A
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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Proceedings ArticleDOI
Model-Driven Development of Wireless Sensor Network Applications
Taniro Rodrigues,Priscilla Dantas,Fl´via C. Delicato,Paulo F. Pires,Luci Pirmez,Thais Batista,Claudio Miceli,Albert Y. Zomaya +7 more
TL;DR: This work proposes a model-driven approach to build WSN applications that promotes the separation of concerns between two levels of requirements involved, and promotes the reuse of software artifacts.
Book
Biological Knowledge Discovery Handbook: Preprocessing, Mining and Postprocessing of Biological Data
Mourad Elloumi,Albert Y. Zomaya +1 more
TL;DR: This book presents a vast overview of the most recent developments on techniques and approaches in the field of biological knowledge discovery and data mining, providing in-depth fundamental and technical field information on the most important topics encountered.
Proceedings ArticleDOI
Handling Uncertainty: Pareto-Efficient BoT Scheduling on Hybrid Clouds
M. Reza HoseinyFarahabady,Hamid R. Dehghani Samani,Luke M. Leslie,Young Choon Lee,Albert Y. Zomaya +4 more
TL;DR: The aim in this paper is to find Pareto-optimal schedules for large-scale Bag-of-Tasks (BoT) applications that meet user defined constraints, such as deadline or budget or some tradeoff between them.
Journal ArticleDOI
Software Tools and Techniques for Big Data Computing in Healthcare Clouds
TL;DR: The exponential growth in the size of the aforementioned health related raw data sets has widened this integration gap, which is severely limiting the potential benefits of having large datasets and HIS/CDSS for medical decision-making processes.
Book ChapterDOI
Function Optimization with Coevolutionary Algorithms
TL;DR: It is shown that both coevolutionary algorithms outperform a sequential GA and may compete to outperform each other in some specific test optimization problems.