posted by organizer: dzissis || 3600 views || tracked by 5 users: [display]

SI: Spatiotemporal Big Data 2017 : Special Issue on Spatiotemporal Big Data Challenges, Approaches, and Solutions

FacebookTwitterLinkedInGoogle

Link: http://www.journals.elsevier.com/future-generation-computer-systems/call-for-papers/special-issue-on-spatiotemporal-big-data-challenges-approach
 
When N/A
Where N/A
Submission Deadline Aug 30, 2017
Notification Due Oct 1, 2017
Final Version Due Jan 30, 2018
Categories    big data   spatiotemporal   data analytics   geospatial
 

Call For Papers

Today, the growing number of distributed sensors and tracking systems are generating overwhelming amounts of high velocity spatio-temporal data. Executing high performance queries on enormous volumes of spatial data, has become a necessity for numerous domains ranging from atmospheric, climate and ocean simulations to signal processing, traffic, and behaviour modelling. As the dimensions and volume of the data grows to massive scales, processing and storage with conventional methods is challenged. Most interestingly though, even most state of the art “Big Data” processing tools fall short in supporting spatiotemporal data needs efficiently, as they lack support for even basic spatial properties and methods (such as spatial indexing and joins). Combining these challenges with real time requirements (such as sub-second query response times required for collision avoidance and anomaly detection) only exacerbates the problem.

To support such applications, the research community has long been exploring methods of data reduction, compression, time-window approaches, parallel processing, distributed storing and many more, while often accepting accuracy and performance trade-offs. This special issue aims to highlight problems originating from real world application fields dealing with spatiotemporal Big Data challenges and invite researchers working towards novel methods for addressing these issues to submit their work. The aim of this special issue publication is to cover novel data science theory and algorithms, data engineering and real world systems architectures, which are aimed at the storage, fusion, processing, learning and ultimately knowledge extraction from real world spatio-temporal datasets.

This SI publication is aimed at researchers, scientists and practitioners with interests that lie at the intersection of data science and large-scale data management problems.

The issue will focus on technologies and solutions related (but not limited) to:

-Spatiotemporal compression and clustering techniques effective for big data processing
-Spatial data mining algorithms and solutions
-Large-scale parallel and distributed implementations for geospatial datasets
-Real-time processing and learning based on spatio-temporal features
-Knowledge discovery implementations from spatiotemporal real world datasets;
-Visual and data analytics, knowledge representation of big geospatial data
-Cloud enabled Big data architectures and real world applications;

CFP available at http://www.journals.elsevier.com/future-generation-computer-systems/call-for-papers/special-issue-on-spatiotemporal-big-data-challenges-approach

Related Resources

SoCAV 2023   2023 International Symposium on Connected and Autonomous Vehicles (SoCAV 2023)
MLDS 2022   3rd International Conference on Machine Learning Techniques and Data Science
MLDM 2023   18th International Conference on Machine Learning and Data Mining
ICBDB 2022   2022 4th International Conference on Big Data and Blockchain(ICBDB 2022)
IEEE SSCI 2023   2023 IEEE Symposium Series on Computational Intelligence
ICACII 2023   2nd International Conference on Advances in Computational Intelligence and Informatics
AIAA 2022   12th International Conference on Artificial Intelligence, Soft Computing and Applications
SI PMABD 2023   Special Issue on Programming Models and Algorithms for Big Data
CBW 2023   4th International Conference on Cloud, Big Data and Web Services
AI-DH 2022   MDPI Big Data and Cognitive Computing - Special Issue on Artificial Intelligence in Digital Humanities