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A cloud computing system in windows azure platform for data analysis of crystalline materials
Author(s) -
Xing Qi,
BlaistenBarojas Estela
Publication year - 2013
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.2912
Subject(s) - computer science , workflow , python (programming language) , cloud computing , analytics , database , executable , visualization , random forest , data mining , operating system , software engineering , world wide web , machine learning
SUMMARY Cloud computing is attracting the attention of the scientific community. In this paper, we develop a new cloud‐based computing system in the Windows Azure platform that allows users to use the Zeolite Structure Predictor (ZSP) model through a Web browser. The ZSP is a novel machine learning approach for classifying zeolite crystals according to their framework type. The ZSP can categorize entries from the Inorganic Crystal Structure Database into 41 framework types. The novel automated system permits a user to calculate the vector of descriptors used by ZSP and to apply the model using the Random Forest™ algorithm for classifying the input zeolite entries. The workflow presented here integrates executables in Fortran and Python for number crunching with packages such as Weka for data analytics and Jmol for Web‐based atomistic visualization in an interactive compute system accessed through the Web. The compute system is robust and easy to use. Communities of scientists, engineers, and students knowledgeable in Windows‐based computing should find this new workflow attractive and easy to be implemented in scientific scenarios in which the developer needs to combine heterogeneous components. Copyright © 2012 John Wiley & Sons, Ltd.