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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.
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On Modeling Dependency between MapReduce Configuration Parameters and Total Execution Time
TL;DR: An analytical method to model the dependency between configuration parameters and total execution time of Map-Reduce applications by multivariate linear regression is proposed.
Proceedings ArticleDOI
MapReduce Implementation of Prestack Kirchhoff Time Migration (PKTM) on Seismic Data
TL;DR: This paper gives an overview of forward/inverse Prestack Kirchhoff Time Migration algorithm, as one of the well-known seismic imaging algorithms, and proposes an approach to fit this algorithm for running on Google's MapReduce framework.
Posted Content
Privacy of Big Data in the Internet of Things Era.
TL;DR: Some of the main challenges of privacy in IoT, and opportunities for research and innovation are discussed; some of the ongoing research efforts that address IoT privacy issues are introduced.
Book ChapterDOI
Dimensionality Reduction for Intrusion Detection Systems in Multi-data Streams—A Review and Proposal of Unsupervised Feature Selection Scheme
TL;DR: Two basic models are provided: an Unsupervised Feature Selection to Improve Detection Accuracy for Anomaly Detection (UFSAD) and its extension ( UFSAD-MS) for multi streams, that could reduce the volume and the dimensionality of the big data resulting from the streams.
Journal ArticleDOI
A Lightweight Short-Term Photovoltaic Power Prediction for Edge Computing
TL;DR: This work proposes a unified training framework combined with the LightGBM algorithm to obtain a prediction model, which can provide short-term predictions of PV power output, and shows that the proposed framework is superior to other benchmark machine learning algorithms.