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.
%0 Journal Article
%1 Rost2021-ze
%A Rost, Christopher
%A Fritzsche, Philip
%A Schons, Lucas
%A Zimmer, Maximilian
%A Gawlick, Dieter
%A Rahm, Erhard
%D 2021
%I arXiv
%K
%T Bitemporal property graphs to organize evolving systems
%X 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{Rost2021-ze,
abstract = {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.},
added-at = {2024-09-10T11:56:37.000+0200},
author = {Rost, Christopher and Fritzsche, Philip and Schons, Lucas and Zimmer, Maximilian and Gawlick, Dieter and Rahm, Erhard},
biburl = {https://puma.scadsai.uni-leipzig.de/bibtex/2e5f2995b6e7373dbe8daba6991e3e1ea/scadsfct},
interhash = {5a2a7bc0f2657de64ccece1d38c5962b},
intrahash = {e5f2995b6e7373dbe8daba6991e3e1ea},
keywords = {},
publisher = {arXiv},
timestamp = {2024-09-10T15:15:57.000+0200},
title = {Bitemporal property graphs to organize evolving systems},
year = 2021
}
