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Although big data has been around for ages, finding the right development method for the specific domain of big data has always been challenging. Many companies are taking maximum benefits from the large amount of data that is available to them. However, in order to make use of this large amount of data, developing and maintaining a dependable and robust software system is a major problem today. We proposed an enriched nonlinear distributed agile development model in big data applications. The model makes it possible to overcome the difficulties of traditional software process models by pairing up evolving technology of big data and distributed agile methodology. In the paper we, first, present arguments behind the multi-agent model. Next, it is shown how it may help improve the interaction between big data and software development project life cycle. Finally, we suggest how the proposed model can be tested experimentally to show how devising a multi-agent computational system may offer an efficient way of monitoring, managing, and deploying software products in big data applications. |
