Bitemporal Property Graphs to Organize Evolving Systems

TL;DR

This paper presents a novel bitemporal property graph model and query language designed to organize and analyze evolving multi-dimensional time-series data, exemplified by sensor data in IoT applications.

Contribution

It introduces a new bitemporal property graph model, a temporal query language, and a prototype database supporting these innovations, advancing data organization for dynamic systems.

Findings

Developed a bitemporal property graph model

Created a temporal graph query language

Built a prototype supporting the model and event detection

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.