Titre :

Analyzing Complex Data in Motion at Scale with Temporal Graphs

Auteur(s) :

Hartmann, Thomas [Auteur]
Security, Reliability and Trust Interdisciplibary Research Centre [S'nT]
Fouquet, Francois [Auteur]
Security, Reliability and Trust Interdisciplibary Research Centre [S'nT]
Jimenez, Matthieu [Auteur]
Security, Reliability and Trust Interdisciplibary Research Centre [S'nT]
Rouvoy, Romain [Auteur]
Institut universitaire de France [IUF]
Self-adaptation for distributed services and large software systems [SPIRALS]
Le Traon, Yves [Auteur]
Security, Reliability and Trust Interdisciplibary Research Centre [S'nT]

Éditeur(s) ou directeur(s) scientifique(s) :

Xudong He
Oscar Pereira
Angelo Perkusich

Titre de la manifestation scientifique :

The 29th International Conference on Software Engineering & Knowledge Engineering (SEKE'17)

Ville :

Pittsburgh

Pays :

Etats-Unis d'Amérique

Date de début de la manifestation scientifique :

2017-07-05

Titre de la revue :

Proceedings of the 29th International Conference on Software Engineering & Knowledge Engineering (SEKE'17)

Éditeur :

KSI Research

Mot(s)-clé(s) en anglais :

Data analytics
Graph databases
Large-scale graphs
Time-evolving graphs

Discipline(s) HAL :

Informatique [cs]/Génie logiciel [cs.SE]
Informatique [cs]/Informatique ubiquitaire
Informatique [cs]/Informatique mobile
Informatique [cs]/Web
Informatique [cs]/Système d'exploitation [cs.OS]

Résumé en anglais : [en]

Modern analytics solutions succeed to under- stand and predict phenomenons in a large diversity of software systems, from social networks to Internet-of-Things platforms. This success challenges analytics algorithms to ...
Lire la suite >Modern analytics solutions succeed to under- stand and predict phenomenons in a large diversity of software systems, from social networks to Internet-of-Things platforms. This success challenges analytics algorithms to deal with more and more complex data, which can be structured as graphs and evolve over time. However, the underlying data storage systems that support large-scale data analytics, such as time-series or graph databases, fail to accommodate both dimensions, which limits the integration of more advanced analysis taking into account the history of complex graphs, for example. This paper therefore introduces a formal and practical definition of temporal graphs. Temporal graphs provide a compact representation of time-evolving graphs that can be used to analyze complex data in motion. In particular, we demonstrate with our open-source implementation, named GreyCat, that the performance of temporal graphs allows analytics solutions to deal with rapidly evolving large-scale graphs.Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Collections :

• Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL