Various semantic technologies such as ontologies, machine learning, or artificial intelligence-based are being used today with data science for the purpose of explaining the meaning of data, and making this explanation exploitable by computer processing. Although some quick and brief reports do exist, to the best of our knowledge, the literature lacks a detailed study reporting why, when and how semantic technologies are used with data science. This paper is a theoretical review aiming at providing an insight into data science with semantic technologies. We characterize this research topic through a framework called DS2T helping to understand data science with semantic technologies and giving a comprehensive overview of the field through different, but complementary views. The proposed framework may be used to position research studies integrating semantic technologies with data science, compare them, understand new trends, and identify opportunities and open issues related to a given application domain. Software development processes are used as illustration domain.

In the context of Product lines, the collaborative configuration process gets complicated when the configuration decisions of involved stakeholders are contradictory, which may lead to conflicting situations. Although considerable research has been devoted to collaborative configuration, little attention has been paid to conflict resolution. Moreover, most of existing approaches rely on a systematic process which constraint decisions of some stakeholders. In this paper, we propose a new collaborative configuration approach which allows conflict resolution based on stakeholders preferences expressed through a set of substitution rules. Based on such preferences, we delete the minimal set of conflicting configuration decisions which are identified using the Minimal Correction Subsets (MCSs) computing algorithm. An illustrating example and a tool prototype are presented to evaluate the applicability of our approach.

COTS-Based Software Development has emerged as an approach aiming to improve a number of drawbacks found in the software development industry. The main idea is the reuse of well-tested software products, known as Commercial-Off-The-Shelf (COTS) components, that will be assembled together in order to develop larger systems. The potential benefits of this approach are mainly its reduced costs and shorter development time, while ensuring the quality. One of the most critical activities in COTS-based development is the identification of the COTS candidates to be integrated into the system under development. Nowadays, the Web is the most used means to find COTS candidates. Thus, the use of search engines turns out to be crucial. This paper deals with existing search engines especially proposed to find COTS components satisfying some needs on the Web. It presents a state of the art and practice of search engines followed by a study assessing to which extent they are able to accomplish their objectives.

Summary form only given. COTS-based development (CBD) is gaining ground on traditional development given the benefits it provides. Several methods are proposed to support one or more CBD process steps. However, in practice many problems are faced. These problems are mainly related to the lack of CBD environments, and the difficulty of choosing the appropriate method in each step and then properly applying it. In this paper, we propose a meta-model for the CBD process. To generate it, we use the map process meta-model which allows to specify decision-oriented processes. The proposed CBD meta-model has as intentions the main CBD process steps and as strategies the different types of existing methods allowing to achieve a process step. It exhibits a strategic level capturing knowledge about existing methods. It provides therefore choice guidance of the appropriate method according to the current situation. Besides, it allows the developer to construct his/her customized process depending on the application to develop, his/her preferences and experience with the methods. The use of this meta-model is illustrated through a case study.

COTS-based development (CBD) process consists in five main steps including identifying COTS products candidates, selecting the most appropriate one and assembling it with the other components. In practice, this process is not yet obvious to follow. Many methods are proposed to support one or several steps of the process. But, which method to use and how to apply it still remains problematic. The MAP is a process meta-model proposed to support methods specification and their enactment. We have already used the MAP to express the CBD process, and the MAP was also used to express PORE, a COTS selection method. In this paper, we propose to generalize the use of the MAP to express both the CBD process and COTS related methods, especially selection ones. As a result, the developer is guided in the process of choosing the appropriate method and its application.

Commercial off-the-shelf (COTS) components are more and more used in the new applications development. They promise to reduce cost and risks, and to ensure software higher quality. However, in practice, many problems are faced during the evaluation, selection and integration. Major causes of these problems are due to the lack of knowledge about COTS products, the ignorance of the available methods of evaluation, selection and integration, and the lack of guidance for choosing between these methods. Therefore, a COTS-based development environment (CEDE) turns out to be essential. In this perspective, we propose a survey of COTS characterizations and propose an extended model. The use of these attributes in a CEDE is specified by a scenario denoted by a multimodel process known as map formalism. The same development process is then illustrated with a map scenario focusing on the different types of selection/integration existing methods.

Speech is the most natural and widespread form of human communication. That is why speech synthesis has interested researchers for decades. It turns out that developing an unlimited text-to-speech system is an enormous task. The traditional methods (synthesis by rule and synthesis by concatenation of pre-recorded sounds) used for this have not given good results. In such a situation, neural networks (NNs) have the potential to give better results thanks to their property of interpolation and their capacity of generalisation. We present a synthesis system for the Arabic language. The choice of parameters which will be used to drive the NN is very important and have an effective influence on the quality of produced speech. Work has been done to evaluate different methods based on linear predictive coding; the resulting speech was machine-like and not intelligible. We suggest to use CELP to drive the NN, which provides high quality speech.

Text-to-speech synthesis is of great interest and its applications are several. For this reason, it has interested many researchers for decades. Two methods are usually used: synthesis by rule and synthesis by concatenation of pre-recorded sounds. But these methods have some disadvantages such as difficulty to be adapted to a new speaker or to a new language. Recently, neural networks (NN) have been used with nonconventional problems where a traditional solution seems impossible. Text-to-speech appears as one of these problems. In this field, it has been shown that NN don't work well when they are directly fed with speech samples. Therefore, works have been done to explore and evaluate different parametric forms of speech based on linear predictive coding (LPC), used for training, and found that LSP produced the best results. However, these methods don't take into account residual signal and speech produced was machine-like and not natural. We propose in this paper to drive the NN with codebook-excited linear prediction (CELP), which provides high quality speech, to perform Arabic speech synthesis.