ABSTRACT Artificial intelligence (AI) is increasingly adopted in animal‐related domains such as health monitoring, behaviour analysis and welfare assessment. However, concerns about the transparency and interoperability of AI outputs are rising. This scoping review investigates how AI and explainable AI (XAI) are applied in animal‐related systems and examines the role of semantic technologies in enhancing their interoperability. In this review, we followed PRISMA‐ScR guidelines and conducted five structured searches across ScienceDirect, Springer, Scopus, IEEE Xplore and Web of Science. The searches targeted AI applications (S 1 ), XAI applications (S 2 ) and the use of semantic knowledge in AI (S 3 ) and XAI (S 4 ). Studies were screened, assessed using the QualSyst tool and selected based on Q1/Q2 SJR or CORE C–A* classification. A total of 21 review papers were selected for AI applications and 8 for XAI. No eligible papers were found regarding the use of semantics in AI or XAI. While explainability is gaining attention, it remains mostly limited to visual or statistical tools, lacking domain‐specific contextualization. We observed that AI is now pervasive in animal‐related research, yet XAI practices remain underdeveloped and lack semantic grounding. This gap calls for ontology‐based explainability to enhance trust, relevance and usability for both experts and non‐specialists.

Aspect-Based Sentiment Analysis (ABSA) offers fine-grained opinion mining by linking sentiments to specific aspects within text. While modern models often act as black boxes, limiting interpretability and trust, particularly in sensitive domains like agriculture, where understanding the underlying explanation behind model predictions is essential for building trust among farmers, agronomists, and agricultural stakeholders. To address this, we present an explainable ABSA pipeline tailored to smart agriculture, combining a hybrid DeBERTaHAN architecture with SHAP post-hoc explainability. Applied to an enriched, domain-specific Twitter dataset, our model achieves high classification performance ($87 \%$ accuracy and F1-score) while offering interpretable, token-level SHAP visualizations. With a local fidelity of 0.98 and sparsity of 0.13, our approach balances predictive accuracy with transparency, making it wellsuited for decision support in smart agriculture. Additionally, we tested our model on popular ABSA benchmark datasets: SemEval2014-Task4, SemEval2015-Task12, and SemEval2016Task5 restaurants datasets, where it achieved accuracies of $88.77 \%, 98.01 \%$, and $87.72 \%$, respectively.

Video-based fall detection plays a crucial role in telemonitoring as a key component in ensuring timely intervention and safety for older adults who live alone. Despite promising advances, most vision-based fall detection methods remain insufficiently robust and fail to generalize effectively to real-world scenarios. In this paper, we present a comprehensive experimental framework designed to rigorously and generically assess the robustness of fall detection approaches. This framework encompasses three evaluation settings: intra-subject, intersubject, and cross-dataset evaluation. It is validated using a custom architecture that combines a convolutional neural network (CNN) with a bidirectional LSTM (BiLSTM), evaluated on three public datasets: URF, Le2i-FD, and MCFD. Experimental results demonstrate strong generalization, with recall exceeding $80 \%$ in cross-dataset settings. These findings underscore the importance of diverse evaluation strategies in developing reliable fall detection systems.

Federated Learning (FL) has emerged as a promising paradigm for decentralized machine learning, enabling multiple clients to collaboratively train a global model without sharing their raw data. Despite its privacy-preserving design, FL remains vulnerable to privacy leakage through inference attacks, such as membership inference, and to integrity threats like Byzantine behaviors that can degrade model reliability. To address these risks, we propose Local Data Privatization Preprocessing (LDPP), a lightweight client-side method that enforces differential privacy while preserving the statistical properties of local data. LDPP operates through a three-stage process: (i) transforming data into a standardized representation (normal or uniform), (ii) injecting calibrated noise using differential privacy mechanisms, such as Laplace or Gaussian distributions, and (iii) applying an inverse transformation to asymptotically recover the original data distribution. We formally prove that LDPP satisfies $\epsilon$-differential privacy and maintains key distributional characteristics. Additionally, LDPP can be combined with robust aggregation techniques, such as Krum, to strengthen defense against adversarial tampering. Comprehensive experiments in EMNIST and MedMNIST datasets demonstrate that LDPP significantly reduces the success of membership inference attacks and improves robustness under label-flipping scenarios while preserving high model accuracy. These findings position LDPP as a scalable and practical solution to improve both privacy and robustness in federated learning frameworks.

Ontology evaluation faces challenges due to existing tools and methods’ diverse and complex nature, which vary widely across different metrics. This diversity highlights the need for a comprehensive tool to cover all evaluation dimensions. In this paper, we propose MoOnEv (Modular Ontology Evaluation and Validation), a tool that covers all assessment aspects. MoOnEv aims to incorporate all evaluation metrics, including the 35 criteria proposed by the OMEVA approach, plus one additional criterion and conducts validation through testing use cases. MoOnEv can evaluate modular and non-modular ontologies, MoOnEv has proven effective and versatile in comprehensive evaluation reports and multiple tests on various ontologies.

Accurate assessment of crop nutritional status is critical for effective farm management, affecting both environmental sustainability and economic viability. Nitrogen, an essential nutrient for plant growth, is critical in detecting crop health and making fertilization decisions. However, standard nitrogen level estimation methods frequently include labor-intensive and environmentally dangerous laboratory analyses. In response, this study investigates the possibilities of modern technologies, notably machine learning (ML) and remote sensing, for improving nitrogen estimate in crops. Remote sensing, which uses sensors mounted on satellites, drones, or other airborne platforms, provides a non-destructive and efficient alternative to traditional methods for obtaining extensive spectral data. Machine learning techniques improve upon this approach by processing massive amounts of data to uncover significant patterns and relationships. Although previous studies have primarily relied on vegetation indices generated from spectral observations, this study provides an alternate technique. By auto-encoding raw spectral data, machine-learned features are developed as an alternative to vegetation indices, providing a new perspective on leaf nitrogen content (LNC) estimation. To test performance, a number of machine learning algorithms are examined, including random forest, support vector machines, and extreme gradient boosting. Our findings suggest that the autoencoder-based methodology outperforms established methods, highlighting its potential for reshaping nitrogen estimate in agriculture.

Computational analysis of animal behavior (CABA) is a modern approach to studying animal behavior using computer techniques. It provides tools for smart farming and analyzes videos mostly with machine learning and deep learning algorithms. The paper aims to integrate ontology in the field of CABA. An ontology is a formal representation of knowledge that can facilitate the integration of data from different sources, allowing experts to better understand and study animal health and behavior. We propose to build a modular ontology based on the Modular Ontology Modeling (MOMo) methodology. The proposed ontology, MoonCAB (Modular ontology for Computational analysis of Animal Behavior), represents the behavior of livestock animals (sheep and goats) in a pasture: the duration of each activity, the meaning of each duration, the season during which these activities take place, etc. Our ontology is composed of 68 classes, 36 properties, 150 individuals, and 8135 axioms. It has been tested by Fact++ reasoner and SPARQL queries.

Wasteful irrigation systems are significant contributors to water scarcity on the globe. Irrigation Scheduling based on Machine Learning (ML) algorithms is considered essential in helping reduce these wastes significantly. We conducted in this study a systematic mapping of ML-based Irrigation scheduling to identify how researchers approached Irrigation Scheduling and which ML models have been used in this area. It builds a comprehensive overview of what has been investigated on irrigation scheduling and discusses the open issues to be addressed in the future.

Summary In the context of Internet of Things, thingsourcing is poised to promote the collective behavior that should prevail among things despite their independent nature being confined into silos. By analogy with crowdsourcing where crowds of (sometimes anonymous) people are contacted for their expertise, thingsourcing abstracts crowds of things and provides the necessary mechanisms for composing things together so they collectively satisfy users' demands. However, to ensure successful discovery of things according to these demands' requirements, an ontology‐based semantic description of things is deemed necessary. This article presents an approach for enriching things' descriptions semantically in preparation for their composition with respect to specific scripts that define who will do what, when, and where. A system demonstrating semantic thingsourcing along with a case study about dairy supply‐chain is presented in this article as well.

Diabetes mellitus is a chronic disease that appears when the pancreas does not produce enough insulin or the body does not correctly use its insulin. If not adequately managed or diagnosed on time, this pathology can cause a lot of damage to the body organs, such as the heart, eyes, kidneys, and so on. Research carried out through machine learning has made it possible to have increasingly efficient and precise models for detecting and preventing type 2 diabetes. However, most of the models mentioned do not offer guarantees on the privacy of patient data used during the training process. In addition, these models are generally stored in a centralized repository, where the analysis is performed with full access to sensitive content, implying increased attack risks on confidentiality and privacy. This paper proposes a Differentially Private Stochastic Gradient Descent applied to the Federated Averaging (DPSGDFedAvg) model for diabetes prediction using the Pima Indian dataset. In first results, we obtained an accuracy between 60% and 70% with a raised level of privacy. We demonstrate in this work the feasibility and effectiveness of the DPSGDFedAvg model in offering a raised level of privacy and maintaining utility of the global FL model.

The world has been experiencing several crises recently, particularly on the social front. Therefore, new technologies have been adapted to provide the most diverse possible solutions, including crowdfunding platforms that concentrate on social projects. They have recently piqued the interest of investors and donors, particularly those based on blockchain technology, thanks to their ability to achieve reliability. Social crowdfunding platforms have developed new strategies for luring donations and investments. However, there is still a lack of development of these ideas and exploiting the benefits and services provided by blockchain technology properly. This paper presents blockchain technology in a socially oriented crowdfunding platform reward-based that aims to provide a transparent, secure, auditable, and efficient system. BELONG is the first leading platform that merged the ideas of crowdfunding, donations, and charitable investments with a type of blockchain-based token called Non-fungible tokens (NFTs). The goal is to create safe investment channels, and that is because of the dearth of studies on the idea of integrating NFTs into humanitarian, charitable, or social activities. It relies on two strategies for seeking funds; the bedrock on which all two are built is the NFT. This study intends to reach out to all societal stakeholders interested in this field. As a result, each strategy targets a specific category, including donors, investors, and individuals, to make funding opportunities available for everyone. A dedicated prototype, using Ethereum and Vuejs, is implemented to demonstrate the platform's feasibility.

Thyroid disease is one of the most common endocrine disorders worldwide. However, thyroid conditions can be challenging to diagnose because symptoms are very similar to those of other diseases. A proper diagnosis depends on clinical examination and many blood tests involving a large amount of complex data that is difficult to interpret. Early thyroid detection is crucial since it significantly reduces complications and minimizes death risk. The main objective of this study is to create an accurate framework for improving the diagnostic accuracy of thyroid diseases. For this purpose, we propose a three-stage approach based on dimensionality reduction using feature selection, data sampling to handle the data-imbalance problem, and stacked ensemble learning instead of a single machine learning algorithm to give the final prediction. This research shows that the proposed approach can diagnose thyroid disease more accurately than existing techniques, achieving 99.49% of precision and 99.46% in terms of F1-score.

In the context of Industry 4.0, the Internet of Robotic Things (IoRT) represents an attractive paradigm that aims to supply real-time data and automate tasks via human imitation. An IoRT is defined as the incorporation of IoT technology within robotic systems. In this setting, the business managers may improve performance and increase the productivity of their process by integrating the IoRT within their Business Processes (BPs). Nonetheless, this integration is not a trivial task due to the diversity of the IoT, robot, and BP concepts. In this paper, we address the incorporation of the IoRT within the BP through a lightweight extension of the Business Process Modeling Language 2.0 (BPMN 2.0) meta-model called IoRT-aware Business Process meta-model (IoRT-aware BP2M). Our proposed IoRT-aware BP2M allows, on the one hand, to represent the main concepts of IoT, robot and BP in a unique meta-model, and on the other hand to specify some practical constraints to select the suitable device. As a proof of concept, we generated an IoRT-aware BP model in the agriculture field with some implementation details. Besides, we used the Bunge-Wand-Weber (BWW) ontology to prove the proposed meta-model’s completeness and clarity. The obtained results show that the proposed meta-model has an acceptable completeness value and ontological expressiveness.

Smart irrigation has many advantages in optimizing resource usage (e.g., saving water, reducing energy consumption) and improving crop productivity. In this paper, we contribute to this field by proposing a robust and accurate machine learning-based approach that combines the power of feature selection methods and stacking ensemble method to effectively determine the optimal quantity of water needed for a plant. Random Forest, Recursive Feature Elimination (RFE), and SelectKBest are used to assess the importance of the features. Then, based on the best subset of features, a stacking ensemble model is proposed that combines CART, Gradient Boost Regression (GBR), Random Forest (RF) and XGBoost regressors. The different models involved in this approach are trained and tested using a collected dataset about various crops such as tomatoes, grapes, and lemon and encompasses different features such as meteorological data, soil data, irrigation data, and crop data. The experiments demonstrated the performance of RF in analyzing the feature importance. The findings of feature selection highlight the importance level of the evapotranspiration, the depletion, and the deficit to maximize the model’s accuracy. The results also showed that the proposed stacking model (Stacking_GBR+CART+RF+XGB) with the 10 most essential features outperforms individual models and other stacking models by achieving low error rates (i.e., MSE=0.0026, MAE=0.0279, RMSE=0.0509) and high R2score (i.e., 0.9927).

Data curation deals with managing the data by applying different tasks such as extraction, enrichment, cleaning to fit the purpose of use. Indeed, nowadays, there is an increasing need to implement such tasks in the big data era to maintain data management. Big data is involved in decision processes to perform analysis, visualization, prediction, etc. Thus, there is a dependency between the generated outcomes and the input data of such a process. Therefore, decision process features (e.g., decision context, user constraints, and requirements) need to be taken into account during the data management process, including the data curation phase. Although the proposed curation approaches in the literature are diverse, most of them are static and do not consider the decision process features. Moreover, most of the proposals are dedicated to curating a specific data source format (e.g., structured/unstructured data source). To overcome these limitations, we propose a new approach ACUSEC (Adaptive CUration SErvice Composition) that ensures adaptive curation services composition by considering different features: the source type, the user constraints and preferences, and the decision context. To do so, we rely on AI and machine learning mechanisms such as reinforcement learning. Following the approach's definition, we conducted experiments that show encouraging results in overall execution time and adaptation to the above features.

The computer science community is paying more and more attention to data due to its crucial role in performing analysis and prediction. Researchers have proposed many data containers such as files, databases, data warehouses, cloud systems, and recently data lakes in the last decade. The latter enables holding data in its native format, making it suitable for performing massive data prediction, particularly for real-time application development. Although data lake is well adopted in the computer science industry, its acceptance by the research community is still in its infancy stage. This paper sheds light on existing works for performing analysis and predictions on data placed in data lakes. Our study reveals the necessary data management steps, which need to be followed in a decision process, and the requirements to be respected, namely curation, quality evaluation, privacy-preservation, and prediction. This study aims to categorize and analyze proposals related to each step mentioned above.

As cloud services are reaching a considerable maturity level, several academics and researchers are exploiting this paradigm to benefit from its advantages. More specifically, with SaaS services' advent, a tremendous number of enterprises rely on their composition as an alternative to their Information Technology infrastructure. While composing SaaS services, achieving the Business Process (BP) goals and objectives through this cloud model is becoming easier and even beneficial; composing multiple SaaS services to support the BP execution is not a trivial practice regarding the complex nature of the BP. Indeed, BP is composed of a set of activities with various functional requirements, data that should be exchanged between each SaaS service, and roles imposing that only authorized actors can perform a specific activity. This paper proposes a comprehensive framework for business process-based SaaS composition that covers the semantic matching between BP activities and SaaS labels and the allocation of BP activities to SaaS services using a genetic algorithm while considering the data and the activities access control issues.

Summary The explosion of cloud services over the Internet has raised new challenges in cloud service selection and ranking. The existence of a great variety of offered cloud services made the users think deeply about the most appropriate services that meet their needs and at the same time are adaptable to their context. Nowadays, online reviews are used for the purpose of enhancing the effectiveness of finding useful product information, having impact on the consumers' decision‐making process. In this context, the current paper suggests a context‐aware cloud service ranking approach using online reviews and based on sentiment analysis (CROSA). Its main objective is to ease the cloud service selection. The CROSA approach analyzes sentiments associated with service measurement index (SMI)–based service properties for each alternative cloud service. Moreover, it enhances the cloud service decision‐making by supporting fuzzy sentiments through the intuitionistic fuzzy set theory and PROMETHEE II. The experimental results presented in this paper show that this approach is efficient and performing.

The Internet of Things (IoT) is expected to improve the individuals’ quality of life. However, ensuring security and privacy in the IoT context is a non-trivial task due to the low capability of these connected devices. Generally, the IoT device management is based on a centralized entity that validates communication and connection rights. Therefore, this centralized entity can be considered as a single point of failure. Yet, in the case of distributed approaches, it is difficult to delegate the right validation to IoT devices themselves in untrustworthy IoT environments. Fortunately, the blockchain may provide decentralization of overcoming the trust problem while designing a privacy-preserving system. To this end, we propose a novel privacy-preserving IoT device management framework based on the blockchain technology. In the proposed system, the IoT devices are controlled by several smart contracts that validate the connection rights according to the privacy permission settings predefined by the data owners and the stored record array of detected misbehavior of each IoT device. In fact, smart contracts can immediately detect the devices that have vulnerabilities and have been hacked or pose a threat to the IoT network. Therefore, the data owner’s privacy is preserved by enforcing the control over the own devices. For validation purposes, we deploy the proposed solution on a private Ethereum blockchain and give the performance evaluation.

In the last years, the Internet of Things (IoT) know a huge widespread thanks to the increase of the connected objects number. The IoT technology has several benefits that make it among the proliferation technology. The major advantage of this technology is the communication between devices known as Machine-to-Machine (M2M) communication allowing them to be connected without human intervention. Thanks to this advantage, the technology become able to facilitate the people's lives that it become smoother through a seamless cooperation between virtual objects and physical ones. As well as, the IoT sweep various fields (e.g., industry, health) thanks to its capacity to automate tasks.In this setting, a tremendous number of business managers are interesting to integrate the IoT devices into their Business Processes (BPs), known in literature as IoT-aware BP. This integration gives the opportunity to the business managers to avail from the IoT technology in their process through an enhancement of the business performance and an achievement of the business competitiveness. Thus, several researchers competed to identify approaches and methods to integrate the IoT technology within the BP paradigm. In this paper, we present a review of the different proposed approaches that deal with the integration of the IoT technology within the BP. Furthermore, we give in this paper, a rich comparative analysis based on a set of criteria. Finally, we identify some initiatives and challenges in the IoT-aware BP paradigm.

This paper presents thingsourcing to enable thing collaboration in the context of IoT. Compared to crowdsourcing that refers to a crowd of persons, there is limited research in thingsourcing which deprives things from participating in complex business applications. In this paper, thingsourcing is associated with a platform that acts as an IoT marketplace where things sign-up and sign-off looking for opportunities to complete users' demands. The platform also has a set of mechanisms that allow to describe, search for, and “glue” things together. For demonstration purposes a car service center is used illustrating how things like service bays and vehicles collaborate in compliance with scripts defined in ComPOS (Composition language for Palcom Oblivious Services).

Nowadays, with the increasing number of data sources, especially in environmental domain, earth observation programs face major challenges for environmental data exploitation, mainly due to data sources heterogeneity of different types such as access techniques, used protocols, languages, data formats, etc. Although typical solutions abstract from this heterogeneity with a layer of data services, the development of such systems remains tedious in this context. In this paper, we propose an approach based on Model-Driven Engineering (MDE) combined with semantic annotations, to automate data service development on top of data sources. Our work contributes to the development of integrated service-based architectures driven by automatic service generation, data integration from existing environmental systems and automatic service annotations. Our solution, applied to the detection of natural disasters, provides 1) appropriate modelling of data sources and services to apply model-to-text (M2T) transformations, 2) automatic generation of Representational State Transfer (REST) data service code template, 3) automatic generation of semantically annotated Hypermedia-based descriptors of these services. We have implemented and evaluated our solution with a set of real data sources provided by the Sahara and Sahel Observatory (OSS), OpenWeatherMap and CHIRPS.

Nowadays, tremendous number of enterprises are looking to outsource their business processes in order to gain in productivity, reduce cost, and enhance performance. Outsourcing business processes to cloud computing and more specifically to SaaS services, is actually among the most prominent opportunities regarding the benefits that this paradigm offers. However, matching the business process activities with the suitable SaaS services is not a trivial task regarding the diversity of both, functional requirements of business processes and the functionalities offered by SaaS services. Furthermore, once the business process execution is entirely supported by the SaaS services, the process owner, lacking in general the required expertise related to SaaS domain, needs to be aware of how the business process is handled by the SaaS. This is done principally through transforming the business process model to the SaaS model. This paper proposes a model-based approach using SAT-based formal verification to select and validate the most suitable SaaS services to support the business process achievement. Furthermore, a model to model transformation from business process to SaaS is proposed. This transformation is based on (i) a lightweight extension of the business process meta-model of ISO/CEI 19510 and (ii) our proposition of the SaaS meta-model.

Social business processes support enterprises tap into the opportunities of Web 2.0. To ensure an efficient support, this paper presents an approach for identifying an enterprise’s business processes that could be converted into social. Indeed, not all processes are eligible for this conversion. The approach is built upon the concept of enterprise architecture that sheds the light on the necessary contextual elements for guiding the socialization of business processes. Due to the fuzzy nature of socialization, two multi-criteria decision making techniques, fuzzy logic and fuzzy-decision tree, enhance this guidance. A set of experiments for evaluating the proposed approach are presented in the paper, as well.

Like any software product, the quality of Business Process (BP) models is an important issue that must be addressed. Indeed, a poor-quality BP model can disturb its implementation and execution as well as its performance. In the literature, different studies focused on the evaluation of BP model's quality using quality metrics. The common challenge highlighted by these studies is the lack of a consensus on the thresholds of the used quality metrics and the difficulty of interpreting the values of these metrics. In this paper, we propose an approach that uses existing quality metrics to evaluate the quality of BP models in terms of comprehensibility and modifiability. Besides, we determine the thresholds of the different quality metrics through decision tree technique. Decision tree also produces a set of decision rules that are useful to determine the quality level of BP model. The approach is validated through a tool named BP-Quality that calculates the values of the quality metrics and deduces the comprehensibility and modifiability levels based on calculated values and the aforementioned thresholds.

The Internet of Things (IoT) provides the opportunity to collect, process and analyze data. This opportunity helps to understand preferences and life patterns of individuals in order to offer them customized services. However, privacy has become a significant issue due to the personal nature of the knowledge derived from these data and the involved potential risks. Despite the increasing legislation pressure, few proposed solutions have dealt with the privacy requirements, such as consent and choice, purpose specification, and collection limitation. In this paper, we propose a privacy ontology in order to incorporate privacy legislation into privacy policies while considering several privacy requirements. Our proposed ontology aims both at making the smart devices more autonomous and able to infer data access rights and enforcing the privacy policy compliance at the execution level. We implemented and evaluated our privacy ontology based on a healthcare scenario.

Despite the smooth weaving of the Internet-of-Things into people's daily lives, many challenges, such as diversity and multiplicity of things' development technologies and communication standards, and users' reluctance due to privacy invasion, are slowing down this weaving. This paper tackles the challenge of things' passive nature that has confined them into a data-supplier role. Empowering things with additional capabilities would make them proactive so, that, they can for instance, reach out to peers exposing collaborative attitude and (un)form dynamic communities when necessary. In this paper, this empowerment takes shape through thing agentification that relies on norms (specialized into business and social) to regulate the operations of things and commitments to ensure thing compliance with these norms. No-compliance would lead to sanctions over things, which should affect their credibility and reputation. A proof-of-concept and missing-child case study technically illustrate thing agentification.

Configurable processes are increasingly being adopted by enterprises that seek experience sharing and best practice adoption. A configurable process is a customizable model that specifies how different enterprises perform similar processes. At the modeling level, a configurable process model provides for flexible business process (BP) reuse by (de)selecting the (ir)relevant parts to derive a particular process variant. At the exploitation level, it offers flexibility and agility to an enterprise looking to outsource its BP to different providers cooperating in a cloud federation. More specifically, an enterprise can use a configurable process model to derive particular process variants that it outsources depending on its objectives. In particular, it may opt for outsourcing the variant that results in the optimal deployment, e.g., having the minimal cost of allocated cloud services that fulfill the user quality of service (QoS) requirements. However, identifying the optimal deployment variants is a complex problem because of the heterogeneity of services within a cloud federation and the number of possible variants that can be derived from a configurable process model. In addition, the complexity of this problem increases for variable user QoS requirements. In this paper, we propose an approach to derive, from a configurable process model, the variant that has the optimal deployment in a cloud federation. We propose a linear programming approach that accounts for the variability of both the BP model and the user QoS requirements, and that ensures an optimal time-aware cloud service allocation. We experimentally show the effectiveness and flexibility of our approach on a generated testbed.

A cloud service is usually classified as Infrastructure as a Service, Platform as a Service or Software as a Service. This classification is not sufficient, when Business Processes are considered. Therefore, the next level of abstraction above SaaS is Business Process as a Service (BPaaS) [1]. BPaaS runs in a dynamic and volatile environment such as cloud computing and has hence very changing QoS (Quality of Service) parameters. Therefore, the BPaaS monitoring and adaptation are of a great importance to guarantee the QoS parameters defined in the Service Level Agreement (SLA). To address these issues, in this paper we proposed two Fuzzy systems for self-adaptive BPaaS that aim to prevent the SLA violations. The proposed approach objective is to satisfy two important issues: (i) the selection of service adaptation strategies and (ii) the cost as well as the impact of changes. We also presented the results of the experiments achieved to evaluate the proposed approach. These experimental results prove the effectiveness of the fuzzy systems.

Nowadays, cloud computing is an emerging technology thanks to its ability to provide on-demand computing services (hardware and software) with less description standardization effort. Multiple issues and challenges in discovering cloud services appear due to the lack of the cloud service description standardization. In fact, the existing cloud providers describe, their similar offered services in different ways. Thus, various existing works aim at standardizing the representation of cloud computing services while proposing ontologies. However, since the existing proposals were not evaluated, they might be less adopted and considered. Indeed, the ontology evaluation has a direct impact on its understandability and reusability. In this paper, we propose an evaluation approach to validate our proposed Cloud Service Ontology (CSO), to guarantee an adequate cloud service discovery. This paper contribution is threefold. First, it specifies a set of patterns and anti-patterns in order to evaluate CSO. Second, it defines an anti-pattern detection method based on SPARQL queries which provides a set of correction recommendations to help ontologists revise the ontology. Finally, some experiment tests were conducted in relation to: (i) the method efficiency and (ii) anti-pattern detection of design anomalies as well as taxonomic and domain errors within CSO.

A Social Business Process (SBP) is the result of blending social computing (a.k.a. Web 2.0) with business process (BP). Despite the benefits of SBP to enterprises, several limitations continue to undermine them. In this paper, we address two specific limitations, namely the difficulty of capturing SBP's requirements and the lack of a definition for SBP. Thus, meta-modeling is used to capture requirements from organizational, technological, and management perspectives. In addition, we introduce a definition for SBP by enriching an existing BP meta-model with social concepts. To annotate the SBP model with its requirements, a BPMN extension is proposed. The proposed meta-models are evaluated in terms of completeness and clarity using the Bunge-Wand-Weber ontology.

Cloud computing has revolutionized delivery of IT solutions increasing economic advantages, robustness, scalability, elasticity and security. Nowadays to achieve their cloud goals, organizations are increasingly move towards enabling multi-cloud environments which promised to support very large-scale, worldwide, distributed applications using multiple and independent cloud environments. However, given their complexity and distribution, multi-cloud has to face several key challenges around security and governance such as interoperability, portability, provisioning, elasticity, high availability and security. Therefore, these challenges increase the needs of security governance in such environments. Although some researches have been realized in the multi-cloud security domain, it becomes imperative to asses the current state of research and practice of its security governance. This paper aims to categorize the existing works related to security governance in multi-cloud environments by applying a systematic mapping study methodology in order toidentify trends and future directions. Our results prove that multi-clouds security governance seems to be a promising areain multi-cloud research and evaluation.

When enterprises decide to outsource their business processes to the Cloud, various considerations should be tackled. Indeed, the enterprises aim to reduce the business processes investment cost, to enhance their performance, and to focus on the enterprise core competency while considering security constraints. Hence, it is essential to assist enterprises to take the suitable decision by providing an appropriate decision system that specifies the activities to be outsourced as well as the Cloud resource to support them while considering the above enterprise preferences. Obviously, the outsourcing decision, when taken in a specific business process context, may be influenced by some variants that make it not suitable in another one. For instance, the business process workloads vary according to its execution period and thus the decision may require to change the Cloud resources as well as the outsourced activities to fit the new context requirements. This consideration should be taken when tackling an outsourcing decision to alleviate enterprise experts from the burden of assessing by themselves the changing business processes context and react by consequence to this change. In this paper, we present an adaptive outsourcing decision system, which provides personalized and autonomic decision-making to support the dynamic business process context when outsourced to the Cloud. The system predicts the business process context and provides accordingly appropriate decisions using the penalty based genetic algorithm.

The efficient monitoring of Cloud infrastructure is a topic that is currently attracting significant interest. Understanding the behaviour of the variety of monitoring tools and how to manage them optimally are challenging tasks. Monitoring tools and techniques have an important role to play in this area by gathering the information required to make informed decisions. Surveying these monitoring tools can identify the fitness of these tools in serving certain objectives for both Cloud providers and consumers in different Cloud operational areas.

Face to the increasingly stringent business competition, Small and Medium Sized Enterprises (SMEs) strive to excel in the marketplace by adopting different strategies and solutions. Outsourcing their business processes to the Cloud is considered as a widely adopted strategy. Among others, SMEs outsource their related business process to improve their performance. However, this strategy is not without inconvenience especially when the decision is taken without being aware of the business process requirements and the experts preferences. This paper's major contributions are: the proposition of a monitoring tool to identify the defaulting activities causing the degradation of the business process performance. These information are used later to favor the outsourcing of defaulting activities as the Cloud environment may be a better alternative environment instead of the enterprise one as it can provide a better IT infrastructure with lower cost compared to the in-house one. Furthermore, we propose a decision model using the Analytic Hierarchy Process (AHP) to assist experts in the fastidious task of selecting suitable activity to be outsourced according to the identified requirements and the experts preferences. It resorts essentially to commonly used standards which make it a powerful tool for both IT and business experts. An evaluation of the results is elaborated to insure the utility of the elaborated tool using the CloudSim toolkit.

A BP is a series of logically related tasks implemented by a set of applications/services performed together to produce a defined set of results. The cloud resources scheduling to BP tasks is a difficult problem. Due that, first, it considers the dependencies and communication between tasks within a BP. Second, it takes into account several objectives like minimizing the execution time, minimizing the execution cost, maximizing the resource utilization. Besides, BP execution can be affected by a set of contextual information such as the unavailability of resources, the overloading of network, etc. which make the scheduling problem more complex. In this paper, we propose a context-based scheduling approach for adaptive BP in the cloud.

Cloud computing is increasingly being used to deliver infrastructure, platform and/or software as services over the Internet. The resulting Cloud-based services with various types create complex management situations at the Cloud provider side. In particular, face to a large and dynamic number of service loads, a Cloud provider needs a means to maintain QoS (Quality of Service) levels it has agreed-upon with its customers. To properly operate and manage such complex situations, an effective and efficient monitoring is necessary. Most of the monitoring propositions are for the infrastructure and platform layers. This paper presents a multi tenant framework for QoS Monitoring and Detection of SLA Violations (QMoDeSV) for composite services implemented as BPEL (Business Process Execution Language) processes and deployed in a Cloud environment. It presents the complete design of QMoDeSV and a preliminary experimental evaluation of its performance.

Cloud computing is a revolutionary paradigm which enables on-demand provisioning of computing resources. Resources are delivered to cloud consumers in the form of infrastructure, platform and software services. These resources are deployed on three different models: private clouds, public clouds and hybrid clouds. In hybrid cloud context, private clouds externalize resources and invoke services from a public cloud when needed. However, in such a specific inter-cloud environment risks may arise. In fact, private cloud users often interact with cloud providers for services provisioning such as infrastructure, platforms and software. However, they are not sufficiently assured about how credible the data computed by these resources they have entrusted. This is due to clouds autonomy preservation, difference in control policy definitions and lack of transparency in clouds. In this paper, we propose a preventive/detective approach for assessing private cloud to select a trustworthy public cloud service. The solution is based on a mediator as a service that ensures the role of trust manager for the private cloud.

This paper discusses how social Web services are held responsible for the actions they take at run time. Compared to (regular) Web services, social Web services perform different actions, for instance establishing and maintaining networks of contacts and forming with some privileged contacts strong and long lasting collaborative groups. Assessing these actions' outcomes, to avoid any violation, occurs through commitments that the social Web services are required to bind to. Two types of commitments are identified: social commitments that guarantee the proper use of the social networks in which the social Web services sign up, and business commitments that guarantee the proper development of composite Web services in response to users' requests. Detecting commitment violation and action prohibition using monitoring results in imposing sanctions on the “guilty” social Web services and taking corrective actions. A system for commitment management in terms of definition, binding, monitoring, and violation detection is also discussed in this paper.

Examining interactions between Web services is of paramount importance to the success of service composition. We have previously proposed a 2-layer framework for modeling, analyzing, and managing these interactions. Interactions are assigned to two layers: business logic and support. The business-logic layer comprises control and transactional flows, whereas the support layer comprises exception and message flows. This paper continues this research effort by focusing on the synchronization of the four flows at run-time. In particular, we discuss the synchronization mechanisms integrated into the 2-layer framework and report our preliminary experiments on the implementation.

This paper is an overview of how capacity-driven Web services are defined and put into action. Because of the specificities of these Web services compared to regular(i.e., mono-capacity) Web services, the way they are looked into is different and occurs through four steps known as description, discovery, composition, and enactment. A Web service that is empowered with several capacities, that are in fact operations to execute, has to know which capacity out of several it has to choose and then, trigger at run-time.For this purpose, this Web service takes into account the requirements like data and network that are put on each capacity it was empowered with. A feasibility discussion on the implementation of capacity-driven Web services is presented in this paper, as well.