https://www.ronpub.com/ojbd/OJBD_2018v4i1n03_Chen.html Channel of the paper: Xiao Chen, Eike Schallehn and Gunter Saake: Cloud-Scale Entity Resolution: Current State and Open Challenges, Open Journal of Big Data (OJBD), 4 (1), pages 30-51, URN: urn:nbn:de:101:1-201804155766, 2018 en-us https://www.ronpub.com/ojbd/OJBD_2018v4i1n03_Chen.html http://nbn-resolving.de/urn:nbn:de:101:1-201804155766 Entity resolution (ER) is a process to identify records in information systems, which refer to the same real-world entity. Because in the two recent decades the data volume has grown so large, parallel techniques are called upon to satisfy the ER requirements of high performance and scalability. The development of parallel ER has reached a relatively prosperous stage, and has found its way into several applications. In this work, we first comprehensively survey the state of the art of parallel ER approaches. From the comprehensive overview, we then extract the classification criteria of parallel ER, classify and compare these approaches based on these criteria. Finally, we identify open research questions and challenges and discuss potential solutions and further research potentials in this field.