The data volume of many scientific applications has substantially increased in the past decade and continues to increase due to the rising needs of high-resolution and fine- granularity scientific discovery. The data movement between stor- age and compute nodes has become a critical performance factor and has attracted intense research and development attention in recent years. In this paper, we propose a novel solution, named Active burst-buffer, to reduce the unnecessary data movement and to speed up scientific workflow. Active burst-buffer enhances the existing burst-buffer concept with analysis capabilities by reconstructing the cached data to a logic file and providing a MapReduce-like computing framework for programming and executing the analysis codes. An extensive set of experiments were conducted to evaluate the performance of Active burst-buffer by comparing it against existing mainstream schemes, and more than 30% improvements were observed. The evaluations confirm that Active burst-buffer is capable of enabling efficient data analysis in-transit on burst-buffer nodes and is a promising solution to scientific discoveries with large-scale data sets.