Romeo Kienzler, Leonardo P. Tizzei, et al.
AGU 2024
We demonstrate the use of a transaction-log that maintains a graph in a serialized form and show how this graph can be materialized in multiple different graph processing systems for specific use-cases. Our demonstration uses this log to build a data observability framework for enterprise-scale data processing systems. In the demo we show two different graph materializations: one generating \emph{reports} for data compliance officers, the other allowing data scientists to perform \emph{analytics}. We demonstrate the greater flexibility of our approach over simply creating different views within the same graph database.
Romeo Kienzler, Leonardo P. Tizzei, et al.
AGU 2024
Phanwadee Sinthong, Dhaval Patel, et al.
VLDB 2022
Oz Anani, Gal Lushi, et al.
SYSTOR 2022
Wenqi Wei, Mu Qiao, et al.
Big Data 2022