Temporal Graph Processing in Modern Memory Hierarchies.

Updates in graph DBMS lead to structural changes in the graph over time with different intermediate states. These intermediate states in a DBMS and the time when the actions to the actual data take place can be processed using temporal DBMSs. Most DBMSs built their temporal features based on their non-temporal processing and storage without considering the memory hierarchy of the underlying system. This leads to slower temporal processing and poor storage utilization. In this paper, we propose a storage and processing strategy for (bi-) temporal graphs using temporal materialized views (TMV) while exploiting the memory hierarchy of a modern system. Further, we show a solution to the query containment problem for certain types of temporal graph queries. Finally, we evaluate the overhead and performance of the presented approach. The results show that using TMV reduces the runtime of temporal graph queries while using less memory.