With the abundance of event data, the challenge of enabling process discovery in the large has attracted the community attention. Several works addressed the problem by performing process discovery directly on relational databases, instead of the traditional file based computations. Preliminary results show that moving (parts of) process discovery to the database engine outperforms file based computations. However, all existing works consider the traditional storage of event data which assumes that a clear and predefined process instance notion exists, and that events are correlated to one process instance. In this work, we go two steps further. First, we address the problem of process discovery on object-centric event data which allows several process instance notions to be flexibly defined. We refer to it as multi-process discovery Second, motivated by the intrinsic nature of process discovery that searches for relationships in event data, we address the question of how graph-based storage of object-centric event data improves the performance of multi-process discovery? We propose in-database process discovery operators based on labeled property graphs. We use Neo4j as a DBMS and Cypher as a query language. We compare different discovery strategies that involve graph and relational databases. Our results show that process discovery in graph databases outperform existing approaches.