Vers un management de la continuité d'activité dirigé par les modèles : application à la prise en charge à domicile

Colloque avec actes et comité de lecture. internationale.International audience"Les technologies d’information et de communication sont reconnues comme des éléments inévitables pour améliorer les pratiques métiers. Elles sont même devenues des éléments déterminants dans la faisabilité dans de secteurtel que la prise en charge à domicile. Cependant, ces organisations évoluent dans un environnement très dynamique et incertain.Au regard de perturbations, endogènes et exogènes, auxquelles sont confrontées les organisations, elles se doivent de réagir de manière agile aux aléas et de faire preuve de résilience. Le management de la continuité d’activité (MCA) estune approche de management des organisations répondant à ces attentes. Notre motivation est la définition d’un cadre méthodologique permettant de mettre en oeuvre le management de la continuité d’activité au sein d’un système sociotechnique.Ce cadre repose sur (i) la définition et la conception d’un méta-modèle de référence fondé sur l’intégration du management de la continuité d’activité dans l’ingénierie d’entreprise et (ii) la définition d’un langage de modélisationétendu aux concepts du MCA. Un cas d’étude du domaine de la prise en charge à domicile vient illustrer le bien fondé de l’application de ce cadre méthodologique sur une base réelle et concrète.

Formal Specification, Implementation, and Evaluation of the AdoBPRIM Approach

Modeling is one of the fundamental aspects of Risk-aware Business Process Management. The conceptualization of new modeling approaches needs to integrate all abstraction layers of risk and business process concepts and requires a highly specialized knowledge in conceptual modeling foundations and formal specification of meta-models. This paper introduces a risk-aware business process modeling approach based on the BPRIM method. In order to comprehensively and unambiguously specify the proposed approach, we revert to the FDMM formalism. Furthermore, a corresponding software prototype called AdoBPRIM has been implemented using the ADOxx meta-modeling platform to assess the technical feasibility of the approach. The usability of the tool has been empirically evaluated and a healthcare process-based example is presented as a proof-of-concept. We show that the AdoBPRIM approach enables Risk-aware Business Process Management with an excellent usability. In summary, this paper constitutes a best-practice for formally specifying, technically implementing, and empirically evaluating modeling method conceptualizations

A Semantic Rule-based Approach Supported by Process Mining for Personalised Adaptive Learning

Currently, automated learning systems are widely used for educational and training purposes within various organisations including, schools, universities and further education centres. There has been a big gap between the extraction of useful patterns from data sources to knowledge, as it is crucial that data is made valid, novel, potentially useful and understandable. To meet the needs of intended users, there is requirement for learning systems to embody technologies that support learners in achieving their learning goals and this process don’t happen automatically. This paper propose a novel approach for automated learning that is capable of detecting changing trends in learning behaviours and abilities through the use of process mining techniques. The goal is to discover user interaction patterns within learning processes, and respond by making decisions based on adaptive rules centred on captured user profiles. The approach applies semantic annotation of activity logs within the learning process in order to discover patterns automatically by means of semantic reasoning. Therefore, our proposed approach is grounded on Semantic Modelling and Process Mining techniques. To this end, it is possible to apply effective reasoning methods to make inferences over a Learning Process Knowledge-Base that leads to automated discovery of learning patterns or behaviour

Semantic-Based Model Analysis Towards Enhancing Information Values of Process Mining: Case Study of Learning Process Domain

Process mining results can be enhanced by adding semantic knowledge to the derived models. Information discovered due to semantic enrichment of the deployed process models can be used to lift process analysis from syntactic level to a more conceptual level. The work in this paper corroborates that semantic-based process mining is a useful technique towards improving the information value of derived models from the large volume of event logs about any process domain. We use a case study of learning process to illustrate this notion. Our goal is to extract streams of event logs from a learning execution environment and describe formats that allows for mining and improved process analysis of the captured data. The approach involves mapping of the resulting learning model derived from mining event data about a learning process by semantically annotating the process elements with concepts they represent in real time using process descriptions languages, and linking them to an ontology specifically designed for representing learning processes. The semantic analysis allows the meaning of the learning objects to be enhanced through the use of property characteristics and classification of discoverable entities, to generate inference knowledge which are used to determine useful learning patterns by means of the Semantic Learning Process Mining (SLPM) algorithm - technically described as Semantic-Fuzzy Miner. To this end, we show how data from learning processes are being extracted, semantically prepared, and transformed into mining executable formats to enable prediction of individual learning patterns through further semantic analysis of the discovered models

PROCESS MODELS DISCOVERY AND TRACES CLASSIFICATION: A FUZZY-BPMN MINING APPROACH.

The discovery of useful or worthwhile process models must be performed with due regards to the transformation that needs to be achieved. The blend of the data representations (i.e data mining) and process modelling methods, often allied to the field of Process Mining (PM), has proven to be effective in the process analysis of the event logs readily available in many organisations information systems. Moreover, the Process Discovery has been lately seen as the most important and most visible intellectual challenge related to the process mining. The method involves automatic construction of process models from event logs about any domain process, and describes causal dependencies between the various activities as performed within the process execution environment. In principle, one can use process discovery to obtain process models that describes reality. To this end, the work in this artcle presents a Fuzzy-BPMN mining approach that uses training events log representing 10 different real-time business process executions to provide a method for discovery of useful process models, and then cross-validating the derived models with a set of test event logs in order to measure the accuracy and performance of the employed approach. The method focuses on carrying out a classification task to determine the traces, i.e. individual cases that makes up the test event logs in order to determine which traces that can be replayed by the original model. Thus, the paper aim is to provide a technique for process models discovery which is as good in balancing between “overfitting” and “underfitting” as it is able to correctly classify the traces that can be replayed (i.e allowed) or non-replayable (disallowed) by the model. In other words, the study shows through the Fuzzy-BPMN replaying notation and the series of validation experiments - how given any classified trace (for the test events log) and discovered process model (the training log) it can be unambiguously determined whether or not the traces found can be replayed on the discovered model

Plas'O'Soins: a software platform for modeling, planning and monitoring homecare activities

International audienceDemographic changes in recent years have contributed to a shift in care models, with the development of homecare as a new alternative to traditional hospitalization. We present a software platform dedicated to the modeling, planning and monitoring of homecare workflows, developed in the framework of the French research program TecSan. The platform is used on the desktop by care coordinators, and on the go by care workers using mobile devices

Définition d'un modèle de propriété et proposition d'un langage de spécification associé : LUSP

MONTPELLIER-BU Sciences (341722106) / SudocEVRY-BU site Pelvoux (911822201) / SudocSudocFranceF

Modélisation et simulation des appels téléphoniques d'un service d'aide médicale d'urgence (SAMU 81)

International audienc

Development of a Risk-aware Business Process Modeling Tool for Healthcare processes

International audienceHealthcare organizations are environments of high management complexity and are subject to risk. Indeed, risk management is one of the most relevant aspects put forward in the literature which highlights the necessity to perform comprehensive analyses intended to uncover the root causes of risks. However, the healthcare sector still suffers from a lack of attention in this context, especially with regard to the establishment of risk management and process-oriented management, which is the motivation for the study described in this paper. In light of these observations, it would be essential for healthcare organizations to explore new risk management approaches. Contributing to this field, the present paper applies a risk-aware business process management method to work out a systemic methodology to study risks impacting healthcare processes. This framework aims to improve healthcare organizations’ maturity towards risk management. A case study related to the management of potential risks in a given healthcare process shall illustrate the usage of the developed framework