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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Talia JOfCloudComp2019 8.png|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig2 Trellet JOfIntegBioinfo2018 15-2.jpg|240px]]</div>
'''"[[Journal:A view of programming scalable data analysis: From clouds to exascale|A view of programming scalable data analysis: From clouds to exascale]]"'''
'''"[[Journal:Semantics for an integrative and immersive pipeline combining visualization and analysis of molecular data|Semantics for an integrative and immersive pipeline combining visualization and analysis of molecular data]]"'''


Scalability is a key feature for big data analysis and machine learning frameworks and for applications that need to analyze very large and real-time data available from data repositories, social media, sensor networks, smartphones, and the internet. Scalable big data analysis today can be achieved by parallel implementations that are able to exploit the computing and storage facilities of high-performance computing (HPC) systems and [[cloud computing]] systems, whereas in the near future exascale systems will be used to implement extreme-scale [[data analysis]]. Here is discussed how cloud computing currently supports the development of scalable data mining solutions and what the main challenges to be addressed and solved for implementing innovative data analysis applications on exascale systems currently are. ('''[[Journal:A view of programming scalable data analysis: From clouds to exascale|Full article...]]''')<br />
The advances made in recent years in the field of structural biology significantly increased the throughput and complexity of data that scientists have to deal with. Combining and [[Data analysis|analyzing]] such heterogeneous amounts of data became a crucial time consumer in the daily tasks of scientists. However, only few efforts have been made to offer scientists an alternative to the standard compartmentalized tools they use to explore their data and that involve a regular back and forth between them. We propose here an integrated pipeline especially designed for immersive environments, promoting direct interactions on semantically linked 2D and 3D heterogeneous data, displayed in a common working space. The creation of a semantic definition describing the content and the context of a molecular scene leads to the creation of an intelligent system where data are (1) combined through pre-existing or inferred links present in our hierarchical definition of the concepts, (2) enriched with suitable and adaptive analyses proposed to the user with respect to the current task and (3) interactively presented in a unique working environment to be explored. ('''[[Journal:Semantics for an integrative and immersive pipeline combining visualization and analysis of molecular data|Full article...]]''')<br />
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Revision as of 15:31, 15 April 2019

Fig2 Trellet JOfIntegBioinfo2018 15-2.jpg

"Semantics for an integrative and immersive pipeline combining visualization and analysis of molecular data"

The advances made in recent years in the field of structural biology significantly increased the throughput and complexity of data that scientists have to deal with. Combining and analyzing such heterogeneous amounts of data became a crucial time consumer in the daily tasks of scientists. However, only few efforts have been made to offer scientists an alternative to the standard compartmentalized tools they use to explore their data and that involve a regular back and forth between them. We propose here an integrated pipeline especially designed for immersive environments, promoting direct interactions on semantically linked 2D and 3D heterogeneous data, displayed in a common working space. The creation of a semantic definition describing the content and the context of a molecular scene leads to the creation of an intelligent system where data are (1) combined through pre-existing or inferred links present in our hierarchical definition of the concepts, (2) enriched with suitable and adaptive analyses proposed to the user with respect to the current task and (3) interactively presented in a unique working environment to be explored. (Full article...)

Recently featured:

A view of programming scalable data analysis: From clouds to exascale
Transferring exome sequencing data from clinical laboratories to healthcare providers: Lessons learned at a pediatric hospital
Research on information retrieval model based on ontology