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GreyCat: Efficient What-If Analytics for ...
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Document type :
Article dans une revue scientifique
DOI :
10.1016/j.is.2019.03.004
Title :
GreyCat: Efficient What-If Analytics for Data in Motion at Scale
Author(s) :
Hartmann, Thomas [Auteur]
Interdisciplinary Centre for Security, Reliability and Trust [Luxembourg] [SnT]
DataThings
Fouquet, François [Auteur]
DataThings
Interdisciplinary Centre for Security, Reliability and Trust [Luxembourg] [SnT]
Moawad, Assaad [Auteur]
DataThings
Security, Reliability and Trust Interdisciplibary Research Centre [S'nT]
Rouvoy, Romain [Auteur] refId
Institut Universitaire de France [IUF]
Self-adaptation for distributed services and large software systems [SPIRALS]
Le Traon, Yves [Auteur]
Journal title :
Information Systems
Pages :
101-117
Publisher :
Elsevier
Publication date :
2019
ISSN :
0306-4379
English keyword(s) :
what-if analysis
graph processing
time-evolving graphs
predictive analytics
HAL domain(s) :
Informatique [cs]/Système d'exploitation [cs.OS]
Informatique [cs]/Génie logiciel [cs.SE]
Informatique [cs]/Base de données [cs.DB]
Informatique [cs]/Algorithme et structure de données [cs.DS]
English abstract : [en]
Over the last few years, data analytics shifted from a descriptive era, confined to the explanation of past events, to the emergence of predictive techniques. Nonetheless, existing predictive techniques still fail to ...
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Over the last few years, data analytics shifted from a descriptive era, confined to the explanation of past events, to the emergence of predictive techniques. Nonetheless, existing predictive techniques still fail to effectively explore alternative futures, which continuously diverge from current situations when exploring the effects of what-if decisions. Enabling prescriptive analytics therefore calls for the design of scalable systems that can cope with the complexity and the diversity of underlying data models. In this article, we address this challenge by combining graphs and time series within a scalable storage system that can organize a massive amount of unstructured and continuously changing data into multi-dimensional data models, called Many-Worlds Graphs. We demonstrate that our open source implementation, GreyCat, can efficiently fork and update thousands of parallel worlds composed of millions of timestamped nodes, such as what-if exploration.Show less >
Language :
Anglais
Peer reviewed article :
Oui
Audience :
Internationale
Popular science :
Non
Collections :
  • Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189
Source :
Harvested from HAL
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