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The aim of this special issue on Advances on Managing ang Mining Large-Scale Time Dependent Graphs (TD-LSG) is to bring together active scholars and practitioners of dynamic graphs. Graph models and algorithms are ubiquitous of a large number of application domains, ranging from transportation to social networks, semantic web, or data mining. However, many applications require graph models that are time dependent. For example, applications related to urban mobility analysis employ a graph structure of the underlying road network. Indeed, the nature of such networks are spatiotemporal. Therefore, the time a moving object takes to cross a path segment typically depends on the starting instant of time. So, we call time-dependent graphs, the graphs that have this spatio-temporal feature.
In this special issue, we aim to discuss the problem of mining large-scale time-dependent graphs, since there are many real world applications deal with a large volumes of spatio-temporal data (e.g. moving objects trajectories). Managing and analyzing large-scale time-dependent graphs is very challenging since this requires sophisticated methods and techniques for creating, storing, accessing and processing such graphs in a distributed environment, because centralized approaches do not scale in a Big Data scenario.
Contributions will clearly point out answers to one of these challenges focusing on large-scale graphs.
Aims and Scope:
Many research questions related to mining large scale time-dependent graphs, will be at the heart of this special issue such as:
1. How to build a TD-LSG using spatio-temporal data or temporal traces in general, such as to favor the mining process ?
2. How to inter-link and enrich TD-LSG with semantic resources during the mining process ?
3. How to allow scalable mining tasks over a TD-LSG ?
4. How to organize and maintain a TD-LSG in distributed architecture, such as to scale the mining process ??
The special issue aims at bringing together scholars and practitioners active in dynamic graphs, to present their research, share their knowledge and experiences, and discuss the current state of the art and the future improvements.
Topics:
We encourage papers with important new insights and experiences on knowledge discovery aspects with dynamic and evolving graphs. Those contributions should shed light on one of the questions mentioned above, related to the knowledge discovery process. Topics of interest include, but are not limited to, the following inter-linked topics, with regards to mining process:
– Theoretical foundation of TD-LSG
– Construction and maintenance of TD-LSG
– Data quality in TD-LSG
– Data integration in TD-LSG
– Indexing techniques for TD-LSG
– Distributed algorithms & navigational query processing
– TD-LSG data mining: frequent pattern mining, similarity, cluster analysis, predictive learning
– Trajectory mining in TD-LSG
– Probabilistic TD-LSG
– Applications related to TD-LSG
Important dates:
Submission deadline :
November 31, 2018Acceptance deadline :
August 31, 2019Please select article type name of “
VSI: Large Evolving Graphs” during submission process.
Editors:– Sabeur Aridhi, LORIA, University of Lorraine, Nancy (France)
– Jose Fernandes de Macedo, Universidade Federale do Ceara, Fortaleza (Brazil)
– Engelbert Mephu Nguifo, LIMOS, University Clermont Auvergne (France)
– Karine Zeitouni, DAVID, Université de Versailles Saint-Quentin (France)
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Sabeur Aridhi – Associate Professor
University of Lorraine – TELECOM Nancy
LORIA/INRIA Nancy Grand Est 615 rue du Jardin Botanique,
54600 Villers-les-Nancy
Responsible for the Big data major (IAMD) at TELECOM Nancy