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A model and query language for temporal graph databases

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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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Notes

  1. http://www.neo4j.com.

  2. http://janusgraph.org/.

  3. https://www.w3.org/RDF/.

  4. https://neo4j.com/docs/cypher-manual/current/.

  5. https://www.antlr.org/.

  6. http://javalin.io.

  7. https://github.com/neo4j-contrib/neo4j-graph-algorithms.

  8. https://www.kaggle.com/usdot/flight-delays?select=flights.csv.

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Acknowledgements

Alejandro Vaisman and Valeria Soliani were partially supported by Project PICT 2017-1054, from the Argentinian Scientific Agency.

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Authors and Affiliations

  1. Department of Information Engineering, Instituto Tecnológico de Buenos Aires, Lavardén 315, C1437FBG, Ciudad Autónoma de Buenos Aires, Argentina

    Ariel Debrouvier, Eliseo Parodi, Matías Perazzo, Valeria Soliani & Alejandro Vaisman

  2. Hasselt University, Data Science Institute, Databases and Theoretical Computer Science Research Group, Agoralaan, Gebouw D, 3590, Diepenbeek, Belgium

    Valeria Soliani

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  1. Ariel Debrouvier
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  2. Eliseo Parodi
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Appendices

Appendix A: Characteristics of the data sets

See Table 8.

Table 8 Number of incoming and outgoing flights for each airport
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Appendix B: Summary of main concepts

See Table 9.

Table 9 Continuous paths experiments: Characteristics of each social network data set
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Debrouvier, A., Parodi, E., Perazzo, M. et al. A model and query language for temporal graph databases. The VLDB Journal 30, 825–858 (2021). https://doi.org/10.1007/s00778-021-00675-4

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