Matthew E. Tolentino

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Main memory in clusters may dominate total system power. The resulting energy consumption increases system operating cost and the heat produced reduces reliability. Emergent memory technology will provide servers with the ability to dynamically turn-on (online) and turn-off (offline) memory devices at runtime. This technology, coupled with slack in memory(More)
Main memory in many tera-scale systems requires tens of kilowatts of power. The resulting energy consumption increases system cost and the heat produced reduces reliability. Emergent memory technologies will provide systems the ability to dynamically turn-on (online) and turn-off (offline) memory devices at runtime. This technology, coupled with slack in(More)
—Rough set theory is a popular and powerful machine learning tool. It is especially suitable for dealing with information systems that exhibit inconsistencies, i.e. objects that have the same values for the conditional attributes but a different value for the decision attribute. In line with the emerging granular computing paradigm, rough set theory groups(More)
—Size and complexity of Big Data requires advances in machine learning algorithms to adequately learn from such data. While distributed shared-nothing architectures (Hadoop/Spark) are becoming increasingly popular to develop such new algorithms , it is quite challenging to adapt existing machine learning algorithms. In this paper, we propose a solution for(More)
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