Néjib Moalla

QoS ontology for service selection and reuse

Web service technologies become popular in software development in all sectors. Variety services related standards are defined but they still have limitations to represent the services such as the service registry does not support the Quality of Service (QoS) properties, web service description language does not allow specifying the QoS properties and there is no common ontology’s structure to store services. Our research aims to enhance the representation of services to assist the service selection and composition process in order to reduce the development costs. The existing resources are analyzed to define a web service ontology (WSOnto). Furthermore, a service selection algorithm is proposed to validate the proposed WSOnto. It considers the multiple criteria of inputs such as: context, functional and non-functional properties of services. The WSOnto and a service selection algorithm are studied to assist the re-engineering of business processes from users’ designed business processes.

Performance assessment architecture for collaborative business processes in BPM-SOA-based environment

To be competitive and flexible, companies engage in collaborations to develop and share their competences in order to cope with the dynamic environment. Collaborative business process evaluation helps to reflect the actual functioning of business process and their performance level. In this perspective, research in assessing collaborative business process performance presents relevant guidelines in order to adapt IT solutions when business requirements evolve.In this paper, we present an analysis and assessment approach for collaborative business processes in the service-oriented architecture in order to maintain their performance in competitive markets. Our approach proposes an evaluation method using execution traces of business process combined with a high-level assessment method using key performance indicators. Our main objectives are to track the execution of collaborative business process and to analyze the performance trajectory of a business process regarding the business performance level. To collect and structure the performance knowledge (execution and measurement), we create an ontological model-based knowledge repository in order to enrich the semantics of an evaluation business process. The precise track of execution data in our approach is able to identify events that disrupt the proper functioning of processes at the runtime. From an industrial case study, we can conclude that our ontological approach can target the performance assessment of collaborative business processes effectively.

Towards ensuring Satisfiability of Merged Ontology

Abstract The last decade has seen researchers developing efficient algorithms for the mapping and merging of ontologies to meet the demands of interoperability between heterogeneous and distributed information systems. But, still state-of-the-art ontology mapping and merging systems is semi-automatic that reduces the burden of manual creation and maintenance of mappings, and need human intervention for their validation. The contribution presented in this paper makes human intervention one step more down by automatically identifying semantic inconsistencies in the early stages of ontology merging. Our methodology detects inconsistencies based on structural mismatches that occur due to conflicts among the set of Generalized Concept Inclusions, and Disjoint Relations due to the differences between disjoint partitions in the local heterogeneous ontologies. We present novel methodologies to detect and repair semantic inconsistencies from the list of initial mappings. This results in global merged ontology free from ‘circulatory error in class/property hierarchy’, „common class/instance between disjoint classes error’, ‘redundancy of subclass/subproperty relations’, ‘redundancy of disjoint relations’ and other types of „semantic inconsistency’ errors. In this way, our methodology saves time and cost of traversing local ontologies for the validation of mappings, improves performance by producing only consistent accurate mappings, and reduces the user dependability for ensuring the satisfiability and consistency of merged ontology. The experiments show that the newer approach with automatic inconsistency detection yields a significantly higher precision.

Dynamic Execution of a Business Process via Web Service Selection and Orchestration

Dynamic execution of a business process requires the selection and composition of multiple existing services regardless of their locations, platforms, execution speeds, etc. Thus web service selection appears as a challenging and elusive task especially when the service task has to be executed based on user requirements at the runtime. This paper presents our Semantic-Based Business Process Execution Engine (SBPEE) for the dynamic execution of business processes by the orchestration of various exposed web services. SBPEE is based on our designed Project Domain Ontology (PrjOnt) that captures user specifications and SWRL rules which classify the user specification into a specific category according to the business logic and requirements of an enterprise. Based on this classification of the user project and requirements, our semantic engine selects web services from the service repository for the dynamic execution of a business process. SBPEE matches functional requirements of a web service and required QoS attributes to identify the list of pertinent candidate services to fulfil the complex business process transactions. Finally, we present our case study on Create Order business process that aims at creating an order for the customer by following various web services for its task completion.

Production Data Integration in the Vaccine Industry

Data integration between information systems presents nowadays some research topics strongly approached by process industries and delivers increasingly sophisticated works and techniques. The pharmaceutical industry and especially the vaccine field are strongly interested by these outputs in order to better integrate data in their supply chain. With a complex product in the design, the form and the definition, this paper aims at proposing an approach to integrate diverse data in the production information system of the vaccine industry. Our scope highlight the importance of information flows and our contribution consist in proposing a transverse approach to track product information throughout its lifecycle and detect diverse entities affecting the production stage. From identified information systems, we collect diverse data with potential impact in the production information system. Using collected information, we aim (i) to ensure technical product data compliance, (ii) to better evaluate and integrate demand and forecast according to market trend, (iii) and finally to ensure a better data integrity to serve global business optimization by an estimation of production cost.

Weaving Trending, Costing and Recommendations Using Big Data Analytic: An Enterprise Capability Evaluator

In the life cycle of a business organization, unprecedented turmoil and turbulent timings are inevitable. This ever green truth is not only an advice but a motivation for every organization to be dynamic in its business plans. In other words, “one size fits all” strategy is short lived and seasonal for any enterprise. We in this study have proposed a dynamic platform capable of handling the challenges which are geared towards conventional stagnant business strategies in commercial world. The technical specification of the proposed platform is based on modern business intelligence tools and techniques. The study is dedicated towards exploration of investigating the Big Data capabilities to develop a platform delivering costing, sourcing and tendering capabilities for industrial parts. While doing so, we have emphasised challenging questions of interoperability in the design paradigm of inundation of structured and semi-structured data.

A Framework to Optimize Production Planning in the Vaccine Industry

In the literature, production planning optimization works are widely approached by mathematical researches to integrate more data and constraints toward delivering more reliable plans. In the particular context of vaccine industry, the vaccine product is a molecular substance with diverse definitions and presentations that involve with the closely coordination of many actors in the company. When it is difficult to support planning process by optimization solution, our contribution in this paper consists of proposing a production planning framework to structure some data integration issues according to different planning levels. With the correspondence of a better data management among production planning process, we aim to decline some best practices to provide more stable and reliable plans.

Toward Data Compliance in Vaccine Industry: Interoperability to align Business and Information Systems

The ultimate goal in the vaccine industry is product quality. However this quality can be altered by the use of a number of heterogeneous information systems with different business structures and concepts along the lifecycle of the product. Interoperability concepts may help to guarantee a certain correspondence and compliance between different product data. In this paper we focus on a particular compliance problem, between production technical data, represented in an ERP, and the corresponding regulatory directives and specifications, represented by the Marketing Authorizations (MA). The MA details the process for vaccine manufacturing according to the requirements imposed by health organizations such as Food and Drug Administration (FDA) and Committee for Medicinal Products for Human use (CHMP).

A Business Collaborative Decision Making System for Network of SMEs

The enterprise collaboration has gain much popularity and strength with the inception of digital revolution. The concept of enterprise collaboration has observed a dynamic and evolving phenomenon of value added chain. This phenomenon under the convergence of information technology has placed a remarkable impact on decision-making processes within enterprises. The enterprises are involved in establishing a common window of collaborative network where the principle enterprise decides the synthesis of the incoming opportunity. In this study, we have shown how the decision-making capability can be improved by means of analytics of vast amount of data during enterprise collaboration. The proposed system has adopted prescriptive analysis across enterprise resources. The outcome of proposed system addresses the individual and collaborative enterprise capability within enterprise network. Our proposed knowledge based decision making model provides a self-adaptive solution for enterprise collaborative services.

Performance monitoring framework for service oriented system lifecycle

Service oriented systems are highly dynamic systems composed of several web services. One of the most important challenges in service oriented systems is to deliver acceptable quality of service. For this purpose, it is required to monitor quality of service along different activities of service oriented system. Existing research focuses on specific activities but do not take into account all the activities of service oriented system together at the infrastructure level. In this paper, we present performance monitoring framework to provide support for the whole service oriented system lifecycle. Our framework integrates several ontologies to monitor the performance of service oriented systems in order to ensure their sustainability. We design a base Service Monitoring Ontology that captures all the information about the service domain. Along with that we design ontologies for technical indicators at service level, binding level, composition level and server level. We conduct a performance evaluation over real web services using suitable estimators for response time, delay, loss and more.