International audienc

Microservices are hailed for their capabilities to tackle the challenge of breaking monolithic business systems down into small, cohesive, and loosely-coupled services. Indeed, these systems are neither easy to maintain nor to replace undermining organizations' efforts to cope with user's changing needs and governments' complex regulations. Microservices constitute an architectural style for developing a new generation of systems as a suite of services that, although they are separate, engage in collaborative execution and communication sessions. However, microservices success depends, among many other things, on the existence of an approach that would automatically identify the necessary microservices according to organizations' requirements. In this paper, we present such an approach and demonstrate its technical doability in the context of a case study, Bicing, for renting bikes. Some salient features of this approach are business processes as input for the identification needs, three models known as control, data, and semantic to capture dependencies between these processes' activities, and, finally, a collaborative clustering technique that recommends potential microservices. Conducted experiments in the context of Bicing clearly indicate that our approach outperforms similar ones for microservices identification and reinforce the important role of business processes in this identification. The approach constitutes a major milestone towards a better architectural style for future microservices systems.

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Purpose -This paper aims to detect opinion leaders, who they play a vital role as influencers of their community, which will help companies to improve their image in social media. This idea came with the fast development of social media, where individuals are increasingly sharing their personal experiences, opinions and critiques about products through these platforms. Thus, the new customers can rely on these spontaneous recommendations to proceed with the purchase without risk of disappointment. Therefore, the mismanagement of the e-reputation can cause huge losses for companies.Design/methodology/approach -In this study, a product reputation framework based on the prediction of opinion leaders is presented. To do so, opinion mining has been used to determine the product reputation in social media. In addition to posts processing, the profile information has also exploited to predict opinion leaders. To achieve the authors' goal, spammers and duplicated profiles have been detected to improve the product reputation results.Findings -The effectiveness of this approach has been tested using a social media simulation. The obtained results show that this approach is efficient and more accurate compared to the classical solutions.Originality/value -The key novelty is the gathering of spammer detection criteria with different weights and the profiles matching by providing the suitable matching methods that take into account the profile's attributes types. Consequently, a different similarity measure was assigned for each of the considered four attributes types. These two steps can ensure that the results obtained from social media are actually supported by opinions extracted directly from the real physical consumers.