How to perform research in Hadoop environment not losing mental equilibrium - case study
This work addresses the problem of maintaining mental equilibrium and reducing costs for researchers and developers working with Hadoop, but it appears incremental as it focuses on best practices and tools rather than novel breakthroughs.
The paper tackles the challenge of conducting efficient, repetitive, and convenient research in a Hadoop environment by presenting guidelines based on the Content Analysis System (CoAnSys) developed at the Center for Open Science, aiming to minimize software engineering costs and improve research workflows.
Conducting a research in an efficient, repetitive, evaluable, but also convenient (in terms of development) way has always been a challenge. To satisfy those requirements in a long term and simultaneously minimize costs of the software engineering process, one has to follow a certain set of guidelines. This article describes such guidelines based on the research environment called Content Analysis System (CoAnSys) created in the Center for Open Science (CeON). Best practices and tools for working in the Apache Hadoop environment, as well as the process of establishing these rules are portrayed.