Viswanath Nandigam

A three tier architecture for LiDAR interpolation and analysis

Emerging Grid technologies enable solving scientific problems that involve large datasets and complex analyses. Coordinating distributed Grid resources and computational processes requires adaptable interfaces and tools that provide a modularized and configurable environment for accessing Grid clusters and executing high performance computational tasks. In addition, it is beneficial to make these tools available to the community in a unified framework through a shared cyberinfrastructure, or a portal, so scientists can focus on their scientific work and not be concerned with the implementation of the underlying infrastructure. In this paper we describe a scientific workflow approach to coordinate various resources as data analysis pipelines. We present a three tier architecture for LiDAR interpolation and analysis, a high performance processing of point intensive datasets, utilizing a portal, a scientific workflow engine and Grid technologies. Our proposed solution is available through the GEON portal and, though focused on LiDAR processing, is applicable to other domains as well.

Leveraging XSEDE HPC resources to address computational challenges with high-resolution topography data

Leveraging service-oriented architectures and taking advantage of the high-performance compute resources provided by XSEDE, we have developed standards-based web services to address the challenges associated with processing large volumes of high resolution topography data. These web services make results from community software packages and other cyberinfrastructure-based applications available to the wider earth sciences community via the OpenTopography Facility and the CyberGIS Gateway.

A three tier architecture applied to LiDAR processing and monitoring

Emerging Grid technologies enable solving scientific problems that involve large datasets and complex analyses, which in the past were often considered difficult to solve. Coordinating distributed Grid resources and computational processes requires adaptable interfaces and tools that provide modularized and configurable environments for accessing Grid clusters and executing high performance computational tasks. Computationally intensive processes are also subject to a high risk of component failures and thus require close monitoring. In this paper we describe a scientific workflow approach to coordinate various resources via data analysis pipelines. We present a three tier architecture for LiDAR interpolation and analysis, a high performance processing of point intensive datasets, utilizing a portal, a scientific workflow engine and Grid technologies. Our proposed solution is available to the community in a unified framework through a shared cyberinfrastructure, the GEON portal, enabling scientists to focus on their scientific work and not be concerned with the implementation of the underlying infrastructure.

Advancing Analysis of High Resolution Topography Using Distributed HPC Resources in OpenTopography

The OpenTopography science gateway provides efficient online access to high resolution topographic data and processing tools for a broad spectrum of research communities. We have integrated XSEDE HPC resources into the OpenTopography processing workflow to meet the growing demand for more complex and resource intensive algorithms from the wider community.

Use of clinical care registries to facilitate research study recruitment.

129 Background: We developed IRB-approved secure research registries for our Breast Imaging and Breast Care Clinics at Moores UC San Diego Cancer Center, as part of the UC statewide Athena Breast Health Network. Our clinical care registries securely store patient-reported intake data, which are summarized and uploaded into the medical record for patient care. Following consent, data are securely maintained in a separate research registry. In addition to facilitating quality assurance data collection, these registries aim to 1) maintain a data-rich research registry, 2) offer patients research opportunities, and 3) facilitate participant screening and recruitment into research studies. METHODS Breast Imaging or Breast Care clinic patients complete an online clinical intake form prior to their appointment, either at home or using an iPad in clinic, and are given the opportunity to be involved in research. Patients are asked for site-specific consent to keep personally identifiable intake data in a research registry, for consent to be approached about providing a biospecimen sample, and for consent to be contacted for future research opportunities. Data from consented participants are pulled into secure databases available to study personnel. RESULTS Participants to date include 4,480 patients, of whom 3,246 consented to use of data for research (72%), and 2,627 have agreed to be contacted for future research opportunities (59%). In a pilot biospecimen collection protocol, 46% of patients agreed to be approached and we have collected over 360 blood or saliva and 51 tissue samples. Additionally, with the use of future contact consent, we have facilitated recruitment of more than 370 participants to multiple lifestyle and survey-based clinical studies. CONCLUSIONS Patients are willing to participate in research, especially in a research registry that requires little additional time on their part. The use of research registries allows collection of a variety of data elements useful for prescreening participants for research studies, including body mass index, age, menopausal status and breast cancer diagnosis. Data- and participant-rich research registries facilitate efficient screening and recruitment for other research studies.