A parametric framework for the analysis of transcriptome data is demonstrated to yield coincident results when applied to data acquired using two different microarray platforms. Microarrays are widely employed to acquire transcriptome information, and several platforms of chips are currently in use. However, discrepancies among studies are frequently reported, particularly among those performed...

City-wide urban infrastructures are increasingly reliant on networked technology to improve and expand their services. As a side effect of this digitalization, large amounts of data can be sensed and analyzed to uncover patterns of human behavior. In this paper, we sense and analyze data from a new type of urban infrastructure called shared bicycling. We provide a spatio-temporal analysis of si...

Due to the surge of spatio-temporal data volume, popularity location-based services and applications, importance extracted knowledge from solve a wide range real-world problems, plethora research development work has been done in area spatial analytics past decade. The main goal existing works was develop algorithms technologies capture, store, manage, analyze, visualize or data. researchers ha...

Due to severe societal and environmental impacts, wildfire prediction using multi-modal sensing data has become a highly sought-after data-analytical tool by various stakeholders (such as state governments power utility companies) achieve more informed understanding of activities plan preventive measures. A desirable algorithm should precisely predict fire risk magnitude for location in real ti...

Abstract More and more applications nowadays use spatio-temporal data for different purposes. In order to be processed used efficiently, this unique type of requires special handling. This paper summarizes methods approaches feature selection machine learning algorithms engineering. Furthermore, it highlights relevant work in specific domains. The range possible processing is quite wide. Howeve...

Multidimensional scaling is one of the important techniques for a big data management. In this paper, various statistical analyses are compared to find the best-fitting method for a representation of a higher dimensional data using the reduced or smaller dimensional data using various multivariate analyses with maximum likelihood estimation through the neurofuzzy systems, which estimate the pre...

We deene a formal model of spatio-temporal partitions which can be used to model temporally changing maps. We investigate new applications and generalizations of operations that are well-known for static spatial maps. We then deene a small set of operations on spatio-temporal partitions that are powerful enough to express all these tasks and more. Spatio-temporal partitions combine the general ...

Spatio-temporal clustering is a process of grouping objects based on their spatial and temporal similarity. It is relatively new subfield of data mining which gained high popularity especially in geographic information sciences due to the pervasiveness of all kinds of location-based or environmental devices that record position, time or/and environmental properties of an object or set of object...