Browsing by Author "Hammoudi, Slimane"

Now showing 1 - 4 of 4
  • Thumbnail Image
    Item
    An Approach to Improve Business Process Models Reuse Using LinkedIn Social Network
    (Springer International Publishing AG 2017, Editors: Rocha, Á., Correia, A.M., Adeli, H., Reis, L.P., Costanzo, S. (Eds.), 2017-04) Khider, Hadjer; Benna, Amel; Meziane, Abdelkrim; Hammoudi, Slimane
    Business process (BP) modeling is an important stage in Business Process Management (BPM) lifecycle. However, modeling BP from scratch is fallible task, complex, time-consuming and error prone task. One of the promising solutions to these issues is the reuse of BP models. BP reusability during the BP modeling stage can be very useful since it reduces time and errors modeling, simplify users’ modeling tasks, improve the quality of process models and enhance modeler’s efficiency. The main objective of this paper is to propose a Social BPM approach based on the user social profile to perform the reuse of BP models. We identify the need of exploring user profile to reuse BP models. The LinkedIn social network is used to extract the users’ business interests. These user business interests are then used to recommend the appropriate BP model.
  • No Thumbnail Available
    Item
    Social Business Process Model Recommender: An MDE approach
    (CERIST, 2018-09-26) Khider, Hadjer; Hammoudi, Slimane; Benna, Amel; Meziane, Abdelkrim
    with the advent of the social Web (Web 2.0) and the massive use of online social networks (OSNs) (e.g.Facebook, LinkedIn). OSNs have become new opportunity that provides huge Masses of data about users’, rich in their diversity and important in their quantity. Exploring the profiles data among these OSNs attract a great deal of attention among researchers in several research areas: social information retrieval systems, social recommendation systems. Social Recommender Systems aim to generate meaningful recommendations to a collection of users for items that might be interesting for them. In this paper we propose to investigate social recommender systems for improving Business process (BP) models reuse in process models repositories. The recommender system we propose to integrate we called SBPR recommender. SBPR recommender aims to recommend to the users of such repositories BP models for reuse. LinkedIn User profile is the source of social data for SBPR recommender; BP models are target items to be recommended to user. We propose a framework based on Model Driven Engineering (MDE) approach where techniques of models, metamodels, transformation and weaving are used to implement a generic recommendation process.
  • Thumbnail Image
    Item
    Toward an Approach to Improve Business Process Models Reuse Based on Linkedin Social Network
    (CERIST, 2017-01-10) Khider, Hadjer; Benna, Amel; Meziane, Abdelkrim; Hammoudi, Slimane
    Business process (BP) modeling is an important stage in BPM lifecycle. However, modeling BP from scratch is fallible task, complex, time-consuming and error prone task. One of the promising solutions to these issues is the reuse of BP models. BP reusability during the BP modeling stage can be very useful since it reduces time and errors modeling, simplify users’ modeling tasks, improve the quality of process models and enhance modeler’s efficiency. The main objective of this paper is to propose a Social Business Process Management (Social BPM) approach based on the user social profile to perform the reuse of BP models. We identify the need of exploring user profile to reuse BP models. The LinkedIn social network is used to extract the users’ business interests these user business interests are then used to recommend the appropriate BP model.
  • Thumbnail Image
    Item
    Towards Big Data Analytics over Mobile User Data using Machine Learning
    (IEEE, 2023-01) Ichou, Sabrina; Hammoudi, Slimane; Cuzzocrea, Alfredo; Meziane, Abdelkrim; Benna, Amel
    Machine Learning (ML) is a science that forces computers to learn and behave like humans. As these systems interact with data, networks, and people, they automatically become smarter so that they can eventually solve or predict a practical issue in the world for us. The use of ML can be a giant leap for cannot simply be integrated as the top layer. This requires redefining workflow, architecture, data collection and storage, analytics, and other modules. This paper aims to discuss the issue of machine learning technique for analysis data of mobile user. First, we identified the machine learning benefits and drawbacks, challenges, advantages of using Machine Learning. Then, we propose a generic model of analytic mobile user data using ML, the model is centered on the machine learning component, which interacts with two other components, including mobile user data, and system. The interactions go in both directions. For instance, mobile user data serves as inputs to the learning component and the latter generates outputs; system architecture has impact on how learning algorithms should run and how efficient it is to run them, and simultaneously meeting. Mobile user data goes through several stages: prepossessing which includes the steps we need to follow to transform or encode the data so that it can be easily analyzed by the machine. Then, modelling in this step we will be clustering and classification the data obtained. Finally, evaluation, various measures of performance, accuracy, recall, precision, and F-measure were used to analyze the results of the naive Bayes, SVM, and K-nearest neighbor classification algorithms.