research-article

A model and query language for temporal graph databases

Authors:

Ariel Debrouvier,

Matías Perazzo,

Valeria Soliani,

Alejandro VaismanAuthors Info & Claims

The VLDB Journal, Volume 30, Issue 5

Pages 825 - 858

https://doi.org/10.1007/s00778-021-00675-4

Published: 12 May 2021 Publication History

Abstract

Graph databases are becoming increasingly popular for modeling different kinds of networks for data analysis. They are built over the property graph data model, where nodes and edges are annotated with property-value pairs. Most existing work in the field is based on graphs were the temporal dimension is not considered. However, time is present in most real-world problems. Many different kinds of changes may occur in a graph as the world it represents evolves across time. For instance, edges, nodes, and properties can be added and/or deleted, and property values can be updated. This paper addresses the problem of modeling, storing, and querying temporal property graphs, allowing keeping the history of a graph database. This paper introduces a temporal graph data model, where nodes and relationships contain attributes (properties) timestamped with a validity interval. Graphs in this model can be heterogeneous, that is, relationships may be of different kinds. Associated with the model, a high-level graph query language, denoted T-GQL, is presented, together with a collection of algorithms for computing different kinds of temporal paths in a graph, capturing different temporal path semantics. T-GQL can express queries like “Give me the friends of the friends of Mary, who lived in Brussels at the same time than her, and also give me the periods when this happened”. As a proof-of-concept, a Neo4j-based implementation of the above is also presented, and a client-side interface allows submitting queries in T-GQL to a Neo4j server. Finally, experiments were carried out over synthetic and real-world data sets, with a twofold goal: on the one hand, to show the plausibility of the approach; on the other hand, to analyze the factors that affect performance, like the length of the paths mentioned in the query, and the size of the graph.

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cover image The VLDB Journal — The International Journal on Very Large Data Bases

The VLDB Journal — The International Journal on Very Large Data Bases Volume 30, Issue 5

Sep 2021

191 pages

ISSN:1066-8888

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© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021.

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 12 May 2021

Accepted: 24 April 2021

Revision received: 11 March 2021

Received: 12 July 2020

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