A Framework for Semantic Grid in E-Science

A Framework for Semantic Grid in E-Science

Diego Liberati (Politecnico di Milano, Italy)
DOI: 10.4018/978-1-60566-184-1.ch023
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Abstract

A framework is proposed that creates, uses, and communicates information, whose organizational dynamics allows performing a distributed cooperative enterprise in public environments, even over open source systems. The approach assumes the web services as the enacting paradigm possibly over a grid, to formalize interactions as cooperative services on various computational nodes of a network. The illustrated case study shows that some portions, both of processes and of data or knowledge, can be shared in a collaborative environment, which is also more generally true for any kind of either complex or resource demanding (or both) interaction that will benefit any of the approaches.
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Background

Four main classes of methodological approaches to the experiments can be easily identified within the investigated field as follows:

  • Process Simulation and Visualization of the already available information sources. This is a very widely studied class, pertaining also to computational biochemistry among other disciplines, on which we will deliberately not speculate within this paper, but focus instead on the following three classes, on which more results have been recently contributed. It is mentioned on the human-computer interaction issues discussed by Liberati (2008a).

  • Supervised or Unsupervised Classification of observed events without inferring any correlation nor causality, such as in clustering (Garatti et al., 2007), and neural networks (Babiloni et al., 2000; Vercesi et al., 2000; Drago et al., 2002).

  • Machine Learning: Rule Generation (Muselli and Liberati, 2002) and Bayesian Networks (Bosin et al., 2006b) are able to select and link salient features involved variables in order to understand relationships (Liberati, 2008b) and to extract knowledge on the reliability, and possibly causal relationships among related cofactors [Paoli et al. 2000] via tools like logical networks (Muselli and Liberati, 2000) and Cart-models.

  • Identification of the Process Dynamics, through either ad hoc non linear models (Chignola et al., 2000), Piece-Wise Affine Identification (Ferrari-Trecate et al., 2003) of Hybrid dynamical-logical processes, or simplified linear (Baraldi et al., 2007) mass-action (De Nicolao et al., 2000; Sartorio et al., 2000; Sartorio et al., 2002) models (Liberati, 2008c) of the underlying Systems Biology (Sacco et al., 2007).

Key Terms in this Chapter

Neuroinformatics: The application of computer science to natural neuronal networks investigation.

Semantic Web: Information processing model in which computers can explicitly associate meanings or parse relationships between data without direct human intervention.

Bioinformatics: Application of computer science to huge biological problems.

Semantic Grid: A semantic web implemented over a Grid architecture.

Grid Computing: A style of computing that dynamically pools IT resources together in order to dynamically allocate resources depending on needs. It allows organizations to provision and scale resources as needs arise, thereby preventing their underutilization.

E-Science: A virtual way to build a scientific community over an information and communication technology infrastructure.

Service-Oriented Architecture: A kind of software design allowing a variety applications to interact regardless of specific technology like programming languages and operating systems.

Virtualization: It allows sharing of the same resources by multiple users as needs arise as if they were co-located even if they are not.

Systems Biology: The application of the systems and control theory to complex Biological problems.

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