Building an infrastructure for scientific Grid computing: status and goals of the EGEE project

Abstract

The state of computer and networking technology today makes the seamless sharing of computing resources on an international or even global scale conceivable. Scientific computing Grids that integrate large, geographically distributed computer clusters and data storage facilities are being developed in several major projects around the world. This article reviews the status of one of these projects, Enabling Grids for E-SciencE, describing the scientific opportunities that such a Grid can provide, while illustrating the scale and complexity of the challenge involved in establishing a scientific infrastructure of this kind.

Using Laboratory Information Management Systems as central part of a proteomics data workflow

“…In the beginning of any kind of project organizing, data is a very important step, like the CERN project demonstrates [1], where data are distributed over countries. But this example leads to the problems of data storage in proteomics research.…”

Recording Process Documentation in the Presence of Failures

“…Secondly, though replication is sophisticated, it comes with a significant cost due to preserving the one-copy equivalence property [23]. Given that the documentation produced in a process can be on the order of terabytes [11], replication becomes very expensive and time consuming. Therefore, compared with replicating provenance stores, the use of alternative stores is a more general, simple and flexible approach.…”

Cloud–Based Evaluation Framework for Big Data

“…In other scientific fields such as physics, large Grid networks [5] such as EGI (European Grid Initiative) or previously EGEE (Enabling Grids for E-Science in Europe) [7] have been created for distributed data analysis where several large institutions share large infrastructures in virtual organizations. Still, most Grid middleware is hard to install and many small research groups in computer science do not have the funding to maintain such infrastructures, meaning that this model cannot be transferred to all fields of science and the medical imaging field is an area where this can be problematic [15].…”

“…In the area of IR, evaluation campaigns have been run for over 20 years [10]. Current evaluation campaigns include TREC (Text REtrieval Conference) 5 , TRECVid (TREC Video Evaluation) 6 , CLEF (Cross Language Evaluation Forum) 7 , ImageCLEF [14], NTCIR (NII Test Collection for IR Systems) 8 , INEX (Initiative for the Evaluation of XML Retrieval) 9 and FIRE (Forum for Information Retrieval Evaluation) 10 . In the area of machine learning, the PAS-CAL challenges are well known 11 , while in the area of medical image analysis, annual challenges are organised as part of the Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) 12 .…”