Blockchain technology, with its features like decentralization, immutability, traceability, and security, offers significant benefits across various industries. However, storage scalability remains a major barrier to its mainstream use. Storing large amounts of data on a blockchain while maintaining performance is challenging, especially with querying difficulties. As of September 8, 2024, the Bitcoin ledger reached 599.26 GB, and Ethereum reached 1149.94 GB. These ledgers contain various data types, including transactions, blocks, smart contract states, and user identities, which continually grow due to blockchain's immutability. The peer-to-peer structure exacerbates storage challenges, as each participant must maintain a full copy of the blockchain. This growth is problematic for networks relying on on-chain storage [1]. Additionally, retrieving specific information within these large ledgers is difficult, as data is dispersed across blocks in a decentralized ledger. Unlike conventional databases with query languages like SQL, blockchain networks lack a standardized query language, complicating data retrieval and filtering [3]. When it comes to storing, processing, and querying large volumes of data, big data technologies bring enormous value. Big data storage tools are specifically designed for storing, managing, retrieving, and querying massive amounts of data. They allow efficient sorting of large amounts of data by various applications as well as efficient querying of these data. Thus, leveraging these tools in the blockchain ecosystem can be crucial for enhancing the storage of blockchain transactions, increasing resource efficiency, and enhancing the system's overall scalability [2].

The convergence of Internet of Things (IoT) and Artificial Intelligence (AI) technologies has given rise to the Internet of Behaviors (IoB) paradigm. This emerging concept leverages advancements in IoT sensors, big data analytics, and AI to gather, analyze, and interpret human behavioral data. This review aims to provide a taxonomy and analysis of the current state and trajectory of IoB. We categorize IoB approaches into online and physical variants, analyzing their key characteristics, data sources, privacy considerations, and monitoring capabilities. Through a structured literature survey, we explore the applications and research challenges through several domains such as healthcare, marketing, transportation, and manufacturing. The comparative analysis conducted in this study ofers insights into the complementary strengths and limitations of online and physical IoB, providing a clear definition of these concepts and a taxonomy of their key characteristics. Based on these definitions, this study investigates their applications across diverse sectors and identifies critical criteria (data sources, privacy concerns, ethical considerations, and implementation challenges) to provide a synthetic overview of their contribution to IoB development.

In recent years, there has been a significant increase in the volume of data and the number of datasets in the healthcare industry, and this trend is expected to continue and intensify. Various strategies are being developed to analyse the data. Nevertheless, these strategies are extensively segregated according to the specific data formats and disorders. Privacy-preserving Hybrid Learning Framework for Healthcare. The framework introduces an hybrid learning technique in order to achieve efficient decision-making. To tackle the challenge of interoperability and heterogeneity with multiple data sources, we propose to integrate a data meshing approach. Furthermore, this paper identifies the potential privacy challenges for machine learning-based healthcare applications that operate with multiple data sources and demand the excessive computation of cloud computing. In addition, we present a comprehensive use case for forecasting cardiovascular disease. The detailed use case and scenarios highlight how our proposal can improve the decision-making process.

Today, with the exponential rise in the use of the internet, the trend of the massive use of mobile devices (tablets, laptops, computers, iPads etc...) the user has become inseparable from his smartphone, which facilitates the use of several services in different fields (work, research, commerce, etc.). Besides, with the proliferation of data, the needs of consumers are becoming increasingly complex. So the composition of web services remains a necessary process allowing to combine several atomic services aggregating different functionalities to answer a complex request. Several techniques in the literature like, AI planning, work flow, Agent, Mathematics and Semantic. These techniques are mainly involved in the automation and improvement of the composition process. In this article, we will survey several context aware web service composition approaches based on these techniques. Remember that the Mobile environment is characterized by software, hardware and space constraints and Web Service Composition in mobile environment must respect these conditions.

This vision paper underscores the technical challenges and difficulties of addressing urban computing from a Diversity and Inclusion (DEI) decolonial standpoint. Issues of DEI, which encompass factors such as gender, race, age, socio-economic status, physical abilities, and religion, necessitate a shift in how we conceptualise the design of scientific and engineering methodologies. The decolonial perspective can be employed to scrutinise the influence of dominant viewpoints on our understanding of progress, innovation, and their contribution to societal welfare. The hypothesis is that these perspectives can be integrated into the design processes that deal with solutions, knowledge, and information systems across all layers, from infrastructure to application and user interfaces. This paper exhibits research questions, challenges and possible strategies to design DEI aware urban computing solutions with a decolonial perspective.

Drug traceability is currently a very challenging area given the complexity of several issues, including drug quality and counterfeit medications. The counterfeited drugs have a major impact on human life, treatment outcomes and economic burden. To deal with these issues, we propose a semantic blockchain-based system for drug traceability that aims at detecting counterfeit drugs in order to improve the patients’ safety and quality of life as well as eliminating manufacturers’ potential loss and increasing their revenue. Our proposal is based on blockchain and semantic web technologies to enhance the representation capability of data in the pharmaceutical supply chain.

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Under the black-box model, data services do not export (meta)-data describing the conditions in which data are collected, in which they are deployed and processed, and the quality of the data they deliver. Thus, this model creates blind spots that prevent determining to which extent providers can be trusted to use their data services for building target applications. This paper proposes a QoS-based trust evaluation model for black box data services that combines QoS indicators, including service performance and data quality. The paper also introduces DETECT (Data sErvice as a black box Trust Evaluation arChitecTure) which validates our model. The experimental results demonstrate the feasibility and effectiveness of our solution.

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.

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.

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.

Service-oriented architecture (SOA) is known to be characterized by its openness, its agility and its composability. Such architecture includes autonomous, interoperable and potentially reusable services, which are implemented as Web Services (WS). When deployed in the Cloud Environment, security threats may increase at the WS level, raising some security challenges, such as vulnerability discovery and trust or availability violation. Addressing security issues require complex tasks that should be treated separately with associated concerns. To deal with this problem, we refer here to an alternative architecture known as Micro-Services Architecture (MSA) which is based on the principle of decomposing large and complex software projects into many atomic sub-projects. In Addition, to control, manager and ensure that vulnerabilities are appropriately treated and the expected security objectives are achieved, we find that the governance of Web Service security is inevitably required. Hence, we suggest here a Web Service security governance approach aims to reduce the security flaws and to enhance trust between web services. This approach consists in combining micro-services by using a sub-set of GDPR (EU General Data Protection Regulation (GDPR) replaces the Data Protection Directive 95/46/EC and was designed to harmonize data privacy laws across Europe, to protect and empower all EU citizens data privacy and to reshape the way organizations across the region approach data privacy.) rules and a set of defined security policies. This approach allows guessing the dedicated micro-service referring to user security requirements.

In recent years, more and more challenges concerning the modeling of new and complex business process systems have emerged. E-learning business process is one of those systems which are based on documents. Modeling documents and their interactions with tasks in the same process, called document driven workflows, has not been widely represented. We focus on security issues by modeling time as an extension of such systems and discuss some constraints. There are different approaches for modeling those temporal aspects in workflow systems. Since Petri Nets are known for their capability and flexibility in modeling complex systems, we propose a Colored Petri Net and emphasize the use of Time Petri Nets to model workflow management systems based on documents with time constraints. We illustrate our model on a running example of e-learning process.

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.

Due to the specificity of the omni-channel retailing scenario, consumers in a brick and mortar store have a merging need for various decision support services to enhance their shopping experience. To survive the fierce competition in the retail world, firms need to predict which services are relevant to their customers and when to offer these services. To address this challenge, we propose a context-aware approach to collect, analyze, and interpret real-time consumer behavioral data from portable devices. Experiment results confirm that context-aware interaction can greatly enhance consumers' shopping experience in the offline scenario.

Cloud provider barriers still stand. After a decade of cloud computing, customers struggle to overcome the challenge of crossing multi-provider clouds to benefit from fine-grained resource distribution, business independence from CSPs and cost savings. Although increasingly popular, most adopted IaaS intercloud solutions are generally limited to specific public cloud providers or present maintainability issues. Remaining hurdles include complexity of management and operations of such infrastructures, in presence of per-customer customizations and provider configurations. The Infrastructure as Code (IaC) paradigm is emerging as key enabler for IaaS multi-clouds, to develop and manage infrastructure configurations. However, due to complexity of the infrastructure life-cycle, to heterogeneity of composing resources and to user-customizations, this approach is far from being viable. In this paper, we explore an aspect-oriented approach to IaC deployment and management. We propose Mantus, a IaC-based multi-cloud builder composed of an aspect-oriented Domain-Specific Language called TML, or TOSCA Manipulation Language, and a corresponding aspect weaver to inject flexibly non-functional services in TOSCA infrastructure templates. We show the practical feasibility of our approach, with also good results in terms of performance and scalability.

Web service discovery is a challenging task that has received a lot of interest in the last decade. Several approaches have been proposed, however, many limitations remain. In this paper, we focus on collaborative semantic discovery of RESTful services according to the HATEOAS principle. We propose an approach based on 1) semantic links between services; 2) user profile clustering to dynamically measure user similarity for queries in a similar context, and 3) a user profile ontology to manage users and corresponding services. A prototype has been implemented to evaluate our proposal and show its effectiveness.

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.

Existing data integration techniques have to be revisited to query big data collections on the Cloud. Service Level Agreements implement the contracts between the cloud provider and the users, and between the cloud and service providers. Given SLA heterogeneity and data integration scalability problems, we propose an SLA guided data integration for querying data on multiple clouds.

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.

Data as a Service (DaaS) builds on service-oriented technologies to enable fast access to data resources on the Web. However, this paradigm raises several new privacy concerns that traditional privacy models do not handle. In addition, DaaS composition may reveal privacy-sensitive information. In this paper, we propose a formal privacy model in order to extend DaaS descriptions with privacy capabilities. The privacy model allows a service to define a privacy policy and a set of privacy requirements. We also propose a privacy-preserving DaaS composition approach allowing to verify the compatibility between privacy requirements and policies in DaaS composition. We propose a negotiation mechanism that makes it possible to dynamically reconcile the privacy capabilities of services when incompatibilities arise in a composition. We validate the applicability of our proposal through a prototype implementation and a set of experiments.

Privacy is still among the key challenges that keep hampering DaaS service composition solution. Indeed services may follow different, conflicting privacy specifications with respect to the data they use and provide within a composition. In this paper, we propose an approach for privacy-aware composition of DaaS services. Our approach allows verifying the compatibility of privacy specifications of services involved in a composition. In the case when any composition will be incompatible in terms of privacy, we introduce a novel approach based on negotiation to reach compatibility of concerned services. The negotiation approach is cautiously operated with without any privacy damaging of services. We validate the applicability of our proposal through a set of experiments.

Data Mashup is a special class of mashup application that combines information on the fly from multiple data sources to respond to transient business needs. Mashing up data requires an important programming skill on the side of mashups' creators, and involves handling many challenging privacy and security concerns raised by data providers. This situation prevents non-expert users from mashing up data at large. In this paper, we propose a declarative approach for mashing-up data. The approach allows data mashup creators to create data mashups without any programming involved, they just need to specify "declaratively" their data needs. The approach will then build the mashups automatically while taking into account the data's privacy and security concerns.

Rich types of data offered by data as a service(DaaS) in the cloud are typically associated with different and complex data concerns that DaaS service providers, data providers and data consumers must carefully examine and agree with before passing and utilizing data. Unlike service agreements, data agreements, reflecting conditions established on the basis of data concerns, between relevant stakeholders have got little attention. However, as data concerns are complex and contextual, given the trend of mixing data sources by automated techniques, such as data mash up, data agreements must be associated with data discovery, retrieval and utilization. Unfortunately, exchanging data agreements so far has not been automated and incorporated into service and data discovery and composition. In this paper, we analyze possible steps and propose interactions among data consumers, DaaS service providers and data providers in exchanging data agreements. Based on that, we present a novel service for composing, managing, analyzing data agreements for DaaS in cloud environments and data marketplaces.

Data Mashup is a special class of mashup application that combines information on the fly from multiple data sources to respond to transient business needs. Data mashup is a difficult task that would require an important programming skill on the side of mashups' creators, and involves handling many challenging privacy and security concerns raised by data providers. This situation prevents non-expert users from mashing up data at large. In this paper, we present a declarative approach for mashing-up data. The approach allows data mashup creators to build data mashups without any programming involved. The approach builds the mashups automatically and takes into account the data's privacy concerns. We evaluate the efficiency of the approach via a thorough set of experiments. The results show that handling data privacy introduces only a negligible cost in the mashup building time.

In this paper, we present Meerkat, a dynamic framework for preserving privacy in Web services. We define a Web service-aware privacy model that deals with the privacy of input data, output data, and operation usage. We introduce a matching protocol that caters for partial and total privacy compatibility. Finally, we propose a negotiation model to reconcile clients' requirements with providers' policies in case of incompatibility.

Web services composition is a keystone in the development of interoperable systems. However, despite the widespread adoption of Web services, several obstacles still hinder their smooth automatic semantic reconciliation when being composed. Consistent understanding of data exchanged between composed Web services is hampered by various implicit modeling assumptions and representations. Our contribution in this article revolves around context and how it enriches data exchange between Web services. In particular, a context-based mediation approach to solve semantic heterogeneities between composed Web services is presented.

Summary form only given. Centralized architectures based on the client server paradigm show their limits each time the Internet extends its capabilities. They present problems in terms of security, robustness, performance, high needs for administration. Short time solutions like load balancing and replication, or purchasing of more powerful hardware tend to cope with these limits for a while, but do not provide a definitive answer to these problems. Currently, different proprietary approaches hinder attempts to enabling communication between heterogeneous systems, while the Internet is developing toward a better interoperability with service-oriented architectures. Open source and standardization efforts allow building a framework providing an original answer to interoperability and scalability over the Internet. In this paper, we consider that the distributed nature of P2P platforms allied with the standardized WS architecture, based on SOAP and WSDL represent a powerful framework for building dynamic interoperable and decentralized information systems. The aim of our work is to allow P2P participants to dynamically access and compose semantic WS outside of the P2P network, and to improve existing WS publication and discovery by using the decentralization of P2P architecture. In order to achieve this goal, we propose a mediation framework built around the JXTA P2P architecture. This framework is based on the OWL-S language as a means for a semantic description of WS interfaces, in order to solve heterogeneity problems.