Towards a Standards-Based Cloud Service Manager

Migrating services to the cloud brings all the benefits of elasticity, scalability and cost-cutting. However, migrating services among different cloud infrastructures or outside of the cloud is not an obvious task. In addition, distributing services among multiple cloud providers, or on a hybrid installation requires a custom implementation effort that must be repeated at each infrastructure change. This situation raises the lock-in problem and discourages cloud adoption. Cloud computing open standards were designed to face this situation and to bring interoperability and portability to cloud environments. However, they target isolated resources, and do not take into account the notion of complete services. In this paper, we introduce an extension to OCCI, a cloud computing open standard, in order to support complete service definition and management automation. We support this proposal with an open-source framework for service management through compliant cloud infrastructures.Peer reviewe

Graph BI & analytics: current state and future challenges

In an increasingly competitive market, making well-informed decisions requires the analysis of a wide range of heterogeneous, large and complex data. This paper focuses on the emerging field of graph warehousing. Graphs are widespread structures that yield a great expressive power. They are used for modeling highly complex and interconnected domains, and efficiently solving emerging big data application. This paper presents the current status and open challenges of graph BI and analytics, and motivates the need for new warehousing frameworks aware of the topological nature of graphs. We survey the topics of graph modeling, management, processing and analysis in graph warehouses. Then we conclude by discussing future research directions and positioning them within a unified architecture of a graph BI and analytics framework.Peer ReviewedPostprint (author's final draft

TopoGraph: an end-to-end framework to build and analyze graph cubes

Graphs are a fundamental structure that provides an intuitive abstraction for modeling and analyzing complex and highly interconnected data. Given the potential complexity of such data, some approaches proposed extending decision-support systems with multidimensional analysis capabilities over graphs. In this paper, we introduce TopoGraph, an end-to-end framwork for building and analyzing graph cubes. TopoGraph extends the existing graph cube models by defining new types of dimensions and measures and organizing them within a multidimensional space that guarantees multidimensional integrity constraints. This results in defining three new types of graph cubes: property graph cubes, topological graph cubes, and graph-structured cubes. Afterwards, we define the algebraic OLAP operations for such novel cubes. We implement and experimentally validate TopoGraph with different types of real-world datasets.Peer ReviewedPostprint (author's final draft

A new constraint-based compound graph layout algorithm for drawing biochemical networks

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.binfo:eu-repo/semantics/publishe

A new compound graph layout algorithm for visualizing biochemical networks

info:eu-repo/semantics/publishe

Semantic visualization of biochemical databases

SCOPUS: ar.kinfo:eu-repo/semantics/publishe

An analytics-aware conceptual model for evolving graphs

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.kinfo:eu-repo/semantics/publishedLadjel 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 Republi

Graph BI & analytics: current state and future challenges

In an increasingly competitive market, making well-informed decisions requires the analysis of a wide range of heterogeneous, large and complex data. This paper focuses on the emerging field of graph warehousing. Graphs are widespread structures that yield a great expressive power. They are used for modeling highly complex and interconnected domains, and efficiently solving emerging big data application. This paper presents the current status and open challenges of graph BI and analytics, and motivates the need for new warehousing frameworks aware of the topological nature of graphs. We survey the topics of graph modeling, management, processing and analysis in graph warehouses. Then we conclude by discussing future research directions and positioning them within a unified architecture of a graph BI and analytics framework.Peer Reviewe

EQS: An elastic and scalable message queue for the cloud

info:eu-repo/semantics/publishe

AROM: Processing big data with data flow graphs and functional programming

info:eu-repo/semantics/publishe