Abdelaziz Bouras

A survey of clustering algorithms for big data: Taxonomy and empirical analysis

Clustering algorithms have emerged as an alternative powerful meta-learning tool to accurately analyze the massive volume of data generated by modern applications. In particular, their main goal is to categorize data into clusters such that objects are grouped in the same cluster when they are similar according to specific metrics. There is a vast body of knowledge in the area of clustering and there has been attempts to analyze and categorize them for a larger number of applications. However, one of the major issues in using clustering algorithms for big data that causes confusion amongst practitioners is the lack of consensus in the definition of their properties as well as a lack of formal categorization. With the intention of alleviating these problems, this paper introduces concepts and algorithms related to clustering, a concise survey of existing (clustering) algorithms as well as providing a comparison, both from a theoretical and an empirical perspective. From a theoretical perspective, we developed a categorizing framework based on the main properties pointed out in previous studies. Empirically, we conducted extensive experiments where we compared the most representative algorithm from each of the categories using a large number of real (big) data sets. The effectiveness of the candidate clustering algorithms is measured through a number of internal and external validity metrics, stability, runtime, and scalability tests. In addition, we highlighted the set of clustering algorithms that are the best performing for big data.

Product lifecycle management – from its history to its new role

This paper is a result of comprehensive consultation among the authors, with the scientists and leading actors in the area of PLM, which is a reference term for a list of phenomena currently ongoing in the industrial community. This paper discusses the pervasive concept of product lifecycle management (PLM), starting from its history to its constituent elements and its role in the current industry. The authors propose and elaborate their vision for the future steps of the PLM in terms of emerging issues and topics that industrial practitioners and researchers need to address.

Jointly identifying opinion mining elements and fuzzy measurement of opinion intensity to analyze product features

Opinion mining mainly involves three elements: feature and feature-of relations, opinion expressions and the related opinion attributes (e.g. Polarity), and feature-opinion relations. Although many works have emerged to achieve its aim of gaining information, the previous researches typically handled each of the three elements in isolation, which cannot give sufficient information extraction results; hence, the complexity and the running time of information extraction is increased. In this paper, we propose an opinion mining extraction algorithm to jointly discover the main opinion mining elements. Specifically, the algorithm automatically builds kernels to combine closely related words into new terms from word level to phrase level based on dependency relations; and we ensure the accuracy of opinion expressions and polarity based on: fuzzy measurements, opinion degree intensifiers, and opinion patterns. The 3458 analyzed reviews show that the proposed algorithm can effectively identify the main elements simultaneously and outperform the baseline methods. The proposed algorithm is used to analyze the features among heterogeneous products in the same category. The feature-by-feature comparison can help to select the weaker features and recommend the correct specifications from the beginning life of a product. From this comparison, some interesting observations are revealed. For example, the negative polarity of video dimension is higher than the product usability dimension for a product. Yet, enhancing the dimension of product usability can more effectively improve the product.

Deriving consistent pairwise comparison matrices in decision making methodologies based on linear programming method

Pairwise comparison matrix (PCM) with crisp or fuzzy elements should satisfy consistency requirements when it is used in analytic hierarchy process (AHP) or in fuzzy AHP methodologies. An algorithm has been presented to obtain a new modified consistent PCM for the corresponding inconsistent original one. The algorithm sets a linear programming problem based on all of the constraints. To obtain the optimum eigenvector of the middle value of the new PCM, segment tree is used to gradually approach the greatest lower bound of distance with the original PCM. As to obtain the lower value and upper value of the new PCM, a theory is proposed to reduce adding uncertainty factors and could maximum maintain the similarity with original PCM. The experiments for crisp elements show that the proposed approach can preserve more the original information than references. The experiments for fuzzy elements show that our method can effectively reduce inconsistency and obtain suitable modified fuzzy PCMs.

Open IoT Ecosystem for Sporting Event Management

By connecting devices, people, vehicles, and infrastructures everywhere in a city, governments and their partners can improve community well-being and other economic and financial aspects (e.g., cost and energy savings). Nonetheless, smart cities are complex ecosystems that comprise many different stakeholders (network operators, managed service providers, logistic centers, and so on), who must work together to provide the best services and unlock the commercial potential of the so-called Internet of Things (IoT). This is one of the major challenges that faces today’s smart city movement, and the emerging “API economy.” Indeed, while new smart connected objects hit the market every day, they mostly feed “vertical silos” (e.g., vertical apps, siloed apps, and so on) that are closed to the rest of the IoT, thus hampering developers to produce new added value across multiple platforms and/or application domains. Within this context, the contribution of this paper is twofold: 1) present the strategic vision and ambition of the EU to overcome this critical vertical silos’ issue and 2) introduce the first building blocks underlying an open IoT ecosystem developed as part of an EU (Horizon 2020) Project and a joint project initiative (IoT-EPI). The practicability of this ecosystem, along with a performance analysis, is carried out considering a proof-of-concept for enhanced sporting event management in the context of the forthcoming FIFA World Cup 2022 in Qatar.

IoT-based Smart Parking System for Sporting Event Management

By connecting devices, people, vehicles and infrastructures everywhere in a city, governments and their partners can improve community wellbeing and other economic and financial aspects (e.g., cost and energy savings). Nonetheless, smart cities are complex ecosystems that comprise many different stakeholders (network operators, managed service providers, logistic centers...) who must work together to provide the best services and unlock the commercial potential of the IoT. This is one of the major challenges that faces todays smart city movement, and more generally the IoT as a whole. Indeed, while new smart connected objects hit the market every day, they mostly feed vertical silos (e.g., vertical apps, siloed apps...) that are closed to the rest of the IoT, thus hampering developers to produce new added value across multiple platforms. Within this context, the contribution of this paper is twofold: (i) present the EU vision and ongoing activities to overcome the problem of vertical silos; (ii) introduce recent IoT standards used as part of a recent Horizon 2020 IoT project to address this problem. The implementation of those standards for enhanced sporting event management in a smart city/government context (FIFA World Cup 2022) is developed, presented, and evaluated as a proof-of-concept.

Application of a Decision Model by Using an Integration of AHP and TOPSIS Approaches within Humanitarian Operation Life Cycle

Emergency logistics is one of the most important parts of disaster relief operations. Quick and adequate decision making in this sector is vital but sometimes hard to achieve. This issue is currently faced by several humanitarian organizations, where the high turnover of staff and the lack of adequate tools make it hard to learn from past experiences. Choosing the most appropriate supplier, the adapted warehouse and transportation means is a complicated task. Indeed, on the one hand there are many criteria to take into account in the decision-making process, and on the other hand the relative importance of those criteria is changing over time. Existing academic works on this issue are very difficult to implement on real case scenarios as they do not propose practical solutions. In this paper, a decision model which evolves over time, depending on operations progresses is proposed. Selection of supplier, warehouse and vehicle are taken into consideration thanks to the Multi-Criteria Decision Making (MCDM) approach. In order to achieve a proper decision, Analytic Hierarchy Process (AHP) is used first to analyze the structure of alternatives selection problem and to determine weights of criteria. Then Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method is used to obtain final ranking in a four-phases of humanitarian operation life cycle. A numerical example based on preliminary data from the French Red Cross including the sensitivity analysis is presented to clarify and validate the methodology.

IoT for Smart City Services: Lifecycle Approach

Internet of Things (IoT) and Smart City are widely recognized to address the complexity of modern city operation. Concentration of population, scarcity of resources and environmental concerns are the main challenges that face city operators, and make ordinary service provisioning less efficient. In city environment, IoT sensors can be sources of real-time data; and, IoT actuators can execute real-time actions in the physical domain. IoT systems range from domain-specific to cross-sectoral systems where valuable data/ information flow across interconnected complex systems. Yet, to integrate domain-specific IoT systems into the complete vision of Smart City, as a System of Systems (SoS), there is a need to address heterogeneity of data sources, diversity of application domains and the big number of stakeholders across different phases of lifecycle. This paper suggests Service Lifecycle Management (SLM) concepts and Lifecycle Modeling Language (LML) to analyze, plan, specify, design, build and maintain IoT-enabled Smart City Service Systems.

Integration of Smart City and Lifecycle Concepts for Enhanced Large-Scale Event Management

Hosting large-scale events is the dream of many cities around the world, however challenging. Hosting a large-scale event is a complex project that requires careful planning, precise implementation, interactive operation, and successful closure of all activities, with the involvement of all relevant organizations, authorities and stakeholders. Therefore, event organizers pay their utmost attention to the improvement of every aspect of Event Management. Application of smart city concepts can address the complexity of service provisioning during large-scale events, through better efficiency, higher quality, and real-time decision-making capabilities. Lifecycle management concepts can improve the whole event management cycle across different phases. This paper proposes combining Smart City and Lifecycle concepts to improve vertical service provisioning and horizontal integration between different sectors, across different phases while creating a suitable platform for information and knowledge sharing within the same event and with other similar events. This research aims to reach a more holistic smart event experience.

Fuzzy multi-criteria lifecycle system maturity decision making based on an integrated Fuzzy AHP and VIKOR methodology

A wide range of maturity models for product lifecycle management are proposed to assess the relative position of companies on their road to complete lifecycle systems implementation. However, it is a tough job for the company to accurately make decisions of improving lifecycle systems maturity due to the vague and complex data. The fuzzy VIKOR (Multi-criteria Optimization and Compromise Solution) is a compromise solution and has the ability of transfer subjective and implicit linguistics into objective and transparent data. An integrated fuzzy AHP (Analytic hierarchy process) and fuzzy VIKOR methodology is proposed to make a decision among option PLM strategies. The weights of the criteria called local weights are determined by fuzzy pairwise comparison matrices. The weights of alternatives with respect to criteria called global weights are calculated by fuzzy VIKOR. The final ranking weights are the overall analysis of local and global weights. A numerical example illustrates and clarifies the running steps of the proposed methodology.