In this paper, we propose a method for the computation of a novel distance metrics, called Multi-Parameterized Edit Distance (MPED) among strings defined over heterogeneous alphabets. We show that the computation of MPED is hard and that several interesting application contexts can benefit from its application. We then present a novel implementation strategy based on an Evolutionary Heuristics, which we experimentally demonstrate to be efficient and effective for the problem at hand. Our approach paves indeed the way to the adoption of this new metric in all those contexts in which involved strings come from heterogeneous sources, each adopting its own alphabet.
High Performance Computation for the Multi-Parameterized Edit Distance / Cauteruccio, Francesco; Consalvo, Davide; Terracina, Giorgio. - (2018), pp. 567-574. (Intervento presentato al convegno 26th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing tenutosi a Cambridge, UK nel 21-23 March 2018).
High Performance Computation for the Multi-Parameterized Edit Distance
Francesco Cauteruccio;
2018-01-01
Abstract
In this paper, we propose a method for the computation of a novel distance metrics, called Multi-Parameterized Edit Distance (MPED) among strings defined over heterogeneous alphabets. We show that the computation of MPED is hard and that several interesting application contexts can benefit from its application. We then present a novel implementation strategy based on an Evolutionary Heuristics, which we experimentally demonstrate to be efficient and effective for the problem at hand. Our approach paves indeed the way to the adoption of this new metric in all those contexts in which involved strings come from heterogeneous sources, each adopting its own alphabet.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
