Geospatial Big Data
- Impact of Geospatial Big Data on designing Geographic Information Systems (GIS)
- Intelligent Geospatial Big Data
- Legal issues in Geospatial Big Data
- Open data, Structured data, and Unstructured data
- Privacy issues in Geospatial Big Data
- Security issues in Geospatial Big Data
- Semantic heterogeneity issues in Geospatial Big Data
- Sensor Geospatial Big Data
- Trust issues in Geospatial Big Data
- Uncertainty in Geospatial Big Data
- Volunteered Geospatial Big Data


Important Dates
Submission Deadline: August 20, 2016
Author Notification: September 2, 2016
Camera Ready: September 15, 2016

Submission Instructions
Authors are requested to submit papers reporting original research results to tarek.sboui.1@ulaval.ca and nafaa.jabeur@gutech.edu.om
Please follow the ACM link (http://www.acm.org/publications/proceedings-template) where you will find two types of templates, Word and Latex, please choose one to write your paper. Authors are, however, requested to submit their papers electronically in PDF format.
For short papers, the number of pages is 4. For full papers, the number of pages must be 5 to 12 pages.
The authors of outstanding papers will be invited to extend their papers to selected special issues of International Journals.

Publication
All accepted Papers will be published by ACM - International Conference Proceedings Series (ICPS) and will be available in ACM Digital Library. The ISBN number assigned By ACM ICPS to BDAW conference is 978-1-4503-4779-2. ACM sends all published materials to DBLP, Scopus and Thomson Reuters for indexing in their products.

Related Resources

ISBDAI 2020   【Ei Compendex Scopus】2018 International Symposium on Big Data and Artificial Intelligence
IEEE COINS 2020   Internet of Things IoT | Artificial Intelligence | Machine Learning | Big Data | Blockchain | Edge & Cloud Computing | Security | Embedded Systems | Circuit and Systems | WSN | 5G
Data Science 2020   3nd Annual International Great Lakes Data Science Symposium
DSIS 2020   2nd ISSAT International Conference on Data Science and Intelligent Systems
ICBDB 2020   2020 2nd International Conference on Big Data and Blockchain(ICBDB 2020) 
IEEE AIML4COINS 2020   IEEE AIML4COINS2020 | Artificial Intelligence | Machine Learning | Deep Learning | Machine Vision | Big Data Analytics | Video Analytics | Speech Recognition | NLP
CBDCom 2020   The 6th IEEE International Conference on Cloud and Big Data Computing
MESS 2020   Metaheuristics Summer School 2020 :: Learning & Optimization from Big Data
LOPAL 2020   Second International Conference on Learning and Optimization Algorithms: Theory and Applications
SC-ML in IoT, BD and CPS 2020   Special Session on Soft Computing and Machine Learning in IoT, Big Data and Cyber Physical Systems