Archivio Istituzionale della RicercaIn this paper, we present an approximate algorithm that is able to quickly modify a large distributed k-nn graph by adding or removing nodes. The algorithm produces an approximate graph that is highly similar to the graph computed using a naïve approach, although it requires the computation of far fewer similarities. To achieve this goal, it relies on a novel, distributed graph based search procedure. All these algorithms are also experimentally evaluated, using both euclidean and non-euclidean datasets.
Fast distributed k-nn graph update / Debatty, Thibault; Michiardi, Pietro; Wees, Wim; Pulvirenti, Fabio. - ELETTRONICO. - Proceedings of IEEE International Conference on Big Data (Big Data), 2016:(2016), pp. 3308-3317. (Intervento presentato al convegno Third International Workshop on High Performance Big Graph Data Management, Analysis, and Mining (BigGraphs 2016) tenutosi a Washington (USA) nel 5 - 12 - 2016) [10.1109/BigData.2016.7840990].
Fast distributed k-nn graph update
PULVIRENTI, FABIO
2016
Abstract
In this paper, we present an approximate algorithm that is able to quickly modify a large distributed k-nn graph by adding or removing nodes. The algorithm produces an approximate graph that is highly similar to the graph computed using a naïve approach, although it requires the computation of far fewer similarities. To achieve this goal, it relies on a novel, distributed graph based search procedure. All these algorithms are also experimentally evaluated, using both euclidean and non-euclidean datasets.
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https://hdl.handle.net/11583/2658686
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