FEI Open Inventor Team is collaborating with Prairie View A&M University to bring interactive 3D visualization to cloud-based big data applications.17 Sep 2016
The Cloud Computing Research Lab at Prairie View A&M University in Texas USA (PVAMU), led by Dr. Lei Huang, is working on a scalable big data analytics cloud project sponsored by NSF and DoD. Called Seismic Analytics Cloud (SAC), it provides an innovative framework for applying Deep Learning techniques to seismic data for the oil & gas industry. One of the many aspects of the project includes the integration of visualization capabilities to manage, analyze and interact with very large 2D and 3D distant data. This is where the FEI Open Inventor Team comes into play…
The FEI Open Inventor Team is working on a cloud initiative to bring interactive 3D visualization to cloud-based big data applications. It includes our remote rendering technology for delivering 3D rendering to remote devices, our online demo portal for validating 3D rendering as a web service, and a proof-of-concept project for rendering big data distributed across multiple cloud nodes (e.g. HDFS). PVAMU’s Seismic Analytics Cloud project provides the Open Inventor cloud initiative with a real-world big data cloud application to test and refine these technologies.
The Open Inventor engineers are now working closely with PVAMU to integrate seismic analytics cloud (SAC) with the Open Inventor cloud initiative, and deliver a demo at the year’s most anticipated oil and gas event: the SEG Annual Meeting (Dallas, Texas – October 16-21). If you are attending SEG this year, please come by booth #644 (HPC Pavilion) to see the demo and to attend PVAMU’s presentation.
If you are moving your application to the cloud, and are interested in knowing more about the Open Inventor cloud initiative, contact us at email@example.com.
Yan, Y.Z., Hanifi, M., Yi, L.Q. and Huang, L. (2015) Building a Productive Domain-Specific Cloud for Big Data Processing and Analytics Service. Journal of Computer and Communications, 3, 107-117. http://dx.doi.org/10.4236/jcc.2015.35014