ABSTRACT Social media imagery serves as a crucial data source for crisis responders to perceive the evolving crisis situations. The crisis‐related information extracted from these images can be used to enhance situational awareness and support decision‐making. However, such information provided by data‐driven methods is difficult to exploit by model‐driven systems, which are widely employed in crisis management practice. This information mismatch caused by the semantic gap undermines the value of social media images in crisis informatics. To address this problem, a metamodel‐powered framework for social media image processing is proposed to support crisis response. This framework integrates deep learning techniques, a disaster‐specific dataset, information transformation middleware, and a crisis‐oriented metamodel. By doing so, it provides ready‐for‐exploitation information, enabling crisis responders to effectively utilise social media image data. The proposed framework is demonstrated through a case study on the 2018 Aude heave precipitation event and further validated against four additional historical crises. The primary contribution lies in the development of a novel design artefact that follows the design science research paradigm. This study not only addresses the specific information mismatch issue but also offers generalizable design principles applicable to information systems facing similar challenges.

• This article proposes a new framework for social BPM supported by conversational agents. • The framework combines social interaction, intent detection, and process memory for collaborative process management. • Four specialized agents are defined to handle the process lifecycle. • This approach demonstrates how AI-based conversations improve participation, flexibility, and transparency in BPM. • A call for tenders use case illustrates the feasibility and benefits of the proposed framework.

This study enhances decision-making and production monitoring in aeronautical assembly lines, focusing on visualizing performance evolution using a KPI tree and varied aggregation methods. By representing trajectory in a three-dimensional space, it facilitates effective risk and opportunities detection, pattern recognition, and trend analysis. The aim is to contribute valuable insights for mitigating disruptions and improving production rates in the aerospace industry, leveraging immersive technologies for comprehensive understanding and advancing decision support systems.

The purpose of this study is to present a novel perspective on decision support based on the conventional SEIR pandemic model paradigm considering the risks and opportunities as physical forces deviating the expected performance trajectory of a system. The impact of a pandemic is measured by the deviation of the social system's performance trajectory within the geometrical framework of its Key Performance Indicators (KPIs). According to the overall premise of utilizing Ordinary Differential Equations to simulate epidemics, the deviations are connected to several alternative interventions. The model is essentially built on two sets of parameters: (i) social system parameters and (ii) pandemic parameters. The ultimate objective is to propose a multi-criteria performance framework to control pandemics that includes a combination of timely measures. On the one hand, the current study optimizes prospective strategies to manage the potential future pandemic, while on the other hand, it explores the COVID-19 epidemic in the state of Georgia (USA).

Most business continuity planning methods help provide adequate precautions in case of a crisis situation to keep the organization’s main activities running uninterruptedly in the moment of the crisis. One way to accurately improve the rules that arise from business continuity planning (BCP) would be to (i) proceed with the data multidimensions and multi-sources (physical, societal, financial, etc.) through a purpose-driven Information System (IS), (ii) build customized integral robustness indicators and (iii) use them to qualify the business continuity strategy. On this basis, this work’s findings make firms, particularly healthcare organizations, less sensitive to catastrophes by designing an interactive dashboard that pilots the business continuity indicators and highlights decisive components and resources.

Inventory Routing Problem (IRP) is a typical optimization problem in logistics. To reduce the total cost, which contains the product transportation cost, the inventory holding cost, the customer satisfaction cost, etc., a wide range of impact factors have to be taken into consideration. Since more and more intelligent devices have been adopted in the management of modern logistics, the amount of the collected data (relevant to those impact factors) increases exponentially. However, the quality of the collected data is suffering from a certain number of uncertainties, such as device status and the transmission network environment. Considering the volume and quality of the collected data, the traditional data‐driven distribution optimization methods encounter a bottleneck. In this paper, we propose a hybrid optimization method which combines data‐driven and knowledge‐driven techniques together. In our method, a domain ontology, which has better scalability and generality, is built as an extension of data‐driven optimization algorithms. Knowledge reasoning techniques are also combined to handle data quality issue and uncertainties. To evaluate the performance of our method, we carried out a case study, which is provided by a French company “Pierre Fabre Dermo‐Cosmetics” (PFDC). This case study is a simplified scenario of the practical business process of PFDC.

To offer high quality services to patients, hospitals try to get a perception of patients' processes. This research work aims at identifying a type of processes known as patients' pathways and to devise a framework which could support the task of discovering patients' pathways. Existing modeling languages for discovering patients' processes fail at extracting the value and nature of each activity relevant to the whole process. To fill the identified gap, this paper uses a new approach for visualizing patients' pathways as Operational Process Charts (OPC). To do so, Real-Time Location Systems (RTLS) have been used to extract the primary event logs which contain the data related to movements of patients. Next, we have designed a meta-model which can filter and analyze the RTLS event logs and identify the different elements for the construction of OPC's. This paper focuses on presenting the DIAG meta-model for interpretation of event logs and the transformation of these data into opc's.

Multi-organization collaboration, which allows partners focus on their core business, is becoming a trend. Besides the interoperability of each partner, the mechanism of selecting qualified and suitable partners is another key issue to guarantee the success of collaboration. Based on our previous work, this paper aims to provide a model-driven approach to solve the partners selecting problem in building collaboration. In this approach, a meta-model is defined to describe the context of collaboration. All the potential partners’ inputs shall be conformed to this meta-model. In order to select the required partners automatically, semantic checks are combined.

International audience

Over the recent years, Business Process Management (BPM) paradigm has become more socially driven. The socialization of processes has become an unavoidable way to realize flexible processes by including means to improve collaboration, knowledge sharing and collective decisions. However, the majority of current proposed approaches are limited to the exploitation of social technologies (social networks, blogs, wiki...), without providing a coherent conceptual and reusable framework, independent from those technologies. This paper defines precisely the social dimension to be taken into account in BPM and recommends a set of models to structure the design and development of Social BPM. In addition, a social ontology has been developed with Protege 5.0 and rules for inferring knowledge and queries for exploiting it have been implemented with SWRL and SPARQL.

Several standards have been defined for enhancing the efficiency of B2B web-supported collaboration. However, they suffer from the lack of a general semantic representation, which leaves aside the promise of deducing automatically the inter-enterprise business processes. To achieve the automatic deduction, this paper presents a social platform, which aims at acquiring knowledge from users and linking the acquired knowledge with the one maintained on the platform. Based on this linkage, this platform aims at deducing automatically cross-organizational business processes (i.e. selection of partners and sequencing of their activities) to fulfill any opportunity of collaboration.

Web service composition, as one of the key aspects in web service domain, has attracted more and more research attentions. Generally, in order to provide a powerful function to a specific problematic, several web services should combine and work together. Such a collaboration of web services is regarded as web service composition. There are two main difficulties in web service composition: selecting web services as partners and making interactions among these web services. A web service works as a functional black box, it takes in inputs and generates outputs. For a specific web services, both the inputs and the outputs are in specific formats. In order to make interactions among web services, it is necessary to be synergistic among their inputs and outputs. To generate specific inputs for a particular web service, the outputs from one or several other web services should transform the formats and combine together. This paper presents an automatic model transformation methodology, which focuses on transforming and combining outputs to generate inputs for web services. This automatic model transformation methodology regards all web services' inputs and outputs as models. In order to do the transformation and combination process efficiently and effectively, syntactic checking and semantic checking measurements have been combined into a refined model transformation process.

Modelling and model transformation are regarded as two pillars of model-driven engineering; they have been used together to solve practical problems. For instance, since different models (e.g. data model) are used by heterogeneous partners involved in a specific collaborative situation, there is an urgent need for model transformations to exchange information among the heterogeneous partners. To quickly define model transformations, this paper presents an approach, which could replace the users' effort in making mappings during the definition of a model transformation process. This approach is based on model transformation methodology, using syntax and semantic relationship among model elements. For this, a generic meta-metamodel and semantics checking methodology are proposed, before being illustrated by an example.

This paper presents our solution to build an adaptive information system to support a crisis response. The Characteristics of crisis response bring us to choose a solution based on a mediator, named Mediation Information System. Unfortunately, crisis is an evolutionary phenomenon. Consequently, our information system has to evolve with the situation. For this, we propose an adaptation service. This adaptation service allows us on the one hand to deduce the evolution of the situation and then the new configuration of the information system to ensure it compliance with the solution, on the other hand, to realize this new configuration on-the-fly.

In order to support the design of complex systems such as optronic systems, and especially the function-processing oriented part of this work, we propose a method based on a connection of stages. This is aimed at making the representation of the system and its properties both formal and checkable. These formalizations can be used to manage the real verification of the system and determine the agreement between its model and the expected properties. The principle is to follow the first steps (corresponding to the design phase) of the traditional V-cycle of system development, and to enrich each step with formal features: the first step is the elaboration, based on initial requirements, of a function processing oriented specification. The first result is then a formalized (functional and non-functional) requirements set. It allows the system designer to construct, using the generic model and properties inventory, the precise model of the system (i.e. an instantiated model, called MOTI), and the list of properties to be verified. This MOTI is then (automatically) translated into a so-called MOTIF expressed in a formalism dedicated to formal proof (required for formal verification and validation processes). Concurrently, the expected properties (which have been extracted from the generic properties inventory enriched by specific characteristics such as user-defined values or application-specific parameters), are translated into the accurate formalism for automated proof.