An Analysis on Graph-Processing Frameworks: Neo4j and Spark GraphX - Inria - Institut national de recherche en sciences et technologies du numérique Accéder directement au contenu
Communication Dans Un Congrès Année : 2022

An Analysis on Graph-Processing Frameworks: Neo4j and Spark GraphX

Résumé

Numerous graph algorithms have been developed to address a variety of problems in the industry, ranging from fraud detection to scheduling or even recommendation systems. Graph-processing frameworks are hence created to simplify the implementation of graph-based solutions. Nonetheless, the number of such frameworks has grown significantly over the past decades with varying benefits and drawbacks. Understanding the requirements and characteristics of each framework plays a vital role in the selection of a suitable solution to a given problem. In this work, we evaluate the performance and usability of 2 popular graph-processing frameworks Neo4j and Apache Spark GraphX by implementing a PageRank solution to solve a practical business problem derived from the Yelp dataset.
Fichier sous embargo
Fichier sous embargo
0 5 16
Année Mois Jours
Avant la publication
mercredi 1 janvier 2025
Fichier sous embargo
mercredi 1 janvier 2025
Connectez-vous pour demander l'accès au fichier

Dates et versions

hal-04317173 , version 1 (01-12-2023)

Licence

Identifiants

Citer

Alabbas Alhaj Ali, Doina Logofătu. An Analysis on Graph-Processing Frameworks: Neo4j and Spark GraphX. 18th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Jun 2022, Hersonissos, Greece. pp.461-470, ⟨10.1007/978-3-031-08333-4_37⟩. ⟨hal-04317173⟩
17 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Mastodon Facebook X LinkedIn More