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Albert Y. Zomaya

Researcher at University of Sydney

Publications -  1020
Citations -  30827

Albert Y. Zomaya is an academic researcher from University of Sydney. The author has contributed to research in topics: Cloud computing & Scheduling (computing). The author has an hindex of 75, co-authored 946 publications receiving 24637 citations. Previous affiliations of Albert Y. Zomaya include University of Alabama & University of Sheffield.

Papers
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Journal ArticleDOI

Cybermatics: Advanced Strategy and Technology for Cyber-Enabled Systems and Applications

TL;DR: This special issue, aiming at enhancing the next generation of computational technologies, is introduced to provide both theoretical and algorithmic support across cyber-physical-social systems with cyber-enabled applications.
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A Fully Distributed Scheme for Discovery of Semantic Relationships

TL;DR: This paper proposes an effective strategy to discover semantic relationships over large-scale distributed networks based on a novel hierarchical knowledge abstraction and an efficient discovery protocol that will effectively facilitate the realization of the full potential of harnessing the collective power and utilization of the knowledge scattered over the Internet.
Proceedings ArticleDOI

A novel quartet-based method for phylogenetic inference

TL;DR: The experimental results show that the probability for the correct tree to be among a very small number of trees constructed using the method is very high, which opens a new research direction to further investigate more efficient algorithms for phylogenetic inference.
Proceedings ArticleDOI

An Intelligent Replication Framework for Data Grids

TL;DR: The main goal of the approach is to build a replica management service that integrates replica placement optimization mechanisms, and dynamic replication techniques, coupled with computation and job scheduling algorithms to provide better system performance in data grids.
Proceedings ArticleDOI

Clustering techniques for dynamic mobility management

TL;DR: This approach integrates the location area scheme and efficient clustering algorithms to find a network topology which can lead to massive savings in the number of signals made to locate users in the network.