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

Autor: Yves Le Traon, Assaad Moawad, Francois Fouquet, Romain Rouvoy, Thomas Hartmann
Přispěvatelé: DataThings, Interdisciplinary Centre for Security, Reliability and Trust [Luxembourg] (SnT), Université du Luxembourg (Uni.lu), Security, Reliability and Trust Interdisciplibary Research Centre (S'nT), Self-adaptation for distributed services and large software systems (SPIRALS), Inria Lille - Nord Europe, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 (CRIStAL), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS), Institut Universitaire de France (IUF), Ministère de l'Education nationale, de l’Enseignement supérieur et de la Recherche (M.E.N.E.S.R.), Computer Science and Communications Research Unit [Luxembourg] (CSC), Laboratory of Advanced Software SYstems [Luxembourg] (LASSY), Université du Luxembourg (Uni.lu)-Université du Luxembourg (Uni.lu), FuzzMe
Jazyk: angličtina
Rok vydání: 2019
Předmět:
FOS: Computer and information sciences
Computer science
[INFO.INFO-DS]Computer Science [cs]/Data Structures and Algorithms [cs.DS]
what-if analysis
02 engineering and technology
[INFO.INFO-SE]Computer Science [cs]/Software Engineering [cs.SE]
Data modeling
Computer Science - Databases
020204 information systems
0202 electrical engineering
electronic engineering
information engineering
Prescriptive analytics
[INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB]
business.industry
Databases (cs.DB)
Predictive analytics
Data science
graph processing
predictive analytics
Hardware and Architecture
Analytics
Scalability
Data analysis
020201 artificial intelligence & image processing
Fork (file system)
[INFO.INFO-OS]Computer Science [cs]/Operating Systems [cs.OS]
time-evolving graphs
business
Software
Information Systems
Zdroj: Information Systems
Information Systems, Elsevier, In press, 83, pp.101-117. ⟨10.1016/j.is.2019.03.004⟩
Information Systems, In press, 83, pp.101-117. ⟨10.1016/j.is.2019.03.004⟩
ISSN: 0306-4379
DOI: 10.1016/j.is.2019.03.004⟩
Popis: International audience; 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.
Databáze: OpenAIRE
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