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

{{Event 
|Acronym=SI: Spatiotemporal Big Data 2017 
|Title=SI: Spatiotemporal Big Data  2017 : Special Issue on Spatiotemporal Big Data Challenges, Approaches, and Solutions 
|Type=Conference 
|Field=big data
|Start date=Invalid date
|End date=2017/08/21T09:54:04 
|City=N/A 
|Country=undefined
|Homepage=http://www.journals.elsevier.com/future-generation-computer-systems/call-for-papers/special-issue-on-spatiotemporal-big-data-challenges-approach
|Submission deadline=2017/08/30T12:00:00
}}
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