This work is a summarized view on the results of a one-year cooperation between Oracle Corp. and the University of Leipzig. The goal was to research the organization of relationships within multi-dimensional time-series data, such as sensor data from the IoT area. We showed in this project that temporal property graphs with some extensions are a prime candidate for this organizational task that combines the strengths of both data models (graph and time-series). The outcome of the cooperation includes four achievements: (1) a bitemporal property graph model, (2) a temporal graph query language, (3) a conception of continuous event detection, and (4) a prototype of a bitemporal graph database that supports the model, language and event detection.
@article{arxiv.2111.13499,
title = {Bitemporal Property Graphs to Organize Evolving Systems},
author = {Christopher Rost and Philip Fritzsche and Lucas Schons and Maximilian Zimmer and Dieter Gawlick and Erhard Rahm},
journal= {arXiv preprint arXiv:2111.13499},
year = {2021}
}