Physical Sciences and Mathematics Commons^™

Cloud Based Scientific Workflow For Nmr Data Analysis, Ashwin Manjunatha, Paul E. Anderson, Satya S. Sahoo, Ajith Harshana Ranabahu, Michael L. Raymer, Amit P. Sheth

Kno.e.sis Publications

This work presents a service oriented scientific workflow approach to NMR-based metabolomics data analysis. We demonstrate the effectiveness of this approach by implementing several common spectral processing techniques in the cloud using a parallel map-reduce framework, Hadoop.

A Study In Hadoop Streaming With Matlab For Nmr Data Processing, Kalpa Gunaratna, Paul E. Anderson, Ajith Harshana Ranabahu, Amit P. Sheth

Kno.e.sis Publications

Applying Cloud computing techniques for analyzing large data sets has shown promise in many data-driven scientific applications. Our approach presented here is to use Cloud computing for Nuclear Magnetic Resonance (NMR)data analysis which normally consists of large amounts of data. Biologists often use third party or commercial software for ease of use. Enabling the capability to use this kind of software in a Cloud will be highly advantageous in many ways. Scripting languages especially designed for clouds may not have the flexibility biologists need for their purposes. Although this is true, they are familiar with special software packages that allow …

Getting Code Near The Data: A Study Of Generating Customized Data Intensive Scientific Workflows With Domain Specific Language, Ashwin Manjunatha, Ajith Harshana Ranabahu, Paul E. Anderson, Amit P. Sheth

Kno.e.sis Publications

The amount of data produced in modern biological experiments such as Nuclear Magnetic Resonance (NMR) analysis far exceeds the processing capability of a single machine. The present state-of-the-art is taking the ”data to code”, the philosophy followed by many of the current service oriented workflow systems. However this is not feasible in some cases such as NMR data analysis, primarily due to the large scale of data.

The objective of this research is to bring ”code to data”, preferred in the cases when the data is extremely large. We present a DSL based approach to develop customized data intensive scientific …