Data Mining - Concepts and Techniques.

Data Mining Practical Machine Learning Tools and Techniques

The Object Management Group's (OMG) Model Driven Architecture (MDA) strategy envisages a world where models play a more direct role in software production, being amenable to manipulation and transformation by machine. Model Driven Engineering (MDE) is wider in scope than MDA. MDE combines process and analysis with architecture. This article sets out a framework for model driven engineering, which can be used as a point of reference for activity in this area. It proposes an organisation of the modelling 'space' and how to locate models in that space. It discusses different kinds of mappings between models. It explains why process and architecture are tightly connected. It discusses the importance and nature of tools. It identifies the need for defining families of languages and transformations, and for developing techniques for generating/configuring tools from such definitions. It concludes with a call to align metamodelling with formal language engineering techniques.

Model Driven Engineering

We review the use on ontologies for the integration of heterogeneous information sources. Based on an in-depth evaluation of existing approaches to this problem we discuss how ontologies are used to support the integration task. We evaluate and compare the languages used to represent the ontologies and the use of mappings between ontologies as well as to connect ontologies with information sources. We also ask for ontology engineering methods and tools used to develop ontologies for information integration. Based on the results of our analysis we summarize the state of the art in ontology-based information integration and name areas of further research activities.

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Ontology-Based Integration of Information — A Survey of Existing Approaches

This is a thought piece on data-intensive science requirements for databases and science centers. It argues that peta-scale datasets will be housed by science centers that provide substantial storage and processing for scientists who access the data via smart notebooks. Next-generation science instruments and simulations will generate these peta-scale datasets. The need to publish and share data and the need for generic analysis and visualization tools will finally create a convergence on common metadata standards. Database systems will be judged by their support of these metadata standards and by their ability to manage and access peta-scale datasets. The procedural stream-of-bytes-file-centric approach to data analysis is both too cumbersome and too serial for such large datasets. Non-procedural query and analysis of schematized self-describing data is both easier to use and allows much more parallelism.

Scientific Data Management in the Coming Decade

On the definition of generic multi-layered ontologies for urban applications

Hierarchy and centrality are two popular notions used to characterize the importance of entities in complex systems. Indeed, many complex systems exhibit a natural hierarchical structure, and centrality is a fundamental characteristic allowing to identify key constituents. Several measures based on various aspects of network topology have been proposed in order to quantify these concepts. While numerous studies have investigated whether centrality measures convey redundant information, how centrality and hierarchy measures are related is still an open issue. In this paper, we investigate the association between centrality and hierarchy using several correlation and similarity evaluation measures. A series of experiments is performed in order to evaluate the combinations of 6 centrality measures with 4 hierarchy measures across 28 diverse real-world networks with varying topological characteristics. Results show that network density and transitivity play a key role in shaping the redundancy between centrality and hierarchy measures.

Interplay Between Hierarchy and Centrality in Complex Networks

In this paper we show how the joint use of metamodeling and ontologies allows to describe domain knowledge for a complex domain. Ontologies are used as stabilized descriptions of a business domain while metamodels allow a fine description of the domain (to be constructed in the initial phases of modeling). We propose to use an ontology for early categorization, i.e., as a "natural" complement of the formal system that is induced by the metamodel.

Do we need metamodels AND ontologies for engineering platforms

The diversity of spatial information systems promotes the need to integrate heterogeneous spatial or geographic information systems (GIS) in a cooperative environment. The paper describes the research project ISIS (Interoperable Spatial Information System) which is a semantic mediation approach to support GIS interoperability. Its key characteristic is a dynamic resolution of semantic conflicts which is adequate for achieving autonomy, flexibility and extensibility. We propose a spatial OO data model and a mediation architecture based on multi-agent paradigm to support GIS interoperability.

ISIS: a semantic mediation model and an agent based architecture for GIS interoperability

Influential nodes play a critical role in boosting or curbing spreading phenomena in complex networks. Numerous centrality measures have been proposed for identifying and ranking the nodes according to their importance. Classical centrality measures rely on various local or global properties of the nodes. They do not take into account the network community structure. Recently, a growing number of researches have shifted to community-aware centrality measures. Indeed, it is a ubiquitous feature in a vast majority of real-world networks. In the literature, the focus is on designing community-aware centrality measures. However, up to now, there is no systematic evaluation of their effectiveness. This study fills this gap. It allows answering which community-aware centrality measure should be used in practical situations. We investigate seven influential community-aware centrality measures in an epidemic spreading process scenario using the Susceptible-Infected-Recovered (SIR) model on a set of fifteen real-world networks. Results show that generally, the correlation between community-aware centrality measures is low. Furthermore, in a multiple-spreader problem, when resources are available, targeting distant hubs using Modularity Vitality is more effective. However, with limited resources, diffusion expands better through bridges, especially in networks with a medium or strong community structure. 

Eric Leclercq

Papers

On the definition of generic multi-layered ontologies for urban applications

Interplay Between Hierarchy and Centrality in Complex Networks

Do we need metamodels AND ontologies for engineering platforms

ISIS: a semantic mediation model and an agent based architecture for GIS interoperability

Comparative evaluation of community-aware centrality measures