A Clouded Future: Analysis of Microsoft Windows Azure As a Platform for Hosting E-Science Applications

Microsoft Windows Azure is Microsoft\u27s cloud based platform for hosting .NET applications. Azure provides a simple, cost effective method for outsourcing application hosting. Windows Azure has caught the eye of researchers in e-science who require parallel computing infrastructures to process mountains of data. Windows Azure offers the same benefits to e-science as it does to other industries. This paper examines the technology behind Azure and analyzes two case studies of e-science projects built on the Windows Azure platform

GRID AND CLOUD COMPUTING FOR E-SCIENCE APPLICATIONS

eScience fields which include areas such as spatial data, electromagnetic,bioinformatics, energy, social sciences, simulation, physical science have on the course of recent years a significant development regarding the complexity of algorithms and applications for data analysis. Information data has also evolved with an explosion in term of data volume and datasets for the scientific community. This has led researchers to identify new necessity regarding tools analysis, applications, by a profound change in computing infrastructures utilization. The field of eScience is constantly evolving through the creation of ever more growing scientific community who have a real needs in availability in computational resources ever more powerful calculations. Another important issue is the ability to be able to share results, this is why cloud technology through virtualization can be an important help for the scientist community for giving a flexible and scalable IT infrastructure depending on necessities. Indeed, cloud computing allows for the provision of computing resources, storage in an easy configurable way and adaptable in functions of real needs. Researchers often do not have all the computing capacities to meet their needs, so cloud technology and cloud models as Private, Public and Hybrid is an enable technology for having a guarantee of service availability, scalability and flexibility. The transition from traditional infrastructure to new virtualized with distributed models allows researchers to have access to an environment extremely flexible allowing an optimization of the use of hardware for having more available resources. However, the computational needs on e-Science have a direct effect regarding the way that applications are developed. The approach of writing algorithm and applications is still too tied to a model centered on a workstation for example. The vast majority of researchers conducts the writing process of their applications on their laptop or workstation in a limited context of computing power, storage and in a non-distributed way

GRID AND CLOUD COMPUTING FOR E-SCIENCE APPLICATIONS

eScience fields which include areas such as spatial data, electromagnetic,bioinformatics, energy, social sciences, simulation, physical science have on the course of recent years a significant development regarding the complexity of algorithms and applications for data analysis. Information data has also evolved with an explosion in term of data volume and datasets for the scientific community. This has led researchers to identify new necessity regarding tools analysis, applications, by a profound change in computing infrastructures utilization. The field of eScience is constantly evolving through the creation of ever more growing scientific community who have a real needs in availability in computational resources ever more powerful calculations. Another important issue is the ability to be able to share results, this is why cloud technology through virtualization can be an important help for the scientist community for giving a flexible and scalable IT infrastructure depending on necessities. Indeed, cloud computing allows for the provision of computing resources, storage in an easy configurable way and adaptable in functions of real needs. Researchers often do not have all the computing capacities to meet their needs, so cloud technology and cloud models as Private, Public and Hybrid is an enable technology for having a guarantee of service availability, scalability and flexibility. The transition from traditional infrastructure to new virtualized with distributed models allows researchers to have access to an environment extremely flexible allowing an optimization of the use of hardware for having more available resources. However, the computational needs on e-Science have a direct effect regarding the way that applications are developed. The approach of writing algorithm and applications is still too tied to a model centered on a workstation for example. The vast majority of researchers conducts the writing process of their applications on their laptop or workstation in a limited context of computing power, storage and in a non-distributed wa

Unsupervised, Efficient and Semantic Expertise Retrieval

We introduce an unsupervised discriminative model for the task of retrieving experts in online document collections. We exclusively employ textual evidence and avoid explicit feature engineering by learning distributed word representations in an unsupervised way. We compare our model to state-of-the-art unsupervised statistical vector space and probabilistic generative approaches. Our proposed log-linear model achieves the retrieval performance levels of state-of-the-art document-centric methods with the low inference cost of so-called profile-centric approaches. It yields a statistically significant improved ranking over vector space and generative models in most cases, matching the performance of supervised methods on various benchmarks. That is, by using solely text we can do as well as methods that work with external evidence and/or relevance feedback. A contrastive analysis of rankings produced by discriminative and generative approaches shows that they have complementary strengths due to the ability of the unsupervised discriminative model to perform semantic matching.Comment: WWW2016, Proceedings of the 25th International Conference on World Wide Web. 201