Special section on large-scale analytics

Abstract

Big Data is no longer exclusively the domain of big organizations. Companies, collaborations, and organizations of all types and sizes are increasingly faced with the need to analyze and make sense of large and growing collections of data. Solving the challenge of large-scale analytics requires innovation across the spectrum of data management: Large volumes of data have to be acquired, processed, stored, and eventually reclaimed. Complex statistical procedures must be applied to those large data sets. Transactional guarantees are required to provide a consistent picture with operational systems.Metadata must be maintained to provide the context of the underlying rawdata for later analysis. These challenges must be faced independently and together in order to establish a scalable, affordable, and flexible large-scale analytics infrastructure. This special section focuses on conceptual and systemsarchitecture issues in this emerging area. The three selected papers present recent efforts that push the envelope of novel schemes for large-scale analytics and provide a deeper understanding and assessment of the current state of the art. The first paper of the special section focuses on optimizing the well-known MapReduce processing paradigm. The proposed approach exploits the fact that MapReduce clusters support multiple, concurrently running jobs often accessing the same set of data. The paper entitled “On the optimization of schedules for MapReduce workloads in the presence of shared scans” improves traditional MapReduce processing