Ontologies play a critical role in structuring and sharing knowledge across diverse domains, particularly in quality management and business process modeling. Temporal concepts are essential to accurately represent the dynamic aspects of these processes. This paper extends the Descriptive Ontology for Linguistic and Cognitive Engineering (DOLCE) to integrate temporal dimensions into business process ontologies. Our approach includes the development of a dedicated time ontology, its alignment with BPMN (Business Process Model and Notation), an implementation of temporal properties using the Camunda Modeler tool, and a demonstration of its application in a clinical case study. By mapping temporal concepts such as intervals, deadlines, and delays to BPMN elements, we enable precise modeling of time-sensitive business processes.

Decision-making relies on the creation of a data warehouse, which in turn depends on the ETL process. The traditional ETL process is foundational for building Data Warehouses (DWs), particularly in relational environments where data is structured and adheres to strict schemas. With the rise of Big Data and NoSQL databases, new challenges have emerged due to schema flexibility and data heterogeneity. Given the critical need for timely and accurate decision-making in healthcare, it is essential to ensure efficient and scalable analytical processing of medical data. To address these challenges, this paper proposes an extended ETL approach—T-ETL-NoSQL—which introduces pre-aggregation during transformation phase and structure creation during loading phase to optimize the construction of NoSQL data warehouses. The focus of the work is on validating the applicability of this adapted ETL process for document-oriented NoSQL systems, specifically in the context of COVID-19 data.

Adaptive generative AI is emerging as a significant method to improve brain disease detection by providing innovative methods for brain data augmentation using modality synthesis and handling incomplete and imbalanced datasets. In this paper, we present an adaptive conditional-aware Generative Adversarial Network (GAN) that generates brain modality at a specific stage of disease by creating realistic brain imaging modalities for both Alzheimer’s disease (AD) and brain tumor. Furthermore, it dynamically learns and captures the mapping between different modalities to generate missing brain modalities, such as generating PET from MRI for Alzheimer’s disease detection and CT from MRI for brain tumor detection. Additionally, we combined this adaptive GAN with a new multimodal spatio-temporal transformer model that integrates different brain modalities such as MRI and PET for AD detection and CT with MRI for brain tumor detection and other data types, including genetic and clinical data. This method is enhanced using CNN with an innovative Squeeze-and-Excitation (SE) block. The proposed multimodal transformer encoder incorporates multi-head linear self-attention and a memory-augmented self-attention module to deal with the quadratic complexity of traditional vision transformers. A multi-cross fusion block is designed to extract the brain modalities’ interactions with genetic and clinical data. We evaluate our models using stratified 5-fold cross-validation on three datasets such as the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset for Alzheimer’s disease detection, achieving an average accuracy of 99.05 ± 0.08, and two brain tumor datasets, such as the Cancer Genome Atlas Low Grade Glioma Collection (TCGA-LGG) dataset, where we obtain 98.9 ± 0.12% and 97.50 ± 0.15% accuracy on the brain tumor dataset from Kaggle, respectively. These obtained results demonstrate the effectiveness of our proposed models in both disease prediction and progression tracking, opening a new way for more advanced brain disease detection.

Data collection is presented as the fundamental process of a computer project, an industrial project, or a research study. It gathers high-quality data that is then used for precise and intricate analysis. Therefore, through the process of data collection, one can formulate answers to relevant questions, enhance decision-making, and evaluate results. On the other hand, due to the vast amount of data, this process becomes increasingly complicated, necessitating the use of advanced tools and modern methods. In this context, we introduce a new data collection process based on agent software. Specifically, our focus is on self-adaptive agent. In our research, we are interested in collecting data in the field of transportation. Thus, this paper presents a case study illustrating the simulation-based design of a road traffic data collection self-adaptive agent.

This research article aims to apply the Business Process Model and Notation (BPMN) for the representation of Sensitive Business Processes (SBPs). Therefore, we propose a new extension “BPMN4SBP” explicitly integrate all the relevant issues and aspects relevant at the coupling of the business process modeling (BPM) domain and the knowledge management (KM) domain for improving the management of crucial knowledge which are mobilized and created by these processes. This article provides the analysis of requirements and relevant concepts for modeling SBP. Based on a core domain ontology, need for extension is identified and the valid BPMN4SBP extension is designed (according to the BPMN extension mechanism) by the construction of a conceptional domain model and the corresponding BPMN extension model.

In this paper, we propose an approach to handling uncertain time intervals and related qualitative relations, based on possibility theory. Four contributions are included in this approach. (1) Representing uncertain time intervals and related qualitative relations by extending the 4D-fluents approach with new ontological components. (2) Reasoning about uncertain time intervals by extending the Allen's interval algebra. The resulting interval relations preserve the good properties of the original algebra. (3) Proposing an OWL 2 possibilistic temporal ontology based on 4D-fluents approach extension and Allen's interval algebra extension. The proposed qualitative temporal relations are inferred via a set of SWRL rules. We validate our work by implementing a prototype based on this ontology. (4) Applying our work to PersonLink ontology and comparing the obtained results with our previous works.

The massive use of ontologies generates a large amount of semantic data. To facilitate their management, persistent solutions for storing and querying these semantic data loads have been proposed. This gave rise to a new type of databases, called ontology-based databases (OBDB). In recent years, the need for data and real-time services has increased significantly in a large number of applications. However, the OBDB does not implement any mechanism to address real-time applications which are characterized, not only by handling large amounts of data, but also by temporal constraints, to which can be submitted data and treatments. As well, geographically extended applications, requiring using real-time databases that manage data and distributed processing are increasingly needed.These applications are managed by Distributed Real-Time DataBase Management System (DRTDBMS). Like any system, the DRTDBMS, often go through overload phases, due to the unpredictable arrival of transactions submitted by users. In order to better manage Quality of Service (QoS) in these systems by facing instability periods, approaches based on Distributed Feedback Control Scheduling (DFCS) were proposed. These approaches does not address the use of ontological data. In this paper, we propose an approach aiming to enhance QoS in DRTDBMS based on data replication. It consists in extending the DFCS architecture by the manipulation of ontological data as well as handling the execution of accessing transactions. In the extension we propose, we study the applicability of different data replication policies. The proposed architecture is then called Replication-Based-Distributed Feedback Control Scheduling Architecture for Real-Time Ontology (Replication-Based-DFCS-RTO). We also show the contribution provided by our approach through simulation results.

Abstract The emergence of the Internet of Things (IoT) in the medical field has led to the massive deployment of a myriad of medical connected objects (MCOs). These MCOs are being developed and implemented for remote healthcare monitoring purposes including elderly patients with chronic diseases, pregnant women, and patients with disabilities. Accordingly, different associated challenges are emerging and include the heterogeneity of the gathered health data from these MCOs with ever‐changing contexts. These contexts are relative to the continuous change of constraints and requirements of the MCOs deployment (time, location, state). Other contexts are related to the patient (medical record, state, age, sex, etc.) that should be taken into account to ensure a more precise and appropriate treatment of the patient. These challenges are difficult to address due to the absence of a reference model for describing the health data and their sources and linking these data with their contexts. This article addresses this problem and introduces a semantic‐based context‐aware system (IoT Medicare system) for patient monitoring with MCOs. This system is based on a core domain ontology (HealthIoT‐O), that is, designed to describe the semantic of heterogeneous MCOs and their data. Moreover, an efficient interpretation and management of this knowledge in diverse contexts are ensured through SWRL rules such as the verification of the proper functioning of the MCOs and the analysis of the health data for diagnosis and treatment purposes. A case study of gestational diabetes disease management is proposed to evaluate the effectiveness of the implemented IoT Medicare system. An evaluation phase is provided and focuses on the quality of the elaborated semantic model and the performance of the system.

Reading and interpreting the medical image still remains the most challenging task in radiology. Through the important achievement of deep Convolutional Neural Networks (CNN) in the context of medical image classification, various clinical applications have been provided to detect lesions from Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) scans. In the diagnosis process for the liver cancer from Dynamic Contrast-Enhanced MRI (DCE-MRI), radiologists consider three phases during contrast injection: before injection, arterial phase, and portal phase for instance. Even if the contrast agent helps in enhancing the tumoral tissues, the diagnosis may be very difficult due to the possible low contrast and pathological tissues surrounding the tumors (cirrhosis). Alongside, in the medical field, ontologies have proven their effectiveness to solve several clinical problems such as offering shareable terminologies, vocabularies, and databases. In this article, we propose a multi-label CNN classification approach based on a parallel preprocessing algorithm. This algorithm is an extension of our previous work cited in the International Conference on Pattern Recognition and Artificial Intelligence (ICPRAI) 2020. The aim of our approach is to ameliorate the detection of HCC lesions and to extract more information about the detected tumor such as the stage, the localization, the size, and the type thanks to the use of ontologies. Moreover, the integration of such information has improved the detection process. In fact, experiments conducted by testing with real patient cases have shown that the proposed approach reached an accuracy of 93% using MRI patches of [Formula: see text] pixels, which is an improvement compared with our previous works.

Temporal data given by Alzheimer's patients are mostly uncertain. Many approaches have been proposed to handle certain temporal data and lack uncertain ones. This paper proposes an approach to represent and reason about quantitative time intervals and points and qualitative relations between them. It is suitable to handle certain and uncertain temporal data. It includes three parts. (1) The authors extend the 4D-fluents approach with certain components to represent certain and uncertain temporal data. (2) They extend the Allen's interval algebra to reason about certain and uncertain time intervals. They adapt these relations to relate a time interval and a time point, and two time points. All relations can be used for temporal reasoning by means of transitivity tables. (3) They propose a certain ontology based on the extensions. A prototype is implemented and integrated into an ontology-based memory prosthesis for Alzheimer's patients to handle uncertain data inputs. The evaluation proves the usefulness of the approach as all the inferences are well established and the precision results are promising.

Over the last few years, NoSQL systems are gaining strong popularity and a number of decision makers are using it to implement their warehouses. In the recent years, many web applications are moving towards the use of data in the form of graphs. For example, social media and the emergence of Facebook, LinkedIn and Twitter have accelerated the emergence of the NoSQL database and in particular graph-oriented databases that represent the basic format with which data in these media is stored. Based on these findings and in addition to the absence of a clear approach which allows the creation of data warehouse under NoSQL model, we propose, in this paper, an approach to create a Graph-oriented Data warehouse. We propose the transformation of Dimensional Fact Model into Graph Dimensional Model. Then, we implement the Graph Dimensional Model using java routines in Talend Data Integration tool (TOS).

In this paper, we propose an ontology-based approach for representing and reasoning about certain and uncertain temporal data. It handles temporal data in terms of quantitative time intervals and points and the qualitative relations between them (e.g., “before”). It includes three parts. (1) We extend the 4D-fluents approach with certain ontological components to represent the handled temporal data in OWL 2. (2) We extend the Allen’s interval algebra to reason about certain and uncertain time intervals. We adapt these relations to allow relating a time interval and a time point, and two time points. All relations can be used for temporal reasoning by means of transitivity tables. (3) The extended Allen’s algebra instantiates the 4D-fluents-based representation. Inferences are based on SWRL rules. Based on this ontology, a prototype is implemented and integrated into an ontology-based memory prosthesis for Alzheimer’s patients to handle certain and uncertain temporal data inputs.

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.

Summary Service Level Agreement (SLA) has recently garnered increasing attention in cloud computing due to its capability in the definition and the monitoring of the Quality of Service (QoS). SLA is generally generated following a process of negotiation in which the client chooses a subset of clauses among choices predefined by the provider on a “take it or leave it” basis. However, the heterogeneity between client requests and provider offers may result in inappropriate SLA. In addition, the cloud service context may change over the time, which causes a client requirement adjustment, and therefore, the SLA becomes unsatisfactory. In this paper, we propose a cloud SLA negotiation and re‐negotiation approach. Our aim is to ameliorate the SLA negotiation phase by adding the ability to integrate a semantic mapping between client requests and provider offers to generate a suitable SLA document. Moreover, our approach includes a context‐aware system to adapt the SLA throughout reasoning techniques and automatically ensure the re‐negotiation. We develop a prototype, and we test its performance. The results of a case study also show the efficiency of our approach.

Several DevOps tools have emerged to orchestrate cloud resources. However, inherent heterogeneity and complex implementation within these tools make it hard for DevOps users to design required resource-related artifacts. Currently, the defacto standard for cloud resource modeling and orchestration is TOSCA. Nonetheless, TOSCA is usually bound to TOSCA-compliant orchestration tools. Moreover, the actual integration between TOSCA and DevOps tools is still performed using costly coding and in ad-hoc manner. To resolve this, we believe that mapping and translation mechanisms between TOSCA and DevOps tools should be provided. In this paper, we propose a new model-driven approach for cloud resource orchestration. Our approach (i) adopts TOSCA to design resource-related artifacts regardless of a specific DevOps tool; (ii) enables a new model-driven translation technique that serves to translate the designed artifacts using TOSCA into DevOps specific artifacts and (iii) provides Connectors that intend to establish the bridge between DevOps-specific artifacts and the DevOps tools. Our approach provides a powerful enhancement to DevOps productivity and reusability by assisting toward a seamless integration between TOSCA and DevOps tools.

International audience

In view of the proliferation of audiovisual documents and the indexing limits mentioned in the literature, the progress of a new solution requires a better description extracted from the content. In this paper, we propose an approach to improve the description of the cinematic audiovisual documents. However, this consists not only in extracting the knowledge meaning conveyed in the content but also combining textual and visual modalities. In fact, the semiotic desription represents important information from the content. We propose in this paper an approach based on the use of pre and post production film documents. Consequently, we concentrate efforts to extract some descriptions about the use not only of the probalistic Latent Dirichlet Allocation (LDA) model but also of the semantic ontology LSCOM. Finally, a process of identifying a description is highlighted. In fact, the experimental results confirmed the importance of the performance of our approach through the comparison of our result with a human jugment and a semi-automatic method by using the MovieLens dataset.

Traditional data warehouses are unable to meet the growing needs of the modern enterprise to integrate and analyze a wide variety of data generated by social, mobile and sensor sources. What is remarkable is that many companies have changed their data storage in NoSQL databases. Given the important role played by this type of databases, it is necessary to study NoSQL databases as a data source for the modeling and the implementation of data warehouses. This paper proposes a method to design the data warehouse schema from schema free databases known as NoSQL databases. Our proposal starts with the extraction of schemes from document-oriented databases as an example of NoSQL database. This extraction is performed based on the MapReduce paradigm. Then, it defines the structure identification graph from the extracted schemes. This graph will help the designer to identify the multidimensional concepts for each schema extracted in order to design the global data warehouse schema.

In this paper, we propose a Multidimensional Business Ontology based-Premodeling System (MBOPS). Its knowledge-oriented core ensures an easier use and share for unambiguous business process modeling. Furthermore, the proposed (MBOPS) guarantees a good understanding level for the modelled-domain familiar designers as well as the modelled-domain non-familiar designers thanks to a Multidimensional business ontology. In order to evaluate the proposed (MBPS) and demonstrate its effectiveness, we choice the breast cancer treatment protocols as an applicative field.

In order to enhance the business process, the Bid Process Information System (BPIS) should be made more performant by ensuring greater flexibility and interoperability between devices. Moreover, the specication of this system has to deal with “three fit” problems. To this end, four dimensions have been identified in order to cope with potential failures: operational, organizational, decision-making, and cooperative dimensions. In this paper, we focus on the decision-making dimension of the BPIS and propose an approach for representing data warehouse schema based on an ontology that captures the multidimensional bid-knowledge.

Services in cloud computing run under specified constraints defined in Service Level Agreement (SLA) which becomes the basic core that guarantee the Quality of Service (QoS). However, the ambiguity in agreement terms makes the consumer face new challenges especially in understanding and analyzing SLA document. These challenges increasingly rise when the consumer uses services from multiple providers; since each one has its own SLA terminology. In this paper, we have significantly automated the process of managing and analyzing cloud SLA using semantic web technologies like OWL (Ontology Web Language), SWRL (Semantic Web Rule Language) and SQWRL (Semantic Query-Enhanced Web Rule Language). We describe the cloud SLA modeling and analyzing approach that assists consumers automatically analyze terms of various SLA documents. A prototype implementation demonstrates the feasibility and the efficiency of our approach.

In the last decade, we have witnessed an explosion of data volume available on the Web. This is due to the rapid technological advances with the availability of smart devices and social networks such as Twitter, Facebook, Instagram, etc. Hence, the concept of Big Data was created to face this constant increase. In this context, many domains should take in consideration this growth of data, especially, the Business Intelligence (BI) domain. Where, it is full of important knowledge that is crucial for effective decision making. However, new problems and challenges have appeared for the Decision Support System that must be addressed. Accordingly, the purpose of this paper is to adapt Extract-Transform-Load (ETL) processes with Big Data technologies, in order to support decision-making and knowledge discovery. In this paper, we propose a new approach called Big Dimensional ETL (BigDimETL) dealing with ETL development process and taking into account the Multidimensional structure. In addition, in order to accelerate data handling we used the MapReduce paradigm and Hbase as a distributed storage mechanism that provides data warehousing capabilities. Experimental results show that our ETL operation adaptation can perform well especially with Join operation.

ETL (Extract, Transform, Load) processes presents the main complex task in data warehouse project. Since, they are exposed to several unauthorized attacks, security aspects should be considered in their design phase to better match the security requirements and to avoid later fundamental, cost-intensive adaptations. To this end, we propose a matching procedure between vulnerabilities and ETL processes steps. The vulnerabilities categorization aims to prevent and protect these processes. We select vulnerabilities from the National Vulnerability Database (NVD), which maintains standardized information about reported software vulnerabilities. The selected vulnerabilities belong to SGBD environment (SQL server, business intelligence, oracle database server products). In this paper, we describe the matching procedure steps and explain the criteria of classification.

The description of movie documents has become a necessary condition to explore the content. For this purpose, the extraction of generic descriptions represents a pertinent solution for an interrogation process. In this context, two types of descriptions are used, an external and a content description. In this regard, the title, the genre and the keyword descriptions represent the description that can be identified from the content. Consequently, we propose in this paper a framework not only to extract these descriptions, but also to define a relationship between the movies through these descriptions. In fact, to extract the external description, we use the Dublin Core metadata standard while to extract the content description, we propose a method to identify the genre and the keyword. The objective behind this method is to extract the semantic knowledge from the content. Indeed, two types of techniques are introduced, namely, statistical and semantic. This principle is based on the use of the combination between textual modality through the synopsis, the script of the film and the visual modality through the use of the superposed text on the image. The experimental results confirmed a promising performance with two databases, namely, "MovieLens dataset" and "MEG database" through the following validation techniques: pertinence, recall, precision and F-measure.

Over the last decade, ontologies have been increasingly used in recommender systems. Domain ontologies are mainly used to analyze the user behaviour with reference to knowledge structure in order to build user profiles. In addition, ontologies are useful for modelling the trust between users extracted from a social network. We, accordingly, propose to combine two approaches to improve the recommendation process in tourism domain. The first is the trusted friends' preference based on the assumption that users generally have a tendency to use items recommended by friends rather than strangers. The second is the ontology based user interest to represent the semantics of these interests. A Trusted Friends' Application is developed to determine social trusted friends by analyzing user's profile on Facebook. Afterwards, we have represented the user's model as an ontology that takes into consideration all trusted friends' preferences and the degree of trust between friends. Finally, we have used this ontology in a knowledge-based tourism recommender system as a smart e-tourism tool able to recommend items based on the users' preferences and their trusted friends' preferences.

Internet of Things (IoT) covers a variety of applications including the Healthcare field. Consequently, medical objects become connected to each other with the purpose to share and exchange health data. These medical connected objects raise issues on how to ensure the analysis, interpretation and semantic interoperability of the extensive obtained health data with the purpose to make an appropriate decision. This paper proposes a HealthIoT ontology for representing the semantic interoperability of the medical connected objects and their data; while an algorithm alleviates the analysis of the detected vital signs and the decision-making of the doctor. The execution of this algorithm needs the definition of several SWRL rules (Semantic Web Rule Language).

Peptide identification using mass spectrometry is an indispensable tool in the field of proteomics. There is already a wide range of approaches in the literature that attempt to infer peptides throughout various computational methods mixed with several biology properties. An important progress in the proteomics research has led a strong need for more efficient and accurate approaches for peptide identification. The accuracy and efficiency of these techniques is indispensable to ensure as many correctly identified peptides as possible. In this paper, we start by a comparison of database search and de novo peptide identification. We then present the main impact of cleavage distribution on intensity values during fragmentation process. Next, we present our proposed method of peptide identification by integrating intensity distribution in both database and de novo methods in order to improve the identification process. Other features have been taken into account in our calculation such as water and ammonia losses, and the correlation between amino acids. Then, we present the experiments and results applied to evaluate our approach in order to prove and ensure the effectiveness of our hypothesis. Finally, we propose our perspectives on future work by giving our thoughtful solutions for several problems that prevent to reach the correct identification.

The identification of alignments between heterogeneous ontologies is one of the main research issues in the semantic web.The manual matching of the ontologies is a complex, time consuming and an error prone task.Therefore, ontology matching systems aims to automate this process.Usually, these systems perform the matching process by combining element and structural level matchers.Selecting the optimal string similarity measure associated with its threshold is an important issue in order to enhance the effectiveness of the element level matcher, which in turn will improve the whole ontology system results.In this paper, we present POMap, an ontology matching system based on a syntactic study covering element and structural levels.For the element level matcher we have adopted the best configuration based on the analysis of the performances of many string similarity measures associated with their thresholds.For the structural level, we have performed a syntactic study on both subclasses and siblings in order to infer the structural similarity.Our proposed matching system is validated and evaluated on the Anatomy, the Conference and the Large Biomedical tracks provided by the benchmark of OAEI 2016 ontology matching campaign.

Many studies, in the literature, have affirmed a low level of user satisfaction concerning the understandability and readability of large-scale Linked Data visualizations offered by current available tools. This issue is especially problematic for inexperienced users. To address these requirements, we have extended our previous work Memo Graph, an ontology visualization tool, to provide a user-centered interactive solution for extracting and visualizing Linked Data. It takes aim to provide comprehensible and legible visualization. To manage scalability, it is built on an incremental approach to extract descriptive summarization from a given Linked Data endpoint where it becomes possible to generate a “summary graph” from the most important data (middle-out navigation approach). It offers user interfaces that reduce task complexity for users, especially the inexperienced ones. We tested Memo Graph on a number of Linked Data datasets with encouraging results. We discuss the promising results derived from an empirical evaluation, which affirmed that Memo Graph is useful in visualizing Linked Data and usable.

This paper introduces BPM4KI- a generic Business Process Meta-Model for Knowledge Identification, which encompasses a clear and semantically rich definition of Sensitive Business Processes (SBPs). This meta-model is well founded on «core» domain ontologies. It covers all aspects of business process modeling and knowledge management: the functional, organizational, behavioral, informational, intentional and knowledge perspectives. The aim of BPM4KI is to develop a rich and expressive graphical representation of SBPs in order to identify and localize the crucial knowledge that is mobilized and created by these processes. In this research work, we focus more specifically on the description of the « Functional Perspective», which represents the core dimension in SBP modeling, exploring the collaboration, interaction and knowledge aspects. Besides, we evaluate the relevance of some proposed concepts through a real SBP scenario from medical domain in the context of the organization of protection of the motor disabled people of Sfax-Tunisia (ASHMS).

The rapid development of digitization and data storage techniques resulted in images volume increase. In order to cope with this increasing amount of informations, it is necessary to develop tools to accelerate and facilitate the access to information and to ensure the relevance of information available to users. These tools must minimize the problems related to the image indexing used to represent content query information. In this paper, we present a new retrieval model called vectorization. The idea is to transform any similarity matching model (between images) to a vector space model providing a score. A study on several methodologies to obtain the vectorization is presented. Some experiments have been undertaken on Oxford5k and Inria Holidays datasets to show the performance of our proposed system.

Recently, ontologies have become more important in modern Semantic Web as they capture knowledge in a particular domain of interest. Indeed, they emphasize interoperability and establish a common shared understanding among the involved actors of web-based applications. Nevertheless, in parallel with the abundance of the proposed approaches for ontology learning, a related problem of the evaluation of such automatically generated ontologies is emerging in different domains. In the Arabic legal domain, a benchmark golden ontology is so necessary in order to assess the good quality of the (semi-)automatic learned ontologies. In this paper, we introduce CrimAr, a handcrafted ontology based on the top-levels of LRI-Core, to represent all relevant knowledge in the Arabic legal domain, especially the criminal matter. The use of CrimAr is also demonstrated in a real case evaluation.

The enormous exploitation of connected objects for patient monitoring becomes a source of a huge quantity of health data. This data can be badly expressed, understood, and exploited by other systems and devices. Additionally, the huge amount of connected objects embeds with resource-constrained devices (sensors, actuators, RFIDS, etc.) are also challenging. Consequently, semantic representation of both medical connected objects and their data becomes important. Furthermore, the semantic reasoning of this data requires the definition of diverse rules in order to provide adequate and efficient results. This paper suggests two main contributions. The first one focuses on the semantic representation of both the medical connected objects and their data by proposing a HealthIoT ontology. The second contribution is proposed to provide practical backup to the use of this ontology. In fact, an IoT Medicare system is proposed to evaluate the HealthIoT ontology.

Bid process translates the techno-economic expertise, which partners build in a cooperative way. It is a key business process which influences the company’s survival and strategic orientations. Therefore, the Bid Process Information System (BPIS) that supports this process must be characterized by integrity, flexibility and interoperability. Nevertheless, the urbanization approach, on which we rely to implement this BPIS, has to deal with “ three fit ” problems. To overcome these deficiencies, we propose our methodology to organize the BPIS following four dimensions: operational, organizational, decision-making, and cooperative dimensions. In this paper we are particularly interested at reduce the gap between business and technical infrastructures of the BPIS. Thus, we propose solutions to deal with “ vertical fit ” problems and to define the characteristics of the operational dimension. In this context, we illustrate that the ERP (Enterprise Resource Planning) implements the operational dimension of the BPIS. However, there is no specific module to the exploitation of the bid process. Thus, we propose to extend “OpenERP 7.0” by a new module that treats the techno-economic bid solutions. We also describe the characteristics of the organizational dimension of the BPIS.

Cloud computing represents a new computing approach that allows creating robust and on-demand services controlled by means of Service Level Agreements (SLA). These documents are generated following a process of negotiation during which the client chooses a subset of clauses among choices predefined by the provider on a "take it or leave it" basis. However, the heterogeneity between client requests and provider offers may result in inappropriate SLA. In addition, the cloud service context may change over the time which causes a client requirements adjustment and therefore, the SLA remains unsatisfactory. In this paper, we propose a cloud SLA negotiation and re-negotiation approach that integrate a semantic mapping between client requests and provider offers with the aim of generating a suitable SLA document. Moreover, our approach includes a context-aware system to adapt the SLA throughout reasoning techniques and automatically ensure the re-negotiation. A prototype implementation demonstrates the feasibility and the efficiency of our approach.

When creating social recommender systems, trust between various users in social networks emerges as an essential decisive feature. This paper aims at calculating trust among users by identifying all possible relations that may exist among those users and evaluate them. Although many models were proposed to analyze computational trust in different applications of social web, little importance is given to the time factor even by models that represent trust as a source of recommendation measurement. Emphasized in this paper is the temporal factor and its role in improving the accuracy of trust in social recommender systems. We propose to integrate the temporal factor in measuring trust between social network friends. A Trusted Friends' Facebook application is developed to demonstrate the importance of time in the users' interactions for determining social trusted friends. After that, this application will be used in a semantic social tourism recommender system as a smart e-tourism tool to motivate users to travel to Tunisia for medical purposes.

Ontologies are the main component of the semantic Web. Constructing large ontologies typically requires to reuse previously existing ones. Modularity is a key requirement for collaborative ontology engineering and for distributed ontology reuse on the Web. In this paper, we propose an approach to automatically compose ontology modules into a global ontology. Our approach is based on similarity measures computed against concept names, attributes and relationships. We also detail the algorithm allowing to merge the ontology modules into a global ontology.

In this paper, we present a new variant to show results of information retrieval process. Search results proposed here are in the form of textual abstract instead of returning a list of links pointing to relevant web documents. Our current work materializes collaboration between two independent axes: Semantic Web and Fuzzy Logic. The automatic abstract generation represents an extension of our previous works aimed to create an information retrieval system dedicated for kids.

Considering the various security constraints is a primordial task in software development. Dealing with security problems early enable us to not going further in the process and avoid rework. Extract-Transform-Load (ETL) processes are the back stage of data warehouse architectures. Securing the ETL processes development is highly important and helps in mitigating security defects. Defining the right set of security requirements constitutes one of the main challenges for the designer of ETL processes. To this end, we propose a GQVM approach for requirement analysis during ETL development. We focus on formulated designer requirements by analysing risks and propose a quantitative method of security measure in ETL processes.

Medical tourism is a domain rich of data that are stored in many hundreds of data sources. Many of these sources should be used during the development of tourism information systems. That is why, it is very important to create a referential model that represents the medical tourism in Tunisia. The e-tourism ontology provides a way to achieve integration and interoperability through the use of shared vocabulary and meanings for terms with respect to other terms. In this paper, we will create an ontology as a very expressive hierarchy of categories, populating with information from Tunisian medical tourism providers' services that represent all concepts and relations of the medical tourism in Tunisia. This ontology will be integrated in a trust based recommender system to deal with the lack of semantic information in personalized recommendation in tourism domain.

E-learning became an omnipresent concept in modern education thanks to the rapid development of Information and Communication Technology for Education. Along with this exponential evolution, the learner plays a central role in both traditional and computer-assisted learning. Accordingly, the learner's individual differences have a remarkable potential to improve his/her performance and motivation. This is ensured by providing an adaptive learning support. The present paper presents a survey of learner modeling in adaptive E-learning systems while describing the main methods for extracting of the personalization parameters. The learner's modeling does not only interest researchers and developers, but also pedagogues, course' designer and tutors.

Nowadays, social media present a valuable source for business decision support. This article outlines the integration of social opinion data in multidimensional design combining sentiment analysis techniques and ETL design to oifer a novel approach for social ETL design. The main contribution of this paper is the definition of a lexicon opinion analysis approach that extracts the sentiment polarity of informal text expressed in the Twitter social network. We propose a new algorithm, POLSentiment, based on lexical resources to firstly extract opinion words and emoticons from the tweet and then detect its positive or negative polarity. To assess the performance of the proposed algorithm, we evaluate POLSentiment on Sanders dataset. The results show that the proposal is suitable to automate the whole polarity analysis process, providing high accuracy levels and low false positive rates. Additionally, we define ETL processes design that consists of extracting tweets, its preprocessing, opinion analysis and polarity classification, then its loading into the Social Data Webhouse.

Ontology matching requests to find correspondences between entities of two semantically related ontologies. several approaches have been proposed for ontology matching. The most of those approaches are based on similarity measurements. consequently, they can detect only the equivalence relations between entities and do not take into account the asymmetric relations such as the subsumption. In addition, complex matching was very few studied for ontologies matching. In this paper we presents an extensional and asymmetric matching approach capable of identifying complex correspondences, an extremely difficult task in ontology matching. In this approach, we use the association rule to discover implicative and conjunctive mapping containing complex matching.

Big Data are collections of data sets so large and complex to process using classical database management tools. Their main characteristics are volume, variety and velocity. Big Data integration is a new research area that faces new challenges due to these characteristics. Ontologies represent knowledge as a formal description of a domain of interest. They are widely used in data integration. This paper illustrates an approach for ontology based Big Data integration taking into account their characteristics. Our approach is based on a NOSQL database namely MongoDB and modular ontologies. It follows three steps: wrapping data sources to MongoDB databases, generating local ontologies, composing the local ontologies to get a global one. A tool implementing the generation of the local ontologies is also detailed.

This paper presents a user-friendly tool, called MEMO GRAPH, for visualizing and navigating ontologies. Compared to related work, MEMO GRAPH is designed to be used by everyone, including ontology experts and users not familiar with ontologies. It provides an accessible and understandable user interface that follows the “design-for-all” philosophy. Precisely, it offers an Alzheimer's patients-friendly interface. The MEMO GRAPH ontology visualization tool is integrated in the CAPTAIN MEMO memory prosthesis and it is applied for visualizing a small-scale ontology (PersonLink) and a large-scale ontology (DBpedia). We discuss the encouraging results derived from the preliminary empirical evaluation, which confirms that MEMO GRAPH is an intuitive and usable ontology visualization tool.

Driven by the ever-growing of data from social network (SN), data warehouse (DW) approaches must be adapted. Generally the star, snowflake or constellation models are used as logical ones. All these models are inadequate when dealing with social data which need scalable and flexible systems. As an alternative, NoSQL systems begin to grow. In the absence of a clear approach which allows the implementation of data warehouse under NoSQL model, we propose in this paper, new rules for transforming a multidimensional conceptual model into two NoSQL ones: column-oriented and documentoriented models. For each model, we distinguish two types of transformation: simple and hierarchical. To validate our transformation rules, we implemented four data warehouses using Cassandra as a column-oriented NoSQL system and MongoDB as document-oriented NoSQL system. These systems were implemented using java routines in Talend Data Integration tool and evaluated in terms of “Write Request Latency” and “Read request Latency” using TPC-DS benchmark.

Dealing with unstructured information is currently a hot research topic since most documents exist in an unstructured form. The effective exploitation of unstructured document, although intricate, is of paramount importance to Information Retrieval (IR). The key to using unstructured data set is to identify the hidden structures within the data set. In this paper, we present an approach to recognize the semantic structure of documents in Arabic legal data. Several main concepts of a document are expressed in this structure, which includes title, the headings of the chapters, sections, subsections, etc. This structural information is employed to obtain a richer and more fine-grained annotation of documents forming a useful and coherent infrastructure ready for IR. Some experiments were conducted in order to evaluate our approach. The initial results seem promising.

Nowadays, e-learning offers advantages over traditional learning in terms of independence. Moreover, adaptive e-learning systems take into account learner's profile, such as learning style and level of knowledge, in order to provide the most appropriate learning object. However, the essential challenge is finding and identifying the learning objects from a big corpus while ensuring their independence in different contexts. To overcome these problems of interoperability and accessibility of learning objects, the authors proposed to define a learning semantic Web service for each learning object. This service is an extension of OWLS that encompasses the description of the learning intention and the use of context that characterize a learning object. In this paper, the authors propose a new discovery mechanism based on learning intention and context use guided by the learner's intention and profile in order to offer a personalized learning path. Experimental results prove the efficiency of the proposed approach and approve its notable contribution.

In Opus project, basic requirements of data-driven processes were supported by an advanced framework. In this paper, we elucidate a proof-of-concept prototype implementing the fundamental concepts of the Opus framework.

We propose here a novel approach for extracting hidden semantic from semi-structured data resources and transformed to RDF triples, to be queried through semantic query languages. Unlike existing approaches that only explore the structure or use ontologies, we present a system that allows us to utilize all available information. Our approach constructs a semantically related data from XML represented in RDF via “semantic network” described here. These data could be queried by SPARQL/XQUERY or both simultaneously in multimodal way to perform a semantic search on a set of multimedia news resources.

In the context of applications development for personalized information retrieval, there is an increasing need for addressing diverse users' interests and requirements in a specific domain with appropriate user tailored data. Consequently, a user desires to obtain information suitable for his entered queries. It is worth mentioning that Social Network use has emerged due to emerging devices for new technologies and advanced Web techniques. In order to answer this need, we propose to enrich our user model ontology for handicraft domain by FOAF ontology. In this paper, we propose an approach for user model ontology construction. Then, we propose two algorithms for matching and merging ontologies so as to achieve the enrichment with FOAF. We define then some bridge axioms. We illustrate the applicability of our approach through a concrete example.

In the present work, we presented a new approach to provide learners with learning paths adapted to their requests. These courses were generated by the composition of the learning semantic Web services. Our approach defined a learning Web service (WS) for each learning object (LO) to overcome the problems of interoperability and accessibility of learning objects. Each WS was represented, in the directory, by a learning semantic Web service (OWLS-LO) to describe a semantic understanding of the object being represented. This semantic approach was described by ontologies.

Big Data are collections of data sets so large and complex to process using classical database management tools. NOSQL databases are in the base of storage of Big Data. Learning ontology from these databases will extract their hidden semantics. This paper illustrates an approach to learn ontology from document oriented NOSQL database. We choose to deal with MongoDB. We present main steps of our approach and detail transformation rules.

Legal information presents a need for every citizen of a State subject to the rule of law or for any organization, legal entity recognized as a legal actor with rights and duties. The formalization of law through legal ontologies has proved their increasingly prominent role in representing, processing and retrieving legal information. Based on this knowledge, it is possible to represent and to make inferences about the semantic content of legal documents. This paper investigates the semi-automatic learning of an ontology from Arabic legal documents. We aim to exploit document properties in order to generate concepts and relations of the ontology. Some experiments were conducted in order to evaluate our approach. The initial results seem promising.

The use of ontologies in the Semantic Web allows for semantic query answering. The problem of answering queries in term of ontologies defined using Description Logic formalisms suffers from high worst-case complexity. The problem becomes more complex with fuzzy ontologies as fuzzy DLs should allow human-like expression and approximate reasoning. Many approaches for query rewriting have been proposed for enhancing crisp query processing. Given a query Q, the most common approach to query rewriting is the use of the terminological level of the knowledge base in order to define a query Q' that can be correctly evaluated on the assertional level only. Query rewriting for fuzzy knowledge base is not well treated in the literature. In this paper, we discuss different aspects of query rewriting for fuzzy OWL2EL ontologies and we propose to use rule based reasoning and views to enhance this process.

How to find and identify learning objects according with the learner profile represents a main interest in the quality of learning process. Thus, using the paradigm of Semantic Web Services ensure the independence and reusability of learning object in a different context. In this paper, we propose an extension of OWLS that encompass the description of the learning intention and the context of use that characterize a learning object. We also describe the generic scenario of the publication and discovery process.

Understanding the evolution of dynamic network structures is an emerging and very interesting topic, which is motivated by several real applications in many scientific fields. In this article, we discuss the link prediction problem, which is one of the key issues in the analysis of social evolving networks. We propose a new hybrid approach to predict the connections in social networks. The approach is inspired from the particle swarm algorithm and is combined with supervised machine learning strategy into a hybrid system. The paper includes an experimental study using real world data sets to compare the proposed methods against other approaches. The obtained results show good performance and prove the effectiveness of the proposed method.

The huge need of users to obtain a better and reliable description highlights the growth of interest of movie description. But, the lack of semantic description always persists. This lacks is mainly due to the failure of the semantic descriptions use extracted from the content. In this context, we therefore concentrate efforts to propose a mechanism to overcome this problem which has not been well treated yet. In fact, the semiotic descriptions represent an important source of information of movie document. In the herein presented work, we achieve a description level that combines two types of analysis such as the statistic and essentially the semantic. It consists in using an open (manual) description in order to extract the semiotic description.

With the proliferation of textual data on the web, efficient access to relevant information to meet the user's needs has become an important problem in the information retrieval tasks. This problem is specially due to the short queries submitted usually by users to an information retrieval system to describe their needs. These systems have to complete the user needs with related terms in order to disambiguate the user query and better meet the user's needs. This paper presents a new method to define semantic relationships between terms of the relevant returned documents for a given query in order to improve the description of the user's needs, by expanding automatically the original query with related terms, and to improve the search results. Some experiments have been performed on the CLEF 2014 collection to show the effectiveness of our method.

Nowadays, Internet users are actually bombarded with huge amounts of data, which makes it impossible at times to reach what they exactly look for and what they need. That is astounding, so it's no wonder that we're now dealing with the issue of information overload. Personalization and recommender systems are now available to help users enhance their experience in trying to search and process relevant information easily and quickly. Web personalization techniques and services are very promising when it comes to alleviating the problem of information overload. We aim to propose, in this paper, an ontology-based approach to provide personalized search results for handicraft woman in which we extract knowledge about her such as personal information, preferences and interests then we represent the user profile model. Added to that, we enrich our contribution by exposing some implementation figures that present the applicability of our approach.

This paper presents the use of multiple hybrid systems for the recognition of isolated words from a large multi-dialect Arabic vocabulary. Such as the Hidden Markov models (HMM), Dynamic Bayesian networks (DBN) lack a discriminatory ability especially on speech recognition even if their progress is huge. Multi-Layer perceptrons (MLP) was applied in literature as an estimator of emission probabilities in HMM and proves it effectiveness. In order to ameliorate the results of recognition systems, we apply Support Vectors Machine (SVM) as an estimator of posterior probabilities since they are characterized by a high predictive power and discrimination. Moreover, they are based on a structural risk minimization (SRM) where the aim is to set up a classifier that minimizes a bound on the expected risk, rather than the empirical risk. In this work we have done a comparative study between three hybrid systems MLP/HMM, SVM/HMM and SVM/DBN and the standards models of HMM and DBN. In this paper, we describe the use of the hybrid model SVM/DBN for multi-dialect Arabic isolated words recognition. So, by using 67,132 speech files of Arabic isolated words, this work arises a comparative study of our acknowledgment system of it as the following: the use of especially the HMM standards leads to a recognition rate of 74.18%.as the average rate of 8 domains for everyone of the 4 dialects. Also, with the hybrid systems MLP/HMM and SVM/HMM we succeed in achieving the value of 77.74%.and 7806% respectively. Moreover, our proposed system SVM/DBN realizes the best performances, whereby, we achieve 87.67% as a recognition rate more than 83.01% obtained by GMM/DBN.

Recent developments in inference and learning in Dynamic Bayesian networks (DBN) allow their use in real-world applications is the first successful application of DBNs to a large scale speech recognition problem. Even if their progress is huge, those models lack a discriminatory ability especially on speech recognition such as the Hidden Markov models (HMM). In this paper, we present the performance of the hybridization of Supports Vectors machine with Dynamic Bayesian networks for Arabic triphones-based continuous speech. In fact, SVM are based on a structural risk minimization (SRM) where the aim is to set up a classifier that minimizes a bound on the expected risk, rather than the empirical risk. The best results are obtained with the proposed system SVM/DBN when we achieve 78.87% as the best recognition rate of a tested speaker. The speech recognizer was evaluated with ARABIC_DB corpus and performs at 8.04% WER as compared to 10.08% with triphones mixture-Gaussian DBN system, 10.54% with hybrid model SVM/HMM and 12.03% with HMM standards.

Under the different searches performed to analyzing social networks, much attention has been devoted to the problem of predicting links. It is a key technique in many applications such as recommendation systems which provide suggestions of potential links between nodes. Traditional link prediction methods use a single proximity metric. In this paper, we study link prediction as a supervised learning task where we try to combine multiple features as input data for classification. To improve the accuracy of prediction, we have been applying a select attributes algorithm. Experiments have been performed on two co-authorship data sets. Results demonstrate that Random Forest, k-nearest neighbors and Principal Component Analysis yield the best performances.

Identifying the Genre of an audiovisual document is among the major challenges for multimedia retrieval. Indeed, the lack of semantic metadata extraction makes these resources underused in the retrieval process. To overcome these difficulties, the extraction of semantic descriptions requires an analysis of the audiovisual document's content. The automation of the process of describing audiovisual documents is essential because of the richness and the diversity of the available analytical criteria. In this paper, we present a method that allows the identification of a semantic and automatic description from the content such as genre. We chose to describe the cinematic audiovisual documents based on the documentation prepared in the pre-production phase of films, namely synopsis. The experimental result on Imdb (Internet Movie Database) and the Wikipedia encyclopedia indicate that our method of genre detection is better than the result of these corpuses.

Visual information retrieval has become a major research area due to increasing rate at which images are generated in many application. This paper addresses an important problems related to the content-based images retrieval. It concerns the vector representation of images and its proper use in image retrieval. Indeed, we propose a new model of content-based image retrieval allowing to integrate theories of neural network on a vector space model, where each low level query can be transformed into a score vector. Preliminary results obtained show that our proposed model is effective in a comparative study on two dataset Corel and Caltech-UCSD.

The description of an audiovisual document represents a major challenge for multimedia retrieval. Indeed, the lack of descriptive metadata extraction makes these proliferated resources underused in the querying process. To overcome these difficulties, the extraction of the semantic metadata of the content and the different structures of an audiovisual document is required. In this paper, we present a method that enables an automatic description of audiovisual documents. This automatic process is mainly based on the use of a multitude of modalities for the description and modeling of audiovisual documents, the standardization of these descriptions in the MPEG-7 standard through its description definition language (DDL) and on the use of semantic web language to link between the audiovisual resources.

During the past decade, the advent of the social network has offered several platforms that promote communication among users on common spaces. Several efforts were devoted to unify the social network domain, particularly the scientific domain through introducing ontology-based modeling of scientific social network. However, the measurement of the researchers standings within the scientific community is generally absent. To overcome this drawback, we propose, in this paper, a scientific social network ontology which includes definitions of main entities and describes main attributes of : Scientific social network concepts aiming to share common understanding of this domain and to reflect the academic career paths.

In this paper, we present an approach to facilitate the annotation and the retrieval of the image documents. Our approach is based on the definition and the generation of semantic rules presented via the logic of predicates. Then, we proposed the classification of these rules by a method of clustering Fuzzy C-means. For this, we used the method of co-citation to calculate the similarity measure between images. This classification has grouped thematically these images with the aim to facilitate research and annotation. To validate our proposal, we implemented a tool tested on a set of images of the city center. Finally, we conducted a series of tests to evaluate our approach.

The present paper introduces an approach for image process research. It discusses work in progress and reports the current state of our approach, which comprises the extension of ontology by the semantic rules to facilitate and optimize research. We describe a method for automatic enrichment query SPARQL language.

The Semantic Web is an infrastructure that enables the interchange, the integration and the reasoning about information on the Web. In the Semantic Web, the resources are described using particular metadata called “Semantic annotations”. A semantic annotation is a particular case of annotation which refers to ontology. The Web content is, for the most part, subject to uncertainty or imperfection. If many extensions of ontology languages have been proposed to deal with fuzzyness on the Semantic Web, the problem of fuzzy ontology querying is not well treated. We propose in this paper a fuzzy extension of the RDFS model. We propose a new query language named FSAQL to query fuzzy RDFS semantic annotations. We define the syntax and the semantics of this language and the way to evaluate it.

This paper presents a framework for real-time database design that is able to support real-time database requirements such as time-constrained data and time-constrained transactions. It is based upon a real-time object-oriented data model in which each object encapsulates time-constrained data, time-constrained methods and concurrency control mechanisms. Our framework is composed of an UML profile for real-time database, a translator to object-relational model, and an UML CASE Tool.

A real-time database is a database in which both the data and the operations upon the data may have timing constraints. The design of this kind of database requires the introduction of new concepts to modelize both data structures and the dynamic behavior of the database. In this paper, we propose an UML2.0 profile, entitled UML-RTDB, allowing the design of structural model for a real-time database. One of the main advantages of UML-RTDB is its capacity to take into account real-time database properties through specialized concepts in rigourous, easy and expressive manner.

Crisp ontologies become less suitable in all domains in which the concepts to be represented have imprecise definitions. Fuzzy ontologies are developed to overcome this problem. Determining similarity relations among fuzzy ontology components is essential for many reuse subprocesses, for query rewriting as well as for fuzzy ontology modeling. Denning similarity relations within fuzzy context may be realized basing on the linguistic similarity among ontology components or may be deduced from their intensional definitions. The later approach needs to be dealt with differently in crisp and fuzzy ontologies. In the first case, two ontology components are related by a given similarity relation or not. In the second one, two ontology components have a degree to be related by a given similarity relation. This is the scope of this paper.

To contribute to the promotion of concurrency controllers for temporal databases, we propose in this paper to formally check the access concurrency control algorithm proposed using SPIN. This algorithm is based on the optimistic approach and must guarantee strong consistency for transaction time relations. SPIN provides a software model checking with a powerful tool to detect errors. It is an appropriate tool for analyzing the logical consistency of concurrent systems. The main target consists of retrieving and correcting blocking error type, on the one hand, and ensuring the validity of the considered system properties specified by temporal logic formulae, on the other hand

Summary form only given. Object-oriented frameworks are recognized as a promising technique for architectural reuse. To guide a framework reuse and help in the traceability of a model, we have proposed an UML profile for framework design, called F-UML. In addition, to provide for precise analysis and reuse validation, we defined a formal semantics for the class diagram of F-UML. In this paper, we complete the formalization of F-UML in object-Z. We, then, show how the formal semantics can be used to analyze both syntactic and semantic/domain specific properties of a framework, and to validate a framework reuse.

Summary form only given. The manual design of data warehouse and data mart schemes can be a tedious, error-prone, and time-consuming task. In addition, it is a highly complex engineering task that calls for methodological support. This paper lays the grounds for an automatic generation approach of multidimensional schemes. It first defines a tabular format for OLAP requirements. Secondly, it presents a set of algebraic operators used to transform automatically the OLAP requirements, specified in the tabular format, to data mart modelled either as star or constellation schemes. Our approach is illustrated with an example.

Frameworks offer reuse through the generality they have to encompass. This same property, however, often makes a framework design fairly complex, hard to understand and, hence, to reuse. This paper briefly presents the F-UML design. It then focuses on the definition of the formal semantics of F-UML. This latter is defined through a translation of the meta-model of F-UML to Object-Z. The Object-Z semantics allows a designer to prove the syntactic well-formedness of an F-UML design. In addition, it allows the verification of several design properties through a theorem prover.

Frameworks are a promising technology for software design reuse. However, their difficult design may impede their widespread use in software engineering. This paper presents a framework design process for F-UML, an object-oriented design language that offers graphical annotations to guide framework reuse. The F-UML design process is based on a stepwise, bottom-up unification by applying a set of comparison rules on various applications in the framework domain.

Distributed design of an information system consists in designing separately different parts of the system and in integrating the resulting models into a single one. To achieve the integration, similarities and conflicts between models are to be detected and resolved. This requires a precise representation of models which we cannot obtain unless we use a formal specification language. We propose to specify the conceptual representations in Object-Z and to determine semantic correspondences between model elements by considering three criteria: linguistic criterion attached to element names, structural criterion attached to object structure and dynamic criterion attached to object behavior. From time criteria, we formulate a set of integration rules which lead to fuse the models into a global one.

An approach towards harmonization of the large variety of existing information system modeling and specification techniques rather than standardization of single techniques is proposed. This approach provides a framework relating meta models in an open ordering and transformation scheme by means of a set of basic transformations.