Tuesday, February 12, 2013

[G] Research Projects on Google App Engine

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Google Research Blog: Research Projects on Google App Engine

By Andrea Held, Program Manager, Google University Relations



Cross-posted on the Google Developers Blog



Last spring Google University Relations announced an open call for proposals for Google App Engine Research Awards. We invited academic researchers to use Google App Engine for research experiments and analysis, encouraging them to take advantage of the platform’s ability to manage heavy data loads and run large-scale applications. Submissions included exciting proposals in various subject areas from mathematics, computer vision, bioinformatics, climate and computer science. We have selected seven projects that have the potential to impact people’s lives by making community seismic networks affordable, creating individualized DNA profiles, collecting useful local data through social media, and by understanding global climate trends, just to mention a few.



We have donated $60,000 in Google App Engine credits to each of these projects recognizing the innovation and vision of the Principal Investigator and his collaborators. Congratulations to all of them!



Below is a brief introduction of the award recipients and their research. We look forward to learning about their progress and will share the news right here. Stay tuned!



K. Mani Chandy, Simon Ramo Professor and Professor of Computer Science, California Institute of Technology

Project title: Cloud-based Event Detection for Sense and Response

Description and research goals: We developed an App Engine-based sense and response platform for the Community Seismic Network (CSN) project. CSN's goals include measuring seismic events with finer spatial resolution than previously possible and developing a low-cost alternative to traditional seismic networks, which have high capital costs for acquisition, deployment, and ongoing maintenance. We are working on generalizing our implementation and experience to provide a system for other members of the community to use in future sense and response applications.



Lawrence Chung, Associate Professor, The University of Texas at Dallas

Project title: Google App Engine: Software Benchmark and Simulation Forecaster

Description and research goals: An important consideration before migrating a company’s application software to Google App Engine is performance and operating cost.

Similarly, the Google App Engine organization would want to estimate Google App Engine’s resource usage and how well the particular resource allocation will meet the performance and cost requirements, as in the service level agreements (SLAs). This research project aims to develop a Google App Engine simulation forecaster - a tool for estimating the performance and cost of software operating on Google App Engine, and produce some important operational benchmark.



Julian Gough, Professor, University of Bristol, UK

Project title: Personalised DNA Analysis

Description and research goals: Personal genomics is still in its infancy and although it is easy, and relatively cheap to obtain personal genotype data, the available analysis is not personalised; it is the same for everybody. In this project we will set up a service powered by App Engine that provides personal DNA analysis specific to each individual. The proposed service does not focus on disease, but on identifying aspects of a healthy person that make them unique. What does your genome tell you about yourself that makes you special?



Ramesh Raskar, PhD, MIT Media Lab; Dr. Erick Baptista Passos, IFPI (Federal Institute of Technology, Brazil)

Project title: Vision Blocks

Description and research goals: Vision Blocks is a research project that aims to make computer vision available to everyone. Its primary goal is to develop tools for delivering computer vision to masses through an extensible visual programming language and an online application building and sharing system. We have a prototype HTML5 client that already performs computer vision tasks locally. Our goals for the next iterations include integration with App Engine for preprocessing of video streaming platforms.



Norman Sadeh, Professor, Director of Mobile Commerce Lab, School of

Computer Science, Carnegie Mellon University; Justin Cranshaw, PhD student, School of Computer Science, Hazim Almuhimedi, PhD student, School of Computer Science

Project title: Mapping the Dynamics of a City & Nudging Twitter Users

Description and research goals: We are working on two research

projects. The first is Livehoods in which we take a computational approach to analyzing large-scale trends in the ways people move through dense urban areas. Our goal is to find algorithmic ways of uncovering local collective knowledge about the city using social media. The second is “Nudging Twitter Users” in which we utilize quantitative and qualitative approaches to understand why people post things on Twitter they wish they had not, and also to understand the nature of these posts. Our objective is to develop tools that help nudge users to reduce the likelihood of those posts.



William Stein, Professor of Mathematics, University of Washington

Project title: Sage: Creating a Viable Free Open Source Alternative to Magma, Maple, Matlab, and Mathematica

Description and research goals: The goal is to create a highly scalable and resilient website through which very large numbers of people can use Sage. This is the next step.



Enrique Vivoni, Associate Professor, Hydrologic Science, Engineering & Sustainability, Arizona State University; Dr. Giuseppe Mascaro, Research Engineer; Jyothi Marupila, Graduate Student; Mario A. Rodriguez, Software Engineer

Project title: Cloud Computing-Based Visualization and Access of Global Climate Data Sets

Description and research goals: Our project uses Google App Engine for analyzing global climate data within the Google Maps API. At this stage, we are able to generate loads from the Global Land Data Assimilation Systems (GLDAS) climate model into the Google App Engine datastore. We select the climate variable to be used and aggregate data at different spatial resolutions. We are using Google App Engine Task Queue API to load large files. For the presentation layer, we are using Django templates to integrate the display of many data points in the Google Maps API. Our objective is to provide scientific data on global climate trends by allowing map-based queries and summaries at the appropriate resolutions. Sample Map



Currently, no further rounds for Google App Engine Research Awards have been planned. We will announce any updates to the program on our website.


URL: http://feedproxy.google.com/~r/blogspot/gJZg/~3/CQ1d_a_EOlg/research-projects-on-google-app-engine.html

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