Title :

GreyCat: Efficient What-If Analytics for Data in Motion at Scale

Author(s) :

Hartmann, Thomas [Auteur]
DataThings
Interdisciplinary Centre for Security, Reliability and Trust [Luxembourg] [SnT]
Fouquet, François [Auteur]
Interdisciplinary Centre for Security, Reliability and Trust [Luxembourg] [SnT]
DataThings
Moawad, Assaad [Auteur]
Security, Reliability and Trust Interdisciplibary Research Centre [S'nT]
DataThings
Rouvoy, Romain [Auteur]
Self-adaptation for distributed services and large software systems [SPIRALS]
Institut universitaire de France [IUF]
Le Traon, Yves [Auteur]
Computer Science and Communications Research Unit [Luxembourg] [CSC]

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 ...
Show more >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

Popular science :

Non

Collections :

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

Source :

Harvested from HAL