Surprises in approximating Levenshtein distances

Baake, Michael; Grimm, Uwe; Giegerich, Robert

doi:10.1016/j.jtbi.2006.06.026

Cited by 5 publications

(1 citation statement)

References 9 publications

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“…We have considered several distances of similarities: distance from Levenshtein, Jaro, distance, distance from Jaccard and distance from JaroWincler. In our approach, we have performed a comparison between the results of these distances (Baake et al, 2006;Raymond, 2005). We have opted for the JaroWincler distance, which provides the best results.…”

Section: Lexical Similaritymentioning

confidence: 99%

Data Cubes Retrieval and Design in OLAP Systems: From Query Analysis to Visualisation Tool

Djiroun

Boukhalfa

Alimazighi

2018

IJBIDM

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Business intelligence systems provide an effective solution from large volumes of data for multidimensional online computing and analysis. Usually, in a decision-making process, organisations and enterprises, require several internal and/or external cubes which are often heterogeneous. Most of the time, the structure of these cubes is unknown to the decision-makers. To analyse a phenomenon, the decision-maker seeks among sets of cubes, in a collection, the cube which responds better to his need. In this context, we propose an approach that enables decision-makers to express their needs via a query expressed in a natural language, returns top-K relevant cubes and designs/constructs new cubes when no, or few deployed cubes are relevant. We propose a tool called RD-cubes-query implementing our approach in a ROLAP architecture. We use this tool in some experiments to validate our approach.

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Section: Lexical Similaritymentioning

confidence: 99%