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Prof. Sadok BEN YAHIA
Department of Software Science,Tallinn University of Technology Akadeemia tee 15a, room 649, Tallinn, Estonia

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Special issue article
Published: 17 August 2021 in Computing
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Suicide has become a serious social health issue in modern society. Suicidal ideation is people’s thoughts about committing or planning suicide. Many factors, such as long-term exposure to negative feelings or life events, can lead to suicidal ideation and suicide attempts. Among these approaches to suicide prevention, early detection of suicidal ideation is one of the most effective ways. Using social networking services provides a platform for people to express their sufferings and feelings in the real world, which provides a source for a deeper investigation into models and approaches for the detection of suicidal intent to enable prevention. This paper addresses the early detection of suicide ideation through the associative classification approach applied to Twitter social media. However, since the number of suicide intention tweets is tiny compared to the number of all the tweets, this leads us to an imbalanced classification problem, in which, the minority class (suicide intention) is more important than the majority class (no suicide intention). In such a situation, classical classifiers usually yield very inaccurate results regarding minor classes, since they can easily discover rules predicting the majority class and overlook those related to the minor. This paper aims to contribute to this line of research by introducing a new interestingness measure to enhance the classification process. This measure highlights the two classes regardless of their imbalanced distribution. Carried out experiments proved that the adapted CBA outweighs in terms of prediction accuracy the original one, and other pioneering baseline classification approaches.

ACS Style

Mohamed Ali Ben Hassine; Safa Abdellatif; Sadok Ben Yahia. A novel imbalanced data classification approach for suicidal ideation detection on social media. Computing 2021, 1 -25.

AMA Style

Mohamed Ali Ben Hassine, Safa Abdellatif, Sadok Ben Yahia. A novel imbalanced data classification approach for suicidal ideation detection on social media. Computing. 2021; ():1-25.

Chicago/Turabian Style

Mohamed Ali Ben Hassine; Safa Abdellatif; Sadok Ben Yahia. 2021. "A novel imbalanced data classification approach for suicidal ideation detection on social media." Computing , no. : 1-25.

Micro article
Published: 23 July 2021 in MethodsX
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Despite the intense research activity in the last two decades, ontology integration still presents a number of challenging issues. As ontologies are continuously growing in number, complexity and size and are adopted within open distributed systems such as the Semantic Web, integration becomes a central problem and has to be addressed in a context of increasing scale and heterogeneity. In this paper, we describe a holistic alignment-based method for customized ontology integration. The holistic approach proposes additional challenges as multiple ontologies are jointly integrated at once, in contrast to most common approaches that perform an incremental pairwise ontology integration. By applying consolidated techniques for ontology matching, we investigate the impact on the resulting ontology. The proposed method takes multiple ontologies as well as pairwise alignments and returns a refactored/non-refactored integrated ontology that faithfully preserves the original knowledge of the input ontologies and alignments. We have tested the method on large biomedical ontologies from the LargeBio OAEI track. Results show effectiveness, and overall, a decreased integration cost over multiple ontologies.

ACS Style

Inès Osman; Salvatore Flavio Pileggi; Sadok Ben Yahia; Gayo Diallo. An Alignment-Based Implementation of a Holistic Ontology Integration Method. MethodsX 2021, 8, 101460 .

AMA Style

Inès Osman, Salvatore Flavio Pileggi, Sadok Ben Yahia, Gayo Diallo. An Alignment-Based Implementation of a Holistic Ontology Integration Method. MethodsX. 2021; 8 ():101460.

Chicago/Turabian Style

Inès Osman; Salvatore Flavio Pileggi; Sadok Ben Yahia; Gayo Diallo. 2021. "An Alignment-Based Implementation of a Holistic Ontology Integration Method." MethodsX 8, no. : 101460.

Review article
Published: 22 June 2021 in SN Computer Science
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In data mining, the classical association rule mining techniques deal with binary attributes; however, real-world data have a variety of attributes (numerical, categorical, Boolean). To deal with the variety of data attributes, the classical association rule mining technique was extended to numerical association rule mining. Initially, the concept of numerical association rule mining started with the discretization method, and later, many other methods, e.g., optimization, distribution are proposed in state-of-the-art. Different authors have presented various algorithms for each numerical association rule mining method; therefore, it is hard to select a suitable algorithm for a numerical association rule mining task. In this article, we present a systematic assessment of various numerical association rule mining methods and we provide a meta-study of thirty numerical association rule mining algorithms. We investigate how far the discretization techniques have been used in the numerical association rule mining methods.

ACS Style

Minakshi Kaushik; Rahul Sharma; Sijo Arakkal Peious; Mahtab Shahin; Sadok Ben Yahia; Dirk Draheim. A Systematic Assessment of Numerical Association Rule Mining Methods. SN Computer Science 2021, 2, 1 -13.

AMA Style

Minakshi Kaushik, Rahul Sharma, Sijo Arakkal Peious, Mahtab Shahin, Sadok Ben Yahia, Dirk Draheim. A Systematic Assessment of Numerical Association Rule Mining Methods. SN Computer Science. 2021; 2 (5):1-13.

Chicago/Turabian Style

Minakshi Kaushik; Rahul Sharma; Sijo Arakkal Peious; Mahtab Shahin; Sadok Ben Yahia; Dirk Draheim. 2021. "A Systematic Assessment of Numerical Association Rule Mining Methods." SN Computer Science 2, no. 5: 1-13.

Journal article
Published: 16 June 2021 in Expert Systems with Applications
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Context-aware recommender systems have received considerable attention from industry and academic areas. In this paper, we pay heed to the growing interest in integrating context-awareness and multi-criteria decision making in recommender systems, to deal with the most pressing challenges in music recommender systems, namely the diversity of the recommended playlist, the scalability of the system, and the cold start problem. This paper introduces a new multi-criteria recommendation approach, named MORec, which generates Top-N music recommendations by bootstrapping the system using beforehand collected data. We usher by gauging the relevance of contextual information from the relation between three elements: user, music genre, and the user’s context. Then, we apply an aggregation technique to uncover the relationship between the context and the overall rating. Besides, we apply the K-means algorithm to generate a predictive model that comprises clusters of similar contexts defining the association between contextual dimensions and music genres. Carried out experiments emphasize very promising results of our approach in terms of clustering quality, compared to the Partitioning Around Medoids algorithm in terms of connectivity and stability. The comparison versus pioneering recommendation baselines underscored the effectiveness of MORec in terms of recommendation quality and usefulness.

ACS Style

Imen Ben Sassi; Sadok Ben Yahia; Innar Liiv. MORec: At the crossroads of context-aware and multi-criteria decision making for online music recommendation. Expert Systems with Applications 2021, 183, 115375 .

AMA Style

Imen Ben Sassi, Sadok Ben Yahia, Innar Liiv. MORec: At the crossroads of context-aware and multi-criteria decision making for online music recommendation. Expert Systems with Applications. 2021; 183 ():115375.

Chicago/Turabian Style

Imen Ben Sassi; Sadok Ben Yahia; Innar Liiv. 2021. "MORec: At the crossroads of context-aware and multi-criteria decision making for online music recommendation." Expert Systems with Applications 183, no. : 115375.

Journal article
Published: 01 June 2021 in Expert Systems with Applications
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Contextual information plays a key role in Context-Aware Recommender Systems (CARS). The rating prediction in CARS focuses on improving recommendation accuracy attempting to form a personalized information recommendation for users. Three key problems that affect the performances of recommender systems: (i) context condition’s selection; (ii) context condition’s weighting; and (iii) users’ context conditions matching. Context-aware approaches have the assumption that all context conditions have the same weight. These approaches ignore that users have different preferences in different contexts. To address these three problems, we introduce a novel approach for Selecting, Weighting Context Conditions (SWCC) and measuring semantic similarity between users’ situations. Evaluation experiments show that the proposed approach is outperforming the pioneering context-aware recommendation approaches of the literature.

ACS Style

Saloua Zammali; Sadok Ben Yahia. How to select and weight context dimensions conditions for context-aware recommendation? Expert Systems with Applications 2021, 182, 115176 .

AMA Style

Saloua Zammali, Sadok Ben Yahia. How to select and weight context dimensions conditions for context-aware recommendation? Expert Systems with Applications. 2021; 182 ():115176.

Chicago/Turabian Style

Saloua Zammali; Sadok Ben Yahia. 2021. "How to select and weight context dimensions conditions for context-aware recommendation?" Expert Systems with Applications 182, no. : 115176.

Conference paper
Published: 08 May 2021 in Business Information Systems
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Accurate stock market prediction is of paramount importance for traders. Professional ones typically derive financial market decision-making from fundamental and technical indicators. However, stock markets are very often influenced by external human factors, like sentiment information that can be contained in online social networks. As a result, micro-blogs are more and more exploited to predict prices and traded volumes of stocks in financial markets. Nevertheless, it has been shown that a large volume of the content shared on micro-blogs is published by malicious entities, especially spambots. In this paper, we introduce a novel deep learning-based approach for financial time series forecasting based on social media. Through the Generative Adversarial Network (GAN) model, we gauge the impact of malicious tweets, posted by spambots, on financial markets, mainly the closing price. We compute the performance of the proposed approach using real-world data of stock prices and tweets related to the Facebook Inc company. Carried out experiments show that the proposed approach outperforms the two baselines, LSTM, and SVR, using different evaluation metrics. In addition, the obtained results prove that spambot tweets potentially grasp investors’ attention and induce the decision to buy and sell.

ACS Style

Tatsuki Ishikawa; Imen Ben Sassi; Sadok Ben Yahia. Assessment of Malicious Tweets Impact on Stock Market Prices. Business Information Systems 2021, 330 -346.

AMA Style

Tatsuki Ishikawa, Imen Ben Sassi, Sadok Ben Yahia. Assessment of Malicious Tweets Impact on Stock Market Prices. Business Information Systems. 2021; ():330-346.

Chicago/Turabian Style

Tatsuki Ishikawa; Imen Ben Sassi; Sadok Ben Yahia. 2021. "Assessment of Malicious Tweets Impact on Stock Market Prices." Business Information Systems , no. : 330-346.

Short review
Published: 23 January 2021 in Information Fusion
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In recent years, the decentralized development of ontologies has led to the generation of multiple ontologies of overlapping knowledge. This heterogeneity problem can be tackled by integrating existing ontologies to build a single coherent one. Ontology integration has been investigated during the last two decades, but it is still a challenging task. In this article, we provide a comprehensive survey of all ontology integration aspects. We discuss related notions and scrutinize existing techniques and literature approaches. We also detail the role of ontology matching in the ontology integration process. Indeed, the ontology community has adopted the splitting of the ontology integration problem into matching, merging and repairing sub-tasks, where matching is a necessary preceding step for merging, and repairing can be included in the matching process or performed separately. Ontology matching and merging systems have become quite proficient, however the trickiest part lies in the repairing step. We also focus on the case of a holistic integration of multiple heterogeneous ontologies, which needs further exploration. Finally, we investigate challenges, open issues, and future directions of the ontology integration and matching areas.

ACS Style

Inès Osman; Sadok Ben Yahia; Gayo Diallo. Ontology Integration: Approaches and Challenging Issues. Information Fusion 2021, 71, 38 -63.

AMA Style

Inès Osman, Sadok Ben Yahia, Gayo Diallo. Ontology Integration: Approaches and Challenging Issues. Information Fusion. 2021; 71 ():38-63.

Chicago/Turabian Style

Inès Osman; Sadok Ben Yahia; Gayo Diallo. 2021. "Ontology Integration: Approaches and Challenging Issues." Information Fusion 71, no. : 38-63.

Regular paper
Published: 21 November 2020 in Multimedia Systems
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To simplify effective music filtering, recommender systems (RS) have received great attention from both industry and academia area. To select which music to recommend, traditional RS uses an approximation of users’ real interests. However, while discarding users’ contexts, profiles information is not able to reflect their exact needs and to provide overpowering recommendations. One of the main issues that have to be considered before the conception of context-aware recommender systems (CARS) is the estimation of the relevance of contextual information. The use of irrelevant or superfluous contextual factors can generate serious problems about the complexity and the quality of recommendations. In this paper, we introduce a multi-dimensional context model for music CARS. We started by the acquisition of explicit items rating from a population in various possible contextual situations. Thus, we proposed a user-based methodology aiming to judge the relation between contextual factors and musical genres. Next, we applied the Multiple Linear Regression technique on users’ perceived ratings, to define an order of importance between contextual dimensions. We described raw collected data with basic statistics about the created dataset. We also summarized the key results and discussed key findings. Finally, we propose a new framework for Music CARS.

ACS Style

Imen Ben Sassi; Sadok Ben Yahia. How does context influence music preferences: a user-based study of the effects of contextual information on users’ preferred music. Multimedia Systems 2020, 27, 143 -160.

AMA Style

Imen Ben Sassi, Sadok Ben Yahia. How does context influence music preferences: a user-based study of the effects of contextual information on users’ preferred music. Multimedia Systems. 2020; 27 (2):143-160.

Chicago/Turabian Style

Imen Ben Sassi; Sadok Ben Yahia. 2020. "How does context influence music preferences: a user-based study of the effects of contextual information on users’ preferred music." Multimedia Systems 27, no. 2: 143-160.

Conference paper
Published: 19 November 2020 in Communications in Computer and Information Science
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In association rule mining, both the classical algorithms and today’s available tools either use binary data items or discretized data. However, in real-world scenarios, data are available in many different forms (numerical, text) and these types of data items are not supported in the classical association rule mining algorithms. There are some association rule mining algorithms that have been proposed for numerical data items but unfortunately, for working data scientists and decision makers, it is challenging to find concrete algorithms that fit their purposes best. Therefore, it is highly desired to have a study on the different existing numerical association rule mining algorithms (NARM). In this paper, we provide such a detailed study by thoroughly reviewing 24 NARM algorithms from different categories (optimization, discretization, distribution).

ACS Style

Minakshi Kaushik; Rahul Sharma; Sijo Arakkal Peious; Mahtab Shahin; Sadok Ben Yahia; Dirk Draheim. On the Potential of Numerical Association Rule Mining. Communications in Computer and Information Science 2020, 3 -20.

AMA Style

Minakshi Kaushik, Rahul Sharma, Sijo Arakkal Peious, Mahtab Shahin, Sadok Ben Yahia, Dirk Draheim. On the Potential of Numerical Association Rule Mining. Communications in Computer and Information Science. 2020; ():3-20.

Chicago/Turabian Style

Minakshi Kaushik; Rahul Sharma; Sijo Arakkal Peious; Mahtab Shahin; Sadok Ben Yahia; Dirk Draheim. 2020. "On the Potential of Numerical Association Rule Mining." Communications in Computer and Information Science , no. : 3-20.

Conference paper
Published: 11 September 2020 in Transactions on Petri Nets and Other Models of Concurrency XV
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Measuring interestingness in between data items is one of the key steps in association rule mining. To assess interestingness, after the introduction of the classical measures (support, confidence and lift), over 40 different measures have been published in the literature. Out of the large variety of proposed measures, it is very difficult to select the appropriate measures in a concrete decision support scenario. In this paper, based on the diversity of measures proposed to date, we conduct a preliminary study to identify the most typical and useful roles of the measures of interestingness. The research on selecting useful measures of interestingness according to their roles will not only help to decide on optimal measures of interestingness, but can also be a key factor in proposing new measures of interestingness in association rule mining.

ACS Style

Rahul Sharma; Minakshi Kaushik; Sijo Arakkal Peious; Sadok Ben Yahia; Dirk Draheim. Expected vs. Unexpected: Selecting Right Measures of Interestingness. Transactions on Petri Nets and Other Models of Concurrency XV 2020, 38 -47.

AMA Style

Rahul Sharma, Minakshi Kaushik, Sijo Arakkal Peious, Sadok Ben Yahia, Dirk Draheim. Expected vs. Unexpected: Selecting Right Measures of Interestingness. Transactions on Petri Nets and Other Models of Concurrency XV. 2020; ():38-47.

Chicago/Turabian Style

Rahul Sharma; Minakshi Kaushik; Sijo Arakkal Peious; Sadok Ben Yahia; Dirk Draheim. 2020. "Expected vs. Unexpected: Selecting Right Measures of Interestingness." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 38-47.

Conference paper
Published: 11 September 2020 in Transactions on Petri Nets and Other Models of Concurrency XV
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Since its introduction in the 1990s, association rule mining(ARM) has been proven as one of the essential concepts in data mining; both in practice as well as in research. Discretization is the only means to deal with numeric target column in today’s association rule mining tools. However, domain experts and decision-makers are used to argue in terms of mean values when it comes to numeric target values. In this paper, we provide a tool that reports mean values of a chosen numeric target column concerning all possible combinations of influencing factors – so-called grand reports. We give an in-depth explanation of the functionalities of the proposed tool. Furthermore, we compare the capabilities of the tool with one of the leading association rule mining tools, i.e., RapidMiner. Moreover, the study delves into the motivation of grand reports and offers some useful insight into their theoretical foundation.

ACS Style

Sijo Arakkal Peious; Rahul Sharma; Minakshi Kaushik; Syed Attique Shah; Sadok Ben Yahia. Grand Reports: A Tool for Generalizing Association Rule Mining to Numeric Target Values. Transactions on Petri Nets and Other Models of Concurrency XV 2020, 28 -37.

AMA Style

Sijo Arakkal Peious, Rahul Sharma, Minakshi Kaushik, Syed Attique Shah, Sadok Ben Yahia. Grand Reports: A Tool for Generalizing Association Rule Mining to Numeric Target Values. Transactions on Petri Nets and Other Models of Concurrency XV. 2020; ():28-37.

Chicago/Turabian Style

Sijo Arakkal Peious; Rahul Sharma; Minakshi Kaushik; Syed Attique Shah; Sadok Ben Yahia. 2020. "Grand Reports: A Tool for Generalizing Association Rule Mining to Numeric Target Values." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 28-37.

Articles
Published: 12 May 2020 in Journal of Information and Telecommunication
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Most of the information is available in the form of unstructured textual documents due to the growth of information sources (the Web for example). In this respect, to extract a set of events from texts written in natural language in the management change event, we have been introduced an open information extraction (OIE) system. For instance, in the management change event, a PERSON might be either the new coming person to the company or the leaving one. As a result, the Adaptive CRF approach (A-CRF) has shown good performance results. However, it requires a lot of expert intervention during the construction of classifiers, which is time consuming. To palpate such a downside, we introduce an approach that reduces the expert intervention during the relation extraction. Also, the named entity recognition and the reasoning, which are automatic and based on techniques of adaptation and correspondence, were implemented. Carried out experiments show the encouraging results of the main approaches of the literature.

ACS Style

Sihem Sahnoun; Samir Elloumi; Sadok Ben Yahia. Event detection based on open information extraction and ontology. Journal of Information and Telecommunication 2020, 4, 383 -403.

AMA Style

Sihem Sahnoun, Samir Elloumi, Sadok Ben Yahia. Event detection based on open information extraction and ontology. Journal of Information and Telecommunication. 2020; 4 (3):383-403.

Chicago/Turabian Style

Sihem Sahnoun; Samir Elloumi; Sadok Ben Yahia. 2020. "Event detection based on open information extraction and ontology." Journal of Information and Telecommunication 4, no. 3: 383-403.

Article
Published: 12 March 2020 in World Wide Web
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In the last decade, Vehicular Ad hoc NETworks (VANETs) have attracted researchers, automotive companies and public governments, as a new communication technology to improve the safety of transportation systems aiming at offering smooth driving and safer roads. In this respect, a new Traffic Information System (TIS) has benefited from VANET services. The ultimate goal of a TIS consists in properly informing vehicles about road traffic conditions in order to reduce traffic jams and consequently CO2 emission while increasing the user comfort. To fulfil these goals, traffic information data or Floating Car data (FCD) must be efficiently exchanged between mobile vehicles by avoiding as far as possible the broadcast storm problem. In this respect, data aggregation appears as an interesting approach allowing to integrate FCD messages to generate a summary (or aggregate), which undoubtedly leads to reduce network traffic. We introduce, in this paper, a new data aggregation protocol, called Smart Directional Data Aggregation (SDDA). The main idea behind our SDDA protocol is to select the most pertinent FCD messages that must be aggregated. To this end, we rely on three filters: The first one is based on the vehicle’s directions. Indeed, every vehicle aggregates only FCD messages corresponding to its direction. Furthermore, it stores, carries and forwards uninteresting data. The second one is carried out by using road speed limitation. The third one relies on a suppression technique to remove duplicated FCD messages. Interestingly enough, our protocol works properly in both highway and urban conditions. The performed experiments show that SDDA outperforms the pioneering approaches of the literature in terms of effectiveness and efficiency.

ACS Style

Sabri Allani; Taoufik Yeferny; Richard Chbeir; Sadok Ben Yahia. Towards a smarter directional data aggregation in VANETs. World Wide Web 2020, 23, 2303 -2322.

AMA Style

Sabri Allani, Taoufik Yeferny, Richard Chbeir, Sadok Ben Yahia. Towards a smarter directional data aggregation in VANETs. World Wide Web. 2020; 23 (4):2303-2322.

Chicago/Turabian Style

Sabri Allani; Taoufik Yeferny; Richard Chbeir; Sadok Ben Yahia. 2020. "Towards a smarter directional data aggregation in VANETs." World Wide Web 23, no. 4: 2303-2322.

Conference paper
Published: 01 January 2020 in Communications in Computer and Information Science
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Nowadays, buildings are increasingly energy intensive, as they represent almost 40% of total energy consumption and more than 35% of CO2 emissions. The excessive and unnecessary use of planet resources and the use fossil fuel and a non-renewable energy source urged government and industry to explore new research directions and utility-driven energy improvement programs to drive advances in energy-efficient. Energy efficiency in smart buildings can be achieved by introducing a context-aware Internet of Things (IoT) approach, where sensors can learn from their surrounding environment to control the actuators in a coordinated network. However, the IoT network requirements are constantly changing in unpredictable fashion, which needs faster and frequent on-demand network reconfiguration. Software Defined Network (SDN) has been envisioned as a new approach to enable a flexible and agile network programmability in diverse IoT scenarios. However, the focus has primarily been on the design of the SDN computation logic, i.e. controllers, while the dynamic delivery and operations service-inferred IoT resource allocation has been postponed. To address this plethora of challenges, this paper we first extend Software Defined Network (SDN) with Network Function Virtualization (NFV) to support distributed IoT sensing devices automation and orchestration in micro-grid data center at the network edge of smart campus building. Second, we introduce a novel IoT data management model based on data-centric middleware IoT message broker that implements a hierarchical containment tree for retrieving sensor data from remote IoT devices. Then, we introduce a context-aware knowledge learning approach that maps raw sensing data into a meaningful context and transform them into the appropriate context representation models. Finally, we provide a proof of concept to demonstrate successful deployment and provisioning of virtualized services in the context of Smart Campus research project.

ACS Style

Akram Hakiri; Bassem Sallemi; Fatma Ghandour; Sadok Ben Yahia. Secure, Context-Aware and QoS-Enabled SDN Architecture to Improve Energy Efficiency in IoT-Based Smart Buildings. Communications in Computer and Information Science 2020, 55 -74.

AMA Style

Akram Hakiri, Bassem Sallemi, Fatma Ghandour, Sadok Ben Yahia. Secure, Context-Aware and QoS-Enabled SDN Architecture to Improve Energy Efficiency in IoT-Based Smart Buildings. Communications in Computer and Information Science. 2020; ():55-74.

Chicago/Turabian Style

Akram Hakiri; Bassem Sallemi; Fatma Ghandour; Sadok Ben Yahia. 2020. "Secure, Context-Aware and QoS-Enabled SDN Architecture to Improve Energy Efficiency in IoT-Based Smart Buildings." Communications in Computer and Information Science , no. : 55-74.

Conference paper
Published: 20 November 2019 in Transactions on Petri Nets and Other Models of Concurrency XV
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Test Suite Reduction (TSR) approaches aim at selecting only those test cases of a test suite to reduce the execution time or decrease the cost of regression testing. They extract the tests that cover test requirements without redundancy, or exercise changed parts of the System Under Test (SUT) or parts affected by changes, respectively. We introduce DTSR (Deterministic Test Suite Reduction), that relies on the hypergraph structural information to select the candidate test cases. Requirement data, which are associated with the test cases, optimize the selection by retaining a deterministic set. To do so, DTSR considers a test suite as a hypergraph, where its nodes are equivalent to tests, and its hyperedges are similar to requirements. The algorithm extracts a subset of the minimal transversals of a hypergraph by selecting the minimum number of test cases satisfying the requirements. We compare our new algorithm versus search based ones, and we show that we outperform the pioneering approaches of the literature. The reduction rate varies from \(50\%\) up to \(65\%\) of the initial set size.

ACS Style

Shaima Trabelsi; Mohamed Taha Bennani; Sadok Ben Yahia. A New Test Suite Reduction Approach Based on Hypergraph Minimal Transversal Mining. Transactions on Petri Nets and Other Models of Concurrency XV 2019, 15 -30.

AMA Style

Shaima Trabelsi, Mohamed Taha Bennani, Sadok Ben Yahia. A New Test Suite Reduction Approach Based on Hypergraph Minimal Transversal Mining. Transactions on Petri Nets and Other Models of Concurrency XV. 2019; ():15-30.

Chicago/Turabian Style

Shaima Trabelsi; Mohamed Taha Bennani; Sadok Ben Yahia. 2019. "A New Test Suite Reduction Approach Based on Hypergraph Minimal Transversal Mining." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 15-30.

Journal article
Published: 14 October 2019 in Procedia Computer Science
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The accuracy and relevance of Business Intelligence & Analytics (BI&A) rely on the ability to bring high data quality to the data warehouse from both internal and external sources using the ETL process. The latter is complex and time-consuming as it manages data with heterogeneous content and diverse quality problems. Ensuring data quality requires tracking quality defects along the ETL process. In this paper, we present the main ETL quality characteristics. We provide an overview of the existing ETL process data quality approaches. We also present a comparative study of some commercial ETL tools to show how much these tools consider data quality dimensions. To illustrate our study, we carry out experiments using an ETL dedicated solution (Talend Data Integration) and a data quality dedicated solution (Talend Data Quality). Based on our study, we identify and discuss quality challenges to be addressed in our future research.

ACS Style

Manel Souibgui; Faten Atigui; Saloua Zammali; Samira Cherfi; Sadok Ben Yahia. Data quality in ETL process: A preliminary study. Procedia Computer Science 2019, 159, 676 -687.

AMA Style

Manel Souibgui, Faten Atigui, Saloua Zammali, Samira Cherfi, Sadok Ben Yahia. Data quality in ETL process: A preliminary study. Procedia Computer Science. 2019; 159 ():676-687.

Chicago/Turabian Style

Manel Souibgui; Faten Atigui; Saloua Zammali; Samira Cherfi; Sadok Ben Yahia. 2019. "Data quality in ETL process: A preliminary study." Procedia Computer Science 159, no. : 676-687.

Conference paper
Published: 09 August 2019 in Transactions on Petri Nets and Other Models of Concurrency XV
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Most of the information is available in the form of unstructured textual documents due to the growth of information sources (the Web for example). In this respect, to extract a set of events from texts written in natural language in the management change event, we have been introduced an open information extraction (OIE) system. For instance, in the management change event, a PERSON might be either the new coming person to the company or the leaving one. As a result, the Adaptive CRF approach (A-CRF) [15] has shown good performance results. However, it requires a lot of expert intervention during the construction of classifiers, which is time consuming. To palpate such a downside, we introduce an approach that reduces the expert intervention during the relation extraction. The named entity recognition and the reasoning which are automatic and based on techniques of adaptation and correspondence. Carried out experiments show the encouraging results of the main approaches of the literature.

ACS Style

Sihem Sahnoun; Samir Elloumi; Sadok Ben Yahia. Event Detection Based on Open Information Extraction and Ontology. Transactions on Petri Nets and Other Models of Concurrency XV 2019, 244 -255.

AMA Style

Sihem Sahnoun, Samir Elloumi, Sadok Ben Yahia. Event Detection Based on Open Information Extraction and Ontology. Transactions on Petri Nets and Other Models of Concurrency XV. 2019; ():244-255.

Chicago/Turabian Style

Sihem Sahnoun; Samir Elloumi; Sadok Ben Yahia. 2019. "Event Detection Based on Open Information Extraction and Ontology." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 244-255.

Conference paper
Published: 09 August 2019 in Transactions on Petri Nets and Other Models of Concurrency XV
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One of the key success points of a data warehousing project is the design of the multidimensional schema. Many researches shows that the use of ontologies in information system design is becoming more and more promising. In this paper, we propose a method for multidimensional design of data warehouses, from an operational data source, using ontologies. Furthermore, we introduce the concept of multidimensional ontology as a tool for the specification of multidimensional knowledge. In addition, we present an ontology-based method for data modeling schema that eventually covers different phases of the data warehouse life cycle, and takes into account the users by considering their personalized needs as well as their knowledge of the domain.

ACS Style

Manel Zekri; Sadok Ben Yahia; Inès Hilali-Jaghdam. A Software Prototype for Multidimensional Design of Data Warehouses Using Ontologies. Transactions on Petri Nets and Other Models of Concurrency XV 2019, 273 -284.

AMA Style

Manel Zekri, Sadok Ben Yahia, Inès Hilali-Jaghdam. A Software Prototype for Multidimensional Design of Data Warehouses Using Ontologies. Transactions on Petri Nets and Other Models of Concurrency XV. 2019; ():273-284.

Chicago/Turabian Style

Manel Zekri; Sadok Ben Yahia; Inès Hilali-Jaghdam. 2019. "A Software Prototype for Multidimensional Design of Data Warehouses Using Ontologies." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 273-284.

Conference paper
Published: 09 August 2019 in Transactions on Petri Nets and Other Models of Concurrency XV
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Over recent years, Collective Intelligence (CI) and crowdsourcing platforms have become a vital resource for learning, problem-solving, decision making and predictions. Unfortunately, the only generic model for developing CI systems (i.e., the CI Genome model) was published nearly a decade ago. Most articles that discuss this model only use examples of older CI projects, thereby raising the question ‘Can the genome model comprehensively describe recent CI platforms? If not, what new genes could be proposed to improve the model’? In this article, we answer this question by conducting an analysis of 10 CI projects developed after 2015. We first analyze these projects with respect to the genome model, and then identify three new components namely: Beneficiaries, Knowledge and Social Cause, and Collaboration-based Contest; that could help us improve the genome model, thereby improving our understanding of more recent CI initiatives.

ACS Style

Shweta Suran; Vishwajeet Pattanaik; Sadok Ben Yahia; Dirk Draheim. Exploratory Analysis of Collective Intelligence Projects Developed Within the EU-Horizon 2020 Framework. Transactions on Petri Nets and Other Models of Concurrency XV 2019, 285 -296.

AMA Style

Shweta Suran, Vishwajeet Pattanaik, Sadok Ben Yahia, Dirk Draheim. Exploratory Analysis of Collective Intelligence Projects Developed Within the EU-Horizon 2020 Framework. Transactions on Petri Nets and Other Models of Concurrency XV. 2019; ():285-296.

Chicago/Turabian Style

Shweta Suran; Vishwajeet Pattanaik; Sadok Ben Yahia; Dirk Draheim. 2019. "Exploratory Analysis of Collective Intelligence Projects Developed Within the EU-Horizon 2020 Framework." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 285-296.

Conference paper
Published: 06 August 2019 in Transactions on Petri Nets and Other Models of Concurrency XV
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A data scientist could apply several machine learning approaches in order to discover valuable knowledge from the data. While applying several techniques, he might discover that some pieces of knowledge are invariant, what ever the technique he used. We consider such knowledge as mandatory concepts, i.e., unavoidable knowledge to be discovered. As interesting property, a mandatory concept is characterized by a non-shared isolated point, that relates pieces of data, e.g., an object to a property, a document to specific words, an image to a specific topic, etc. Hence, the isolated points allow to make the distinction between the concepts. In this paper, we present a new approach for mandatory concepts extraction by making a level-based properties composition. Hence, the N-Composites isolated points are identified and constitute a key element for mandatory concept localization. We experiment our new algorithm by considering the coverage quality metrics.

ACS Style

Samir Elloumi; Sadok Ben Yahia; Jihad Al Ja’Am. Using Mandatory Concepts for Knowledge Discovery and Data Structuring. Transactions on Petri Nets and Other Models of Concurrency XV 2019, 362 -375.

AMA Style

Samir Elloumi, Sadok Ben Yahia, Jihad Al Ja’Am. Using Mandatory Concepts for Knowledge Discovery and Data Structuring. Transactions on Petri Nets and Other Models of Concurrency XV. 2019; ():362-375.

Chicago/Turabian Style

Samir Elloumi; Sadok Ben Yahia; Jihad Al Ja’Am. 2019. "Using Mandatory Concepts for Knowledge Discovery and Data Structuring." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 362-375.