Margarida Mamede

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In the era of global-scale services, big data analytical queries are often required to process datasets that span multiple data centers (DCs). In this setting, cross-DC bandwidth is often the scarcest, most volatile, and/or most expensive resource. However, current widely deployed big data analytics frameworks make no attempt to minimize the traffic(More)
We introduce a novel data structure for solving the range query problem in generic metric spaces. It can be seen as a dynamic version of the List of Clusters data structure of Chávez and Navarro. Experimental results show that, with respect to range queries, it outperforms the original data structure when the database dimension is below 12. Moreover, the(More)
We evaluate the performance of range queries in the Recursive List of Clusters (RLC) metric data structure, when the metric spaces are natural language dictionaries with the Levenshtein distance. The study compares RLC with five data structures (GNAT, H-Dsatl, LAESA, LC, and vp-trees) and comprises six dictionaries. The natural language dictionaries (in(More)