We take you through 10 best practices, considerations, and suggestions that can enrich your Microsoft Teams deployment and ensure both end-user adoption and engagement.
Microsoft just a few days ago announced a new program for research. They have partnered with several researchers over the last 3 years to experiment with the cloud. With the success of the research projects already completed Microsoft found value in the research. To support scientific research in many disciplines Microsoft launched the Windows Azure for Research program. Microsoft will offer some training and aim to award at least 100 grants a year.
If you would like to apply for a grant go to the proposal submission site:
“Proposals will be evaluated and awards announced six times a year. The first deadline for proposals is October 15, 2013, and results will be announced within two weeks after that date. The next deadline is December 15, 2013, and on the fifteenth of every other month after that.”
Below is a list from the program site of some of the projects that have already been conducted and supported under this program. So if you believe you can benefit from this program it doesn’t hurt to apply.
Twister4Azure: Data Analytics in the Cloud
Thilina Gunarathne, Xiaoming Gao, and Judy Qiu, Indiana University
The project applies innovations in MapReduce to microbial genomics analysis in the cloud.
Using the Cloud to Model and Manage Large Watershed Systems
Jon Goodall, University of South Carolina; and Marty Humphrey, University of Virginia
This project explores extending hydraulic systems analysis beyond modeling to include the entire workflow from data collection to decision making.
Network3D on Windows Azure: Web Portal for Ecological Network Simulations & Analysis
Jennifer Dunne, Santa Fe Institute; Sanghyuk Yoon and Neo Martinez, Pacific Ecoinformatics and Computational Ecology Lab
The long-term scientific goal is the development of a theory that accurately predicts the response of different species and whole ecosystems to physical changes in the environment.
Developing the Forecast-as-a-Service (FaaS) Framework for Renewable Energy Sources
Kwa-Sur Tam, Virginia Tech
Accurate forecast is key to effective utilization of weather-dependent renewable energy sources, such as wind and solar. This project will enable the combined use of different types of data from different sources and enhance the synthesis of more accurate forecasts by using prediction results from different models.
XCloud: Seamless Integration of Multiple Clouds
Hakim Weatherpoon, Cornell University
This project will explore an architecture that decouples cloud users from cloud providers and eliminates the need to share a complex hypervisor.
Use of Azure for Computing in the Cloud.
Douglas Thain, University of Notre Dame
This project studied the implications of moving a particular family of applications (ensemble molecular dynamics) across a range of large-scale distributed systems.
Architecting Delay-Sensitive and Scalable Cloud Applications
Mohammad Hajjat, Shankar Narayanan, and Sanjay Rao, Purdue University
This project tackles the challenges that application designers face in architecting scalable and performance-sensitive applications across cloud data centers, while meeting their performance, cost, and policy constraints
Large-Scale Annotation of Gene Transcription Regulatory Sequences in Bacterial Genomes Using Cloud Computing
Zhengchang Su, Srinivas Arkela, and Youjie Zhou, the University of North Carolina at Charlotte
The project will annotate regulatory sequences in sequenced bacterial genomes by using comparative genomics-based algorithms.
SAMP-Computing in the Cloud Using the Windows Azure Platform
Estela Blaisten-Barojas, George Mason University
This project will create web interfaces that enable the execution of our Structure-Adaptive Materials Prediction (SAMP) software modules, and make them available to a larger community.
FACET on Cloud: a Social Classification System
Harris Wu, Kurt Maly, Mohammad Zubair, Old Dominion University
This project is developing a web-based system (FACET) that allows users to collaboratively organize and classify multimedia collections.
Spatial Overlay Operations on Vector-Based GIS Data on Azure
Sushil Prasad, Georgia State University
Geographic information systems and science (GIS) have always perceived large-scale, vector-data computation as a challenge, due to the intensity of the data. To solve this problem, this project uses massive parallelism in the cloud.
SQL Share: Database-as-a-Service for Long-Tail Science
Bill Howe, Garret Cole, Alicia Key, Nodira Khoussainova, and Leilani Battle, University of Washington
To simplify the challenge of managing research data, the University of Washington e-Science Institute has built a cloud-based relational data sharing and analysis platform called SQLShare that allows users to upload their spreadsheet data and immediately query it by using SQL—no schema design, no reformatting, and no database administrators are required.
Scalable Algebraic Visualization in the Cloud
Bill Howe, University of Washington
A visualization algebra—analogous to the relational algebra but specialized for manipulating 4-D mesh datasets—has been built to support the oceanographic community for interoperable visualization and analysis for unstructured grid model data.
Protein Folding with Rosetta@home in the Cloud
Nikolas Sgourakis, University of Washington
The scientific challenge was to elucidate the structure of a molecular machine called the needle complex, which is involved in the transfer between cells of dangerous bacteria, such as salmonella and e-coli. This was accomplished by porting Rossetta to Windows Azure using 2,000 cores.
Scalable, Secure Analysis of Social Network Graph on the Azure Platform
Yogesh Simmhan, Alok Kumbhare, Mark Redekopp, and Viktor Prasanna, University of Southern California
This project built Cryptonite, a scalable storage repository that was designed to share datasets securely among a collaboration of users on untrusted public clouds. Cryptonite was used to protect the data used in the study of social networks.
Towards a Mobile Cloud Computing Framework to Support Next-Generation Mobile Applications
Richard Han, University of Colorado at Boulder
The main thrust of this project is to build a mobile cloud computing infrastructure called SocialFusion to support context-aware mobile social applications.
BetaSIM on the Cloud
Michele Di Cosmo, Angela Sanger, University of Trento Centre for Computational and Systems Biology
BetaSIM is a dry experiment simulator, driven by BlenX—a stochastic, process algebra-based programming language for modeling and simulating biological systems as well as other complex dynamic systems.
Running Fire Risk Estimation and Fire Propagation Models on Windows Azure
Nikos Athanasis, University of the Aegean
The application scenario addresses emergency management to help with wildfire early warning, fire control, and civil protection. The main objectives focus on adapting and migrating applications and models for the prediction of fire risk and fire propagation simulation in the Windows Azure cloud computing infrastructure, and providing an interactive cloud-hosted user interface.
A Structural Analysis Cloud Service Implementation Through Azure
José M. Alonso, Pedro de la Fuente, Vicente Hernández, Pau Lozano, Universidad Politécnica de Valencia
The main aim of this work is to develop an innovative cloud system that offers on-demand, high-performance static and dynamic structural simulations to the structural community.
Privacy and Personal Data in Crowd Sourcing
Dominic Price, University of Nottingham
This team proposes a “marketplace” and toolkit for secure crowd-sourcing activities as part of the UK Horizon project, which focuses on the role of “always on, always with you” ubiquitous computing technology.
Porting Bioinformatics Applications in Azure
Abel Carrión, Ignacio Blanquer, and Vicente Hernández, Universidad Politécnica de Valencia
This project has prototyped and developed a client tool that provides the same interface as the conventional bioinformatics tools (such as BLAST, bwa, fasta, bowtie, BLAT, and ssaha) that are used in local computers, but linking them to a powerful processing service on the cloud.
Drug Discovery in the Azure Cloud
Jacek Cala, Paul Watson, University of Newcastle
In the search for new anti-cancer therapies, the family of kinase enzymes are important biological targets since many are intimately connected to cell division and other important maintenance functions. This project ported the eScience central workflow tools to Windows Azure to speed the analysis of these enzymes.
Biodiversity on Windows Azure: Data for Science
Marko Mikulicic, Pasquale Pagano, CNR
The D4science e-Infrastructure aims at providing the scientific community with a wide range of tools and environments for managing research data, organized in virtual research environments that are tailored for the specific needs of scientific communities.
Targets on the Cloud: a Cloud-Based MicroRNA Target Prediction Platform
Theodore Dalamagas, Thanassis Vergoulis, and Michalis Alexakis, Athena RC
In this project, we will set up target prediction methods on the Windows Azure platform to provide real-time target prediction services. Our aim is to meet the requirements for efficient calculation of targets and efficient handling of the frequent updates in the miRNA and gene databases.
Green Prefab: Civil Engineering Hub in Windows Azure
Furio Barzon, Green Prefab
Green Prefab (GPF) is a product life-cycle management system for the building industry sector. GPF enables users to design and construct a new generation of prefabricated buildings by means of collaborative software and industrial production.
Optimizing Data Storage for MapReduce Applications in the Azure Cloud
Radu Marius Tudoran, Gabriel Antoniu, Luc Bougé, and Alexandru Costan, INRIA, France
This project optimizes MapReduce for the challenge of conducting joint genetic and neuroimaging data analysis on large cohorts of subjects.
Evolving Inversion Methods in Geophysics with Cloud Computing
J Craig Mudge and Pinaki Chandrasekhar, University of Adelaide
Magnetotellurics is a geophysics technique for characterization of geothermal reservoirs, mineral exploration, and other geoscience endeavors that need to sound deeply into the earth—many kilometers or tens of kilometers. Central to its data processing is an inversion problem, which currently takes several weeks to complete on a desktop machine.
Location Improvement in VANETs Using Windows Azure
Farhan Ahammed, Javid Taheri, and Albert Y. Zomaya, University of Sydney
This project is concerned with finding lower bounds on the amount of improvement possible on GPS-provided coordinates, by simulating a Virtual Ad Hoc Network (VANET) with vehicles travelling along a road, while communicating with each other to improve their location coordinates.
Secure High Performance Computing on Windows Azure
Javid Taheri and Albert Y. Zomaya, University of Sydney
This project proposes a high-performance computing platform with security guarantees. An example to demonstrate the goal of this project is to try to analyze millions of bank transactions to detect frauds and/or illegal activities.
An Accountable Coordination System for Coal Supply Chains in Australia
Shiping Cheng, CSIRO Australia
A typical transport supply chain (called coal chain in the following) in Australian coal industry involves multiple independent business entities that own different resources, such as coal mines, mine loading points, rail connections, coal loading terminals, and vessels. This project uses cloud technology to provide an optimal supply-chain scheduling and analysis tool.
Argument Structure Analysis of Huge Web Corpora for Improving a Search Engine Infrastructure
Daisuke Kawahara and Sadao Kurohashi, Kyoto University
This team has been developing a search engine infrastructure, TSUBAKI, which is based on deep natural language processing. While most conventional search engines register only words to their indices, TSUBAKI provides a framework that indexes synonym relations, hypernym-hyponym relations, dependency/case/ellipsis relations, and so forth. These indices enable TSUBAKI to capture the semantic matching between a given query and documents more precisely and flexibly. This project used 10,000 cores on Window Azure to complete a huge analysis of a large web corpus for use by TSUBAKI.