Building architectures for data-intensive science using the ADAGE framework

摘要

One of the main activities in data-intensive science is data analysis. Although there are many popular technologiesrnthat can assist scientists in various isolated aspects of data analysis, supporting analysis processesrnin holistic ways that promote system interoperability, integration and automation, as well as scientificrnreproducibility and efficient data handling, presents many challenges. A common solution to address thesernchallenges is to find efficient ways of integrating various existing technologies together to meet the analysisrnneeds of scientists (which is similar to the idea behind science gateways). We believe that this solution isrnessentially an exercise in software design; and in many situations, these challenges should be tackled fromrna software design perspective. Consequently, this paper reviews different architectural design approachesrnthat can be used to address these challenges and proposes a service-oriented framework called the AdrnHoc Data Grid Environment, which consists of an architectural pattern and its associated operationalrnguidelines. The guidelines prescribe a number of activities based on an iterative decomposition approachrnto produce and evolve software architectures according to constantly changing user needs. The frameworkrnis demonstrated on a case study involving analysis processes required for conducting financial event studies.