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Development of Services for Big Data Analytics with Everest Platform

Student: Fayzullina Dinara

Supervisor: Oleg V. Sukhoroslov

Faculty: Faculty of Computer Science

Educational Programme: Applied Mathematics and Information Science (Bachelor)

Year of Graduation: 2016

Everest is a web-based platform for distributed execution of scientific applications and data transfer is one of the key factors of the functionality of such platforms. Everest has some constraints on data management, in particular, supported means presuppose that all data have to be uploaded to Everest server before transferring to the computing resources. Thus, finding possible solutions to data transfer problem has become one of the tasks of the research. In the context of data transfer problem Everest platform has been integrated with Dropbox allowing users to work with data through Dropbox file hosting system. Moreover, extra functionality has been added that enables users to run applications with data downloaded from Dataverse public repository using Digital Object Identifier. Another problem that is stated in the research concerns the development of Everest applications for distributed data processing that is aimed to show the importance of the publication of such applications as services. As an example of such application the computationally intensive task of mapping reads on a reference genome has been chosen. As a result of working on this part of the research Everest application using Hadoop MapReduce technology for solving mapping task was implemented. The application implementation takes care about data download from FTP server that can be regarded as a solution to data transfer problem in a particular case and it can be integrated with Everest on a permanent basis. In the near future it is planned to provide access to the new functionality for data transfer for all users, though, firstly, production status approval has to be received from Dropbox support. Moreover, the integration of Everest platform and Globus Online service is under consideration as it may grant reliable and fast tools for big data transfer from users to computing resources and vice versa.

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