A survey of current challenges in partitioning and processing of graph-structured data in parallel and distributed systems

Abstract

One of the concepts that attracts attention since entering of big data era is the graph-structured data. Suitable frameworks to handle such data would face several constraints, especially scalability, partitioning challenges, processing complexity and hardware configurations. Unfortunately, although several works deal with big data issues, there is a lack of literature review concerning the challenges related to query answering on large-scale graph data. In this survey paper, we review current problems related to the partitioning and processing of graph-structured data. We discuss existing graph processing systems and provide some insights to know how to choose the right system for parallel and distributed processing of large-scale graph data. Finally, we survey current open challenges in this field.