Mining Big Data for Detecting, Extracting and Recommending Architectural Design Concepts An architecture recommender system can help programmers make better design choices to address their architectural quality attribute concerns while doing their daily programming tasks. We mine big data to detect and extract a large set of architectural design concepts, such as design patterns, design tactics, architecture styles, etc., to be used in our architecture recommender system called ARS. However, mining big data poses many practical challenges for system implementation. The volume, velocity and variety of our data set, like all other big data systems, requires careful planning. This first challenge is to select appropriate technologies from the large number of available products for our system implementation. Building on these technologies our greatest challenge is to custom-fit our algorithms to the parallel processing platform we have selected for ARS, to meet our performance goals.