432 research outputs found
A semi-automatic data integration process of heterogeneous databases
No full textOne of the most difficult issues today, is the integration of data from various sources. Thus, it arises the need of automatic Data Integration (DI) methods. However, in the literature there are fully automatic or semi-automatic DI techniques, but they require the involvement of IT-experts with specific domain skills. In this paper we present a novel DI methodology for which it is not required the involvement of IT-experts; in this methodology syntactically/semantically similar entities present in the sources are merged, by exploiting an information retrieval technique, a clustering method and a trained neural net-work. Although the suggested process is completely automated, we planned some interactions with the Company Manager, a figure who is not required to have IT-skills, but whose only contribution will be to define limits and tolerance thresholds during the DI process, based on the interests of the company. The validity of the proposed approach showed an integration accuracy between 99% - 100% .(c) 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/
METAMORPHOS: MEthods and Tools for migrAting software systeMs towards web and service Oriented aRchitectures: exPerimental evaluation, usability, and tecHnOlogy tranSFer
No full textUsing fold-in and fold-out in the architecture recovery of software systems
No full textIn this paperwe present an approach to automate the architecture recovery process of software systems. The approach is built on information retrieval and clustering techniques, and, in particular, uses Latent Semantic Indexing (LSI) to get similarities among software entities (e.g., programs or classes) and the k-means clustering algorithm to form groups of software entities that implement similar functionality. In order to improve computational time in the context of the software evolution and then reduce energy waste, the architecture recovery process can be also applied by using fold-in and fold-out mechanisms that, respectively, add and remove software entities to the LSI representation of the understudy software system. The approach has been implemented in a prototype of a supporting software system as an Eclipse plug-in. Finally, to assess the approach and the plug-in, we have conducted an empirical investigation on five open source software systems implemented using the programming languages Java and C/C++. In the investigation special emphasis has been also given to the effect of using the fold-in and fold-out mechanisms
Comparison of Multi-objective Evolutionary Algorithms for prototype selection in nearest neighbor classification
No full textThe nearest neighbor classifiers are popular supervised classifiers due to their ease of use and good performance. However, in spite of their success, they suffer from some defects such as high storage requirements, high computational complexity, and low noise tolerance. In order to address these drawbacks, prototype selection has been studied as a technique to reduce the size of training datasets without deprecating the classification accuracy. Due to the need of achieving a trade-off between accuracy and reduction, Multi-Objective Evolutionary Algorithms (MOEAs) are emerging as methods efficient in solving the prototype selection problem. The goal of this paper is to perform a systematic comparison among well-known MOEAs in order to study their effects in solving this problem. The comparison involves the study of MOEAs' performance in terms of the well-known measures such as hypervolume, Δ index and coverage of two sets. The empirical analysis of the experimental results is validated through a statistical multiple comparison procedure
Source-Code Comprehension Tasks Supported by UML Design Models: Results from a Controlled Experiment and a Differentiated Replication
No full textApplying SPEA2 to prototype selection for nearest neighbor classification
No full textThe k-nearest neighbor (k-NN) algorithm is one of the most well-known supervised classifiers due to its ease of use and good performance. However, in spite of its popularity, k-NN suffers from some drawbacks such as high computational complexity, high storage requirements, and low noise tolerance. Prototype selection is a successful technique aimed at addressing aforementioned issues by reducing the size of training datasets without deprecating, but improving, the classification accuracy. Recently, evolutionary algorithms have been successfully applied to the optimisation of accuracy and size of reduction of prototype selection because of their innate exploration and exploitation capabilities in visiting the space of solutions of a problem. However, so far, all the evolutionary approaches for prototype selection are based on a so-called multi-objective 'a priori' technique, where multiple objectives are aggregated together into a single objective through a weighted combination. This paper proposes to apply, for the first time, an 'a posteriori' algorithm, namely SPEA2, to prototype selection problem in order to explicitly deal with both objectives and offer a better trade-off between classification and reduction performance. As shown in the experimental section, the application of SPEA2 allows to hold high accuracy in nearest neighbour classification with a significant reduction of training data thanks to the discovery of higher quality solutions than those detected by a conventional 'a priori' approach. © 2016 IEEE
- …
