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Querying Temporal Property Graphs

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Advanced Information Systems Engineering (CAiSE 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13295))

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Abstract

Nowadays, in many real-world problems, objects are characterized by properties and interactions that evolve over time. Several temporal property graph models associated with query languages are proposed in the literature to manage the temporal and interconnectivity features of such problems. However, they are not widely used due to the lack of a conceptual view. To overcome this drawback, we propose user-oriented operators to analyze temporal evolution of property graphs. We also define translation rules between our operators and existing property graph query languages to implement them directly. To illustrate the feasibility of our solution, we present two case studies based on a Neo4j and an OrientDB implementations of our operators and show some real-world querying examples.

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Notes

  1. 1.

    https://www.w3.org/TR/rdf-sparql-query/.

  2. 2.

    https://neo4j.com/developer/cypher/querying/.

  3. 3.

    Graphs are input and output of queries [5].

  4. 4.

    Basic graph pattern matching consists in retrieving subgraphs that match a user-defined pattern (or graph structure).

  5. 5.

    Available on the Reality Commons website http://realitycommons.media.mit.edu/socialevolution.html.

  6. 6.

    Participate at least one common activity.

  7. 7.

    Bluetooth signal sent from whose mobile phone and received by whose mobile phone and time, indicating the sender’s mobile phone was within 10 m of the receiver’s mobile phone at the time of the record.

  8. 8.

    The value of a symptom attribute equals 0 if the student does not have the symptom and 1 if the student has the symptom.

  9. 9.

    http://www.tpc.org/tpc_documents_current_versions/pdf/tpc-ds_v2.13.0.pdf.

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Authors and Affiliations

  1. IRIT-CNRS (UMR 5505) - Université Toulouse 1 Capitole (UT1), 2 Rue du Doyen Gabriel Marty, 31042, Toulouse Cedex 09, France

    Landy Andriamampianina, Franck Ravat, Jiefu Song & Nathalie Vallès-Parlangeau

  2. Activus Group, 1 Chemin du Pigeonnier de la Cépière, 31100, Toulouse, France

    Landy Andriamampianina & Jiefu Song

Authors
  1. Landy Andriamampianina
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  2. Franck Ravat
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  3. Jiefu Song
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  4. Nathalie Vallès-Parlangeau
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Corresponding author

Correspondence to Landy Andriamampianina .

Editor information

Editors and Affiliations

  1. Department of Service and Information System Engineering (ESSI), Universitat Politècnica de Catalunya, Barcelona, Spain

    Xavier Franch

  2. Ghent University, Gent, Belgium

    Geert Poels

  3. Ghent University, Gent, Belgium

    Frederik Gailly

  4. KU Leuven, Leuven, Belgium

    Monique Snoeck

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Andriamampianina, L., Ravat, F., Song, J., Vallès-Parlangeau, N. (2022). Querying Temporal Property Graphs. In: Franch, X., Poels, G., Gailly, F., Snoeck, M. (eds) Advanced Information Systems Engineering. CAiSE 2022. Lecture Notes in Computer Science, vol 13295. Springer, Cham. https://doi.org/10.1007/978-3-031-07472-1_21

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  • DOI: https://doi.org/10.1007/978-3-031-07472-1_21

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  • Online ISBN: 978-3-031-07472-1

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