Horacio González-Vélez

KER Journal 2015 Journal Article

Distributed aggregation of heterogeneous Web-based Fine Art Information: enabling multi-source accessibility and curation

• Frances Buchanan
• Niccolo Capanni
• Horacio González-Vélez

Abstract The sources of information on the Web relating to Fine Art and in particular to Fine Artists are numerous, heterogeneous and distributed. Data relating to the biographies of an artist, images of their artworks, location of the artworks and exhibition reviews invariably reside in distinct and seemingly unrelated, or at least unlinked, sources. While communication and exchange exists, there is a great deal of independence between major repositories, such as museum, often owing to their ownership or heritage. This increases the individuality in the repository’s own processes and dissemination. It is currently necessary to browse through numerous different websites to obtain information about any one artist, and at this time there is little aggregation of Fine Art Information. This is in contrast to the domain of books and music, where the aggregation and re-grouping of information (usually by author or artist/band name) has become the norm. A Museum API (Application Programming Interface), however, is a tool that can facilitate a similar information service for the domain of Fine Art, by allowing the retrieval and aggregation of Web-based Fine Art Information, whilst at the same time increasing public access to the content of a museum’s collection. In this paper, we present the case for a pragmatic solution to the problems of heterogeneity and distribution of Fine Art Data and this is the first step towards the comprehensive re-presentation of Fine Art Information in a more ‘artist-centric’ way, via accessible Web applications. This paper examines the domain of Fine Art Information on the Web, putting forward the case for more Web services such as generic Museum APIs, highlighting this via a prototype Web application known as the ArtBridge. The generic Museum API is the standardisation mechanism to enable interfacing with specific Museum APIs.

KER Journal 2015 Journal Article

Intelligent computing in large-scale systems

• Joanna Kołodziej
• Horacio González-Vélez
• Fatos Xhafa
• Leonard Barolli

Abstract Intelligent computing in large-scale systems provides systematic methodologies and tools for building complex inferential systems, which are able to adapt, mine data sets, evolve, and act in a nimble manner within major distributed environments with diverse architectures featuring multiple cores, accelerators, and high-speed networks. We believe that the papers presented in this special issue ought to serve as a reference for students, researchers, and industry practitioners interested in the evolving, interdisciplinary area of intelligent computing in large-scale systems. We very much hope that readers will find in this compendium new inspiration and ideas to enhance their own research.

KER Journal 2011 Journal Article

A knowledge-rich distributed decision support framework: a case study for brain tumour diagnosis

• David Dupplaw
• Madalina Croitoru
• Srinandan Dasmahapatra
• Alex Gibb
• Horacio González-Vélez
• Miguel Lurgi
• Bo Hu
• Paul Lewis

Abstract The HealthAgents project aims to provide a decision support system for brain tumour diagnosis using a collaborative network of distributed agents. The goal is that through the aggregation of the small data sets available at individual hospitals, much better decision support classifiers can be created and made available to the hospitals taking part. In this paper, we describe the technicalities of the HealthAgents framework, in particular how the interoperability of the various agents is managed using semantic web technologies. On the broad scale the architecture is based around distributed data-mart agents that provide ontological access to hospitals’ underlying data that has been anonymized and processed from proprietary formats into a canonical format. Classifier producers have agents that gather the global data from participating hospitals such that classifiers can be created and deployed as agents. The design on a microscale has each agent built upon a generic-layered framework that provides the common agent program code, allowing rapid development of agents for the system. We believe that our framework provides a well-engineered, agent-based approach to data sharing in a medical context. It can provide a better basis on which to investigate the effectiveness of new classification techniques for brain tumour diagnosis.

KER Journal 2011 Journal Article

Guest editorial preface: Computational intelligence for neuro-oncological diagnosis

• Horacio González-Vélez

KER Journal 2011 Journal Article

The design and implementation of a novel security model for HealthAgents

• Liang Xiao
• Srinandan Dasmahapatra
• Paul Lewis
• Bo Hu
• Andrew Peet
• Alex Gibb
• David Dupplaw
• Madalina Croitoru

Abstract In this paper, we analyze the special security requirements for software support in health care and the HealthAgents system in particular. Our security solution consists of a link-anonymized data scheme, a secure data transportation service, a secure data sharing and collection service, and a more advanced access control mechanism. The novel security service architecture, as part of the integrated system architecture, provides a secure health-care infrastructure for HealthAgents and can be easily adapted for other health-care applications.