Biomedical Case Studies in Data Intensive Computing

Authors:
Geoffrey Fox;Xiaohong Qiu;Scott Beason;Jong Choi;Jaliya Ekanayake;Thilina Gunarathne;Mina Rho;Haixu Tang;Neil Devadasan;Gilbert Liu
Affiliations:
Pervasive Technology Institute, and School of Informatics and Computing,;Pervasive Technology Institute,;Pervasive Technology Institute,;Pervasive Technology Institute, and School of Informatics and Computing,;Pervasive Technology Institute, and School of Informatics and Computing,;Pervasive Technology Institute, and School of Informatics and Computing,;School of Informatics and Computing,;School of Informatics and Computing,;The Polis Center,;School of Medicine Indiana University,
Venue:
CloudCom '09 Proceedings of the 1st International Conference on Cloud Computing
Year:
2009

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Abstract

Many areas of science are seeing a data deluge coming from new instruments, myriads of sensors and exponential growth in electronic records. We take two examples --- one the analysis of gene sequence data (35339 Alu sequences) and other a study of medical information (over 100,000 patient records) in Indianapolis and their relationship to Geographic and Information System and Census data available for 635 Census Blocks in Indianapolis. We look at initial processing (such as Smith Waterman dissimilarities), clustering (using robust deterministic annealing) and Multi Dimensional Scaling to map high dimension data to 3D for convenient visualization. We show how scaling pipelines can be produced that can be implemented using either cloud technologies or MPI which are compared. This study illustrates challenges in integrating data exploration tools with a variety of different architectural requirements and natural programming models. We present preliminary results for end to end study of two complete applications.