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Graphs are widespread structures providing a powerful abstraction for modeling networked data. Large and complex graphs have emerged in various domains such as social networks, bioinformatics, and chemical data. However, current warehousing frameworks are not equipped to handle efficiently the multidimensional modeling and analysis of complex graph data. In this paper, we propose a novel framework for building OLAP cubes from graph data and analyzing the graph topological properties. The framework supports the extraction and design of the candidate multidimensional spaces in property graphs. Besides property graphs, a new database model tailored for multidimensional modeling and enabling the exploration of additional candidate multidimensional spaces is introduced. We present novel techniques for OLAP aggregation of the graph, and discuss the case of dimension hierarchies in graphs. Furthermore, the architecture and the implementation of our graph warehousing framework are presented and show the effectiveness of our approach., SCOPUS: cp.k, info:eu-repo/semantics/published

Graphs are ubiquitous data structures commonly used to represent highly connected data. Many real-world applications, such as social and biological networks, are modeled as graphs. To answer the surge for graph data management, many graph database solutions were developed. These databases are commonly classified as NoSQL graph databases, and they provide better support for graph data management than their relational counterparts. However, each of these databases implement their own operational graph data model, which differ among the products. Further, there is no commonly agreed conceptual model for graph databases. In this paper, we introduce a novel conceptual model for graph databases. The aim of our model is to provide analysts with a set of simple, well-defined, and adaptable conceptual components to perform rich analysis tasks. These components take into account the evolving aspect of the graph. Our model is analytics-oriented, flexible and incremental, enabling analysis over evolving graph data. The proposed model provides a typing mechanism for the underlying graph, and formally defines the minimal set of data structures and operators needed to analyze the graph. © 2013 Springer-Verlag GmbH., SCOPUS: cp.k, info:eu-repo/semantics/published, Ladjel Bellatreche and Mukesh Mohania, editors.Proceedings of the 15th International Conference on Data Warehousing and KnowledgeDiscovery, DaWaK'13, number 8057 in Lecture Notes in Computer Science.Springer-Verlag, Prague, Czech Republic

info:eu-repo/semantics/published

info:eu-repo/semantics/published

Due to the huge amount of information available in biochemical databases, biologists need sophisticated tools to accurately extract the information from such databases and to interpret it correctly. Those tools must be able to dynamically generate any kind of biochemical subgraph (i.e. metabolic pathways, genetic regulation, signal transduction, etc.) in a single graph. The visualization tools must be able to cope with such graphs and to take into account the particular semantics of all kinds of biochemical subgraphs. Therefore, such tools need generic graph layout algorithms that adapt their behavior to the data semantics. In this chapter, we present the constrained compound graph layout (C2GL) algorithm designed for the generic representation of biochemical graphs and in which users can represent knowledge about how to draw graphs in accordance with the biochemical semantics. We show how we implemented the C2GL algorithm in the visual BioMaze framework, the visualization tool of the BioMaze project. © 2008, IGI Global., SCOPUS: ch.b, info:eu-repo/semantics/published

Due to the huge amount of information available in biochemical databases, biologists need sophisticated tools to accurately extract the information from such databases and to interpret it correctly. Those tools must be able to dynamically generate any kind of biochemical subgraph (i.e. metabolic pathways, genetic regulation, signal transduction, etc.) in a single graph. The visualization tools must be able to cope with such graphs and to take into account the particular semantics of all kinds of biochemical subgraphs. Therefore, such tools need generic graph layout algorithms that adapt their behavior to the data semantics. In this chapter, we present the constrained compound graph layout (C2GL) algorithm designed for the generic representation of biochemical graphs and in which users can represent knowledge about how to draw graphs in accordance with the biochemical semantics. We show how we implemented the C2GL algorithm in the visual BioMaze framework, the visualization tool of the BioMaze project. © 2008, IGI Global., SCOPUS: ch.b, info:eu-repo/semantics/published

info:eu-repo/semantics/published

info:eu-repo/semantics/published

SCOPUS: ar.k, info:eu-repo/semantics/published

SCOPUS: ar.k, info:eu-repo/semantics/published