Reference : Analyzing Complex Data in Motion at Scale with Temporal Graphs

	Document type :	Scientific congresses, symposiums and conference proceedings : Paper published in a book
	Discipline(s) :	Engineering, computing & technology : Computer science
	Focus Areas :	Computational Sciences
	To cite this reference:	http://hdl.handle.net/10993/31800

Title :	Analyzing Complex Data in Motion at Scale with Temporal Graphs
Language :	English
Author, co-author :	Hartmann, Thomas [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
	Fouquet, François [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
	Jimenez, Matthieu [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
	Rouvoy, Romain [University of Lille / Inria / IUF]
	Le Traon, Yves [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
Publication date :	Jul-2017
Main document title :	Proceedings of the 29th International Conference on Software Engineering and Knowledge Engineering
Peer reviewed :	Yes
Event name :	29th International Conference on Software Engineering and Knowledge Engineering
Event date :	05-07-2017 to-07-07-2017
Event place (city) :	Pittsburgh
Event country :	USA
Keywords :	[en] data analytics ; graph databases ; large-scale graphs ; time-evolving graphs
Abstract :	[en] Modern analytics solutions succeed to understand 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 pro- vide 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.
Permalink :	http://hdl.handle.net/10993/31800

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