Experiences with Implementing Landmark Embedding in Neo4j

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2019
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Proceedings of the 2nd Joint International Workshop on Graph Data Management Experiences & Systems (GRADES) and Network Data Analytics (NDA) - GRADES-NDA'19. New York: ACM Press, 2019, 7. ISBN 978-1-4503-6789-9. Available under: doi: 10.1145/3327964.3328496
Zusammenfassung

Reachability, distance, and shortest path queries are fundamental operations in the field of graph data management with various applications in research and industry. However, while various preprocessing-based methods have been proposed to optimize the computation of such queries, the integration of existing methods into graph database management systems and processing frameworks has been limited. In this paper, we present an implementation of a static graph index that employs landmark embedding for Neo4j, to enable the index-based computation of reachability, distance, and shortest path queries on the database. We explore different strategies for selecting landmarks and different schemes for storing the precomputed landmark distances. To evaluate the efficiency of each landmark selection strategy and each storage scheme, we conduct an experimental evaluation using four real-world network datasets. We measure the preprocessing cost, the query processing time, and the accuracy of the distance estimation of different configurations of our index structure.

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The 2nd Joint International Workshop on Graph Data Management Experiences & Systems (GRADES) and Network Data Analytics (NDA), 30. Juni 2019 - 5. Juli 2019, Amsterdam, Netherlands
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ISO 690HOTZ, Manuel, Theodoros CHONDROGIANNIS, Leonard WÖRTELER, Michael GROSSNIKLAUS, 2019. Experiences with Implementing Landmark Embedding in Neo4j. The 2nd Joint International Workshop on Graph Data Management Experiences & Systems (GRADES) and Network Data Analytics (NDA). Amsterdam, Netherlands, 30. Juni 2019 - 5. Juli 2019. In: Proceedings of the 2nd Joint International Workshop on Graph Data Management Experiences & Systems (GRADES) and Network Data Analytics (NDA) - GRADES-NDA'19. New York: ACM Press, 2019, 7. ISBN 978-1-4503-6789-9. Available under: doi: 10.1145/3327964.3328496
BibTex
@inproceedings{Hotz2019Exper-46242,
  year={2019},
  doi={10.1145/3327964.3328496},
  title={Experiences with Implementing Landmark Embedding in Neo4j},
  isbn={978-1-4503-6789-9},
  publisher={ACM Press},
  address={New York},
  booktitle={Proceedings of the 2nd Joint International Workshop on Graph Data Management Experiences & Systems (GRADES) and Network Data Analytics (NDA)  - GRADES-NDA'19},
  author={Hotz, Manuel and Chondrogiannis, Theodoros and Wörteler, Leonard and Grossniklaus, Michael},
  note={Article Number: 7}
}
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