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Predictive process monitoring approaches aim to make predictions about the future behavior for running instances of business processes, such as next activity or remaining time. Most of these approaches use single object type event logs as if the business process is operating in isolation. Whereas, in an organization, several instances of different processes related to a set of objects can be executed at the same time and may interact with each other. This paper investigate the use of objectcentric event logs as they offer information about events and their related objects, allowing access to a global view about the running processes in an organization. We propose an object-centric predictive approach considering interactions between different object types. The proposed approach is evaluated on a publicly available object-centric log. The analysis of the results shows that using additional features (i.e., several object types' information) can generally help increase prediction performances.

Process innovation is assumed to require a more intrinsic rethinking of business processes, which is typically a creative process. Nevertheless, in this creative, prolific process, there can be artifacts derived from rational practices that are capable to provide insightful recommendations. In this work, the authors claim that an event log, a file that registers the execution of the relevant business processes, can be the source of such an artifact. They describe the fundamental elements of two problem formulations, namely the set of alternatives; the set of potential actions that the decision-maker may undertake; the set of points of view (dimensions) from which the potential actions are observed, analyzed, evaluated, compared, etc.; and the problem statement (what is expected to be done with the alternatives) for two cases.

Data as a Service (DaaS) is seen as a promising cloud offering for wrangling the overload of information and making it available across cloud platforms anytime and anywhere. While there exist a large number of DaaS providers in the market, each one has a different way to describe its provided services as well as supplied datasets. The lack of a well-defined machine-readable model strongly hinders the automatic selection and composition of DaaSs. This paper presents MoDaaS, a model-driven framework for the modeling and the description of DaaS services. MoDaaS enables DaaS providers to describe their services capabilities and concerns according to a shared ontology, thereafter it enables them to automatically generate service views in order to assist the integration and data exchange between heterogeneous services.

We describe an approach that is able to discover business process activities from emails. In addition, for each activity type, we extract metadata such as the roles of the people exchanging the email, type of the attached documents, or the domains of the mentioned links.

Flexibility and adaptability have been praised and empirically validated as critical for an organization's performance. They are associated with the number of different behaviors that the organization exhibits, as well as with their level of sophistication, which in turn implies the number and the quality of the available knowledge and capabilities in place. In this work, we propose the creation of a bipartite network, involving organizations and behaviors, through an event log, to capture the prospects of both organizations and behaviors for process innovation. By using the Method of Reflections, we claim that the structure of that network can expose a set of sophistication metrics, that eventually suggest i) the potential of behaviors for better organizational performance; ii) the most reachable innovation paths; iii) an "order of significance" over the behaviors considering their improvement contribution power. We show the practical potential of our approach through a Proof of Concept based on a real dataset.

Data lakes have recently emerged as an alternative solution to costly traditional data warehouse solutions. To exploit data lakes, however, there is a need for means that assist users in combining and integrating data stored within a data lake. In this paper, we position ourselves in the recurrent context where a user has a local dataset that is not sufficient for processing the queries that are of interest to him/her. We show how data lakes, or more specifically the service lakes, since we are focusing on data providing services, can be leveraged to answer user queries, taking into account the quality of the services and respecting the (time and monetary) budget set by the user.

Certain business environments, like health-care or customer service, host complex and highly variable business processes. In such situations, we expect fluctuating process behavior, which is difficult to attribute to specific causes, at least automatically. This work aims to provide process analysts with an additional tool to discover factors that affect the process flow. To this end, we propose a three-stage methodology to deal with the several challenges of this goal. Adhering to the process mining paradigm that suggests for evidence-based process analysis and improvement, we introduce a horizontal partitioning approach to identify elements of process behavior during the first stage. Then, during the second stage, we discuss how log manipulations can yield characteristics that reflect various perspectives of the process. Finally, we propose a multi-target feature evaluation step to deliver insights about the associations between characteristics and process behavior. The proposed methodology is designed to tackle challenges related to the general correlation problem of process mining, like dealing with general process behavior (not just local decisions) and relaxing the independence assumption among the elements of behavior. We demonstrate our approach step by step through a case study on a real-world, open dataset.

Web service discovery consists in identifying suitable existing services that satisfy specific goals or user requirements. Web service discovery can be a hard task because of the lack of explicit and semantic information for apprehending what services really do. In this article, we propose a service description enrichment approach based on I/O relations for improving Web service discovery. Our process is divided into two parallel steps. The first step consists in extracting existing relations between I/O of the services from the underlying ontologies using SPARQL, while the second step concerns the extraction of services’ I/O relations from the text descriptions of services using NLP techniques. Matching the I/O relations extracted by the two steps is applied in order to enrich the initial service description, allowing a more accurate automatic service discovery. This article presents our approach that uses dependency grammar and word2vec as well as our experimental results on OWLS-TC.

Email is a reliable, confidential, fast, free and easily accessible form of communication. Due to its wide use in personal, but most importantly, professional contexts, email represents a valuable source of information that can be harvested for understanding, reengineering and repurposing undocumented business processes of companies and institutions. Few researchers have investigated the problem of extracting and analyzing the process-oriented information contained in emails. In this paper, we go forward in this direction by proposing a new method to discover business process instances from email logs that uses unsupervised classification techniques. The approach is composed of two clustering steps. The first one uses a powerful semantic similarity measurement method, Word2vec, while the second one uses a similarity measure combing several email attributes. Experimental results are detailed to illustrate and prove our approach contributions.

In the Big Data era, companies are moving away from traditional data-warehouse solutions whereby expensive and time-consuming ETL (Extract-Transform-Load) processes are used, towards data lakes, which can be viewed as storage repositories holding a vast amount of raw data. In this paper, we position ourselves in the recurrent context where a user has a local dataset that is not sufficient for processing the queries that are of interest to him. In this context, we show how the data lake, or more specifically the service lake since we are focusing on data providing services, can be leveraged to enrich the local dataset with concepts that cater for the processing of user queries. Furthermore, we present the algorithms we have developed for this purpose and showcase the working of our solution using a study case.

Due to its wide use in personal, but most importantly, professional contexts, email represents a valuable source of information that can be harvested for understanding, reengineering and repurposing undocumented business processes of companies and institutions. Few researchers have investigated the problem of extracting and analyzing the process-oriented information contained in emails. In this paper, we go forward in this direction by proposing a new method to discover business process activities from email logs. Towards this aim, emails are grouped according to the process model they belong to. This is followed by sub-grouping and labeling the emails of each process model into business activity types. These tasks are applied by deploying an unsupervised mining technique accompanied by semantic similarity measurement methods. Two representative similarity measurement methods are examined: Latent Semantic Indexing (LSA) and Word2vec. These methods are compared to prove that Word2vec provides a better performance than LSA in grouping emails according to what process model they are related to, and in discovering emails belonging to the same activity type. Experimental results are detailed to illustrate and prove our approach contributions.

Editorial about the special issue based on best papers from ICSOC 2015: Frontiers and future of services research.

LNCS n°9341; International audience; The Linked Open Data (LOD) cloud is a massive source of data in different domains. However, these data might be incomplete or outdated. Furthermore, there are still a lot of data that are not published as static linked data such as sensors data, on-demand data, and data with limited access patterns, that are in general available through web services. In order to use web services as complementary sources of data, we introduce LIDSEARCH (Linked Data and Services Search), a SPARQL-driven framework for searching linked data and relevant semantic web services with a single user query.

Lecture Notes in Business Information Processing, vol. 221; International audience; In flexible environments like healthcare and customer service, business processes are executed with high variability. Often, this is because cases’ characteristics vary. However, it is difficult to correlate process flow with characteristics because characteristics may refer to different perspectives, their number can be real big or even because deep domain knowledge may be required to state hypotheses. The goal of this paper is to propose an effective exploratory tool for discovering the characteristics that are causing the process variation. To this end, we propose a process mining approach. First, we apply a clustering approach based on Latent Class Analysis to identify subtypes of related cases based on the case-wise process characteristics. Then, a process model is discovered for each cluster and through a model similarity step, we are able to recommend the characteristics that mostly diversify the flow. Finally, to validate our methodology, we applied it to both simulated and real datasets.

LNCS n°8831; International audience; The last years witnessed the success of Linked Open Data (LOD) project and the growing amount of semantic data sources available on the web. However, there is still a lot of data that will not be published as a fully materialized knowledge base (dynamic data, data with limited acces patterns, etc). Such data is in general available through web api or web services. In this paper, we introduce a SPARQL-driven approach for searching linked data and relevant services. In our framework, a user data query is analyzed and transformed into service requests. The resulting service requests, formatted for different semantic web services languages, are addressed to services repositories. Our system also features automatic web service composition to help finding more answers for user queries. The intended applications for such a framework vary from mashups development to aggregated search.

International audience; In the context of retrieving business processes, most approaches still lead to a high selectivity rate, resulting in a large number of services offering similar functionalities and behavior. As these services may offer different Quality of Services (QoS) values, taking into account user preferences on QoS attributes allow to retrieve the most appropriate services. In this paper, we advocate an approach for handling preferences on QoS parameters in a bipolar way, i.e., distinguishing between negative and positive preferences as it seems to be the case in the human mind. Negative preferences represent constraints that have to be satisfied by the returned services, while positive preferences express wishes and are not compulsory. Finally, a set of experiments based on real data is conducted to demonstrate the relevance and the scalability of our proposal.

International audience; One of the key tasks in the service oriented architecture that Semantic Web services aim to automate is the discovery of services that can fulfill the applications or user needs. OWL-S is one of the proposals for describing semantic metadata about Web services, which is based on the OWL ontology language. Majority of current approaches for matching OWL-S processes take into account only the inputs/outputs service profile. This chapter argues that, in many situations the service matchmaking should take into account also the process model. We present matching techniques that operate on OWL-S process models and allow retrieving in a given repository, the processes most similar to the query. To do so, the chapter proposes to reduce the problem of process matching to a graph matching problem and to adapt existing algorithms for this purpose. It proposes a similarity measure used to rank the discovered services. This measure captures differences in process structure and semantic differences between input/outputs used in the processes.

International audience; The importance gained by process models in modern information systems leaded to the proliferation of process model repositories. Retrieving process models within such repositories is a critical functionality. Recent works propose metrics that rank process models of a repository according to their similarity to a given query. However, these methods sequentially browse all the processes of the repository and compare each one against the query, which is computationally expensive. This paper presents a technique for quickly retrieving process models similar to a given query that relies on an index built on behavioral characteristics of process models.

International audience; Current approaches for service discovery are based on semantic knowledge, such as ontologies and service behavior (described as process model). However, these approaches still remain with a high selectivity rate, resulting in a large number of services offering similar functionalities and behavior. One way to improve the selectivity rate and to provide the best suited services is to cope with user preferences defined on quality attributes. In this paper, we propose and evaluate a novel approach for service retrieval that takes into account the service process model and relies both on preference satisfiability and structural similarity. User query and target process models are represented as annotated graphs, where user preferences on QoS attributes are modelled by means of fuzzy sets. A flexible evaluation strategy based on fuzzy linguistic quantifiers (such as almost all) is introduced. Then, two families of ranking methods are discussed. Finally, an extensive set of experiments based on real data sets is conducted, on one hand, to demonstrate the efficiency and the scalability of our approach, and on the other hand, to analyze the effectiveness and the accuracy of the proposed ranking methods compared to expert evaluation.

International audience; Current approaches for service discovery are based on semantic knowledge, such as ontologies and service behavior (described as a process model). However, these approaches have high selectivity rate, resulting in a large number of services offering similar functionalities and behavior. One way to improve the selectivity rate is to cope with user preferences defined on quality attributes. In this paper, we propose a novel approach for service retrieval that takes into account the service process model and relies both on preference satisfiability and structural similarity. User query and target process models are represented as annotated graphs, where user preferences on QoS attributes are modelled by means of fuzzy sets. A flexible evaluation strategy based on fuzzy linguistic quantifiers is introduced. Finally, different ranking methods are discussed.

The capability to easily find useful services (software applications, software components, scientific computations) becomes increasingly critical in several fields. Current approaches for services retrieval are mostly limited to the matching of their inputs/outputs possibly enhanced with some ontological knowledge. Recent works have demonstrated that this approach is not sufficient to discover relevant components. Motivated by these concerns, we have developed BeMatch platform for ranking web services based on behavior matchmaking. We developed matching techniques that operate on behavior models and allow delivery of partial matches and evaluation of semantic distance between these matches and user requirements. Consequently, even if a service satisfying exactly the user requirements does not exist, the most similar ones will be retrieved and proposed for reuse by extension or modification. We exemplify our approach for behavioral services matchmaking by describing two demonstration scenarios for matchmaking BPEL and WSCL protocols, respectively. A demo scenario is also described concerning the tool for evaluating the effectiveness of the behavioral matchmaking method.

This paper introduces an evolution to classical workflow model that allows more flexible execution of processes while retaining its simplicity. Processes are described in the same way they are represented in design and engineering manuals, but the workflow system allows a flexible execution that is closer to the way participants actually execute their tasks in cooperative processes without computer support. This evolution is based on the concept of anticipation, that is, the weakening of strict sequential execution of activity sequences in workflows by enabling intermediate results to be used as preliminary input into succeeding activities. The concept can also be applied to provide rapid feedback to preceding activities and to improve parallelism between activities in the main process and its subprocesses. The architecture and implementation of a workflow execution engine prototype enabling anticipation is described.

The capability to easily find useful services (software applications, software components, scientific computations) becomes increasingly critical in several fields Current approaches for services retrieval are mostly limited to the matching of their inputs/outputs Recent works have demonstrated that this approach is not sufficient to discover relevant components In this paper we argue that, in many situations, the service discovery should be based on the specification of service behavior The idea behind is to develop matching techniques that operate on behavior models and allow delivery of partial matches and evaluation of semantic distance between these matches and the user requirements Consequently, even if a service satisfying exactly the user requirements does not exist, the most similar ones will be retrieved and proposed for reuse by extension or modification To do so, we reduce the problem of behavioral matching to a graph matching problem and we adapt existing algorithms for this purpose A prototype is presented which takes as input two BPEL models and evaluates the semantic distance between them; the prototype provides also the script of edit operations that can be used to alter the first model to render it identical with the second one.

The capability to easily find useful services (software applications, software components, scientific computations) becomes increasingly critical in several fields. Current approaches for services retrieval are mostly limited to the matching of their inputs/outputs possibly enhanced with some ontological knowledge. Recent works have demonstrated that this approach is not sufficient to discover relevant components. In this paper we argue that, in many situations, the service discovery should be based on the specification of service behavior (in particular, the conversation protocol). The idea behind is to develop matching techniques that operate on behavior models and allow delivery of partial matches and evaluation of semantic distance between these matches and the user requirements. Consequently, even if a service satisfying exactly the user requirements does not exist, the most similar ones will be retrieved and proposed for reuse by extension or modification. To do so, we reduce the problem of behavioral matching to a graph matching problem and we adapt existing algorithms for this purpose. A prototype is presented (available as a web service) which takes as input two conversation protocols and evaluates the semantic distance between them.

The capability to easily find useful services (software applications, software components, scientific computations) becomes increasingly critical in several fields. Current approaches for components retrieval are mostly limited to the matching of their inputs/outputs. To go beyond the limits of these approaches, a substantial effort has been done by different works on semantic Web and ontologies by exploiting more knowledge on the semantics of the components. However, this effort still remains insufficient and does not fulfil user needs as many functional or quality aspects are hidden within the specification of components behavior. In this paper we argue that, in many situations, the component discovery process should be based on the specification of this behavior that is the process model, which describes each component. The idea behind is to develop matching techniques that operate on process models and allow delivery of partial matches and evaluation of semantic distance between these matches and the user requirements. Consequently, even if a service satisfying exactly the user requirements does not exist, the most similar ones will be retrieved and proposed for reuse by extension or modification. To do so, we reduce the problem of service behavioral matching to a graph matching problem and we adapt existing algorithms for this purpose.

The push toward business process automation has generated the need for integrating different enterprise applications involved in such processes. The typical approach to integration and to process automation is based on the use of adapters and message brokers. The need for adapters in Web services mainly comes from two sources: one is the heterogeneity at the higher levels of the interoperability stack, and the other is the high number of clients, each of which can support different interfaces and protocols, thereby generating the need for providing multiple interfaces to the same service. In this paper, we characterize the problem of adaptation of web services by identifying and classifying different kinds of adaptation requirements. Then, we focus on business protocol adapters, and we classify the different ways in which two protocols may differ. Next, we propose a methodology for developing adapters in Web services, based on the use of mismatch patterns and service composition technologies.

The capability to easily find useful services becomes increasingly critical in several fields. In this paper we argue that, in many situations, the service discovery process should be based on both behavior specification (that is the process model which describes each composite service) and quality features of services. The idea behind is to develop matching techniques that operate on process models and allow delivery of partial matches and evaluation of semantic distance between these matches and the user requirements. To do so, we reduce the problem of service behavioral matching to a graph matching problem and we adapt existing algorithms for this purpose. The matching algorithm is extended by a flexible quality evaluation procedure which checks whether a given service is worth to be delivered or not.

International audience; Workflow management systems are now widely deployed for handling administrative and production applications. In order that the workflow management systems keep supporting a larger range of applications, several research works have been launched to improve the workflow technology. This paper reports on the CORVETTE [1] project, an experiment in developing a cooperative workflow management system by integrating a workflow management system commercialised by Hitachi Ltd. with a cooperation technology proposed by INRIA. A cooperative workflow management system is a system gathering workflow management functionalities and having capabilities to manage cooperative behaviours characteristics of creative application processes (e.g. in codesign and coengineering). In this kind of applications, interactions between activities are characterised by being more subtle than in traditional applications (i.e. non-scheduled, unpredictable, dynamic, etc.). More precisely, CORVETTE was targeted to support coordination workflow processes of a virtual team working over the internet.

Article dans revue scientifique avec comité de lecture. nationale.; National audience; In this paper we present a process management technology for the coordination of creative and large scale distributed processes. Our approach is the fruit of usage analysis in domains like Software Development, Architecture/Engineering/Construction, and e-Learning processes. The basic conclusions of these experiments are the following: 1) cooperative processes are described in the same way than production processes are, but these descriptions are interpreted in a different way depending on the nature of process, 2) the interpretation of process description depends mainly on the required flexibility of control flow and of data flow, and on the relationship between them, 3) the management of intermediate results is a central feature for supporting the cooperation inherent to these processes. COO-flow is a process technology that results from these studies. It is based on two complementing contributions: anticipation that allows succeeding activities to cooperate, and COO-transactions that allows parallel activities to cooperate. This paper introduces COO-flow characteristics, gives a (partial) formalization and briefly discusses its Web implementation.

Colloque avec actes et comité de lecture. internationale./http://www.springerlink.com/; International audience; This paper describes an approach to support cooperation in workflow based on the combination of a cooperative transaction protocol (COO) and a traditional workflow model. This combination allows activities to exchange data during their execution. It also allows some activities to start in advance on the predefined control flow (anticipate) and to exchange results with its preceding activities. All these communications of draft results are done under the control of the extended cooperation protocol and under the responsibility of the users. This allows for more flexibility regarding the actual execution of the process model while keeping some control on the way the exchanges are done.

Colloque avec actes et comité de lecture. internationale.; International audience; This paper introduces an evolution to classical workflow that allows more flexible execution of processes while retaining its simplicity. On the one hand it allows to describe processes in the same way that they are in design and engineering manuals. On the other hand it allows to control these processes in a way that is close to the way they are actually enacted. This evolution is based on the concept of anticipation, i.e. the weakening of strict sequential execution of activity sequences in workflows by allowing intermediate results to be used as preliminary input into succeeding activities. The architecture and implementation of a workflow execution engine prototype allowing anticipation is described.

Colloque avec actes et comité de lecture. internationale./http://ieeexplore.ieee.org/; International audience; This paper introduces an evolution to classical workflow that allows more flexible execution of processes while retaining its simplicity On the one hand it allows describing processes in the same way that they are in design and engineering manuals, on the other hand it allows to control these processes in a way that is close to the way they are actually enacted. Flexible execution is combined with the Coo transaction protocol to provide an environment supporting an advanced form of coordination in a cooperative environment.

In the Big Data era, companies are moving away from traditional data-warehouse solutions whereby expensive and timeconsumingETL (Extract, Transform, Load) processes are used, towards data lakes in order to manage their increasinglygrowing data. Yet the stored knowledge in companies’ databases, even though in the constructed data lakes, can never becomplete and up-to-date, because of the continuous production of data. Local data sources often need to be augmentedand enriched with information coming from external data sources. Unfortunately, the data enrichment process is one of themanual labors undertaken by experts who enrich data by adding information based on their expertise or select relevantdata sources to complete missing information. Such work can be tedious, expensive and time-consuming, making itvery promising for automation. We present in this work an active user-centric data integration approach to automaticallyenrich local data sources, in which the missing information is leveraged on the fly from web sources using data services.Accordingly, our approach enables users to query for information about concepts that are not defined in the data sourceschema. In doing so, we take into consideration a set of user preferences such as the cost threshold and the responsetime necessary to compute the desired answers, while ensuring a good quality of the obtained results.; De nos jours, d’énormes volumes de données sont créés en continu et les utilisateurs s’attendent à ce que ceux-ci soient collectés, stockés et traités quasiment en temps réel. Ainsi, les lacs de données sont devenus une solution attractive par rapport aux entrepôts de données classiques coûteux et fastidieux (nécessitant une démarche ETL), pour les entreprises qui souhaitent stocker leurs données. Malgré leurs volumes, les données stockées dans les lacs de données des entreprises sont souvent incomplètes voire non mises à jour vis-à-vis des besoins (requêtes) des utilisateurs.Les sources de données locales ont donc besoin d’être enrichies. Par ailleurs, la diversité et l’expansion du nombre de sources d’information disponibles sur le web a rendu possible l’extraction des données en temps réel. Ainsi, afin de permettre d’accéder et de récupérer l’information de manière simple et interopérable, les sources de données sont de plus en plus intégrées dans les services Web. Il s’agit plus précisément des services de données, y compris les services DaaS du Cloud Computing. L’enrichissement manuel des sources locales implique plusieurs tâches fastidieuses telles que l’identification des services pertinents, l’extraction et l’intégration de données hétérogènes, la définition des mappings service-source, etc. Dans un tel contexte, nous proposons une nouvelle approche d’intégration de données centrée utilisateur. Le but principal est d’enrichir les sources de données locales avec des données extraites à partir du web via les services de données. Cela permettrait de satisfaire les requêtes des utilisateurs tout en respectant leurs préférences en terme de coût d’exécution et de temps de réponse et en garantissant la qualité des résultats obtenus.

Exchanged information in emails’ texts is usually concerned by complex events or business processes in which the entities exchanging emails are collaborating to achieve the processes’ final goals. Thus, the flow of information in the sent and received emails constitutes an essential part of such processes i.e. the tasks or the business activities. Extracting information about business processes from emails can help in enhancing the email management for users. It can be also used in finding rich answers for several analytical queries about the employees and the organizations enacting these business processes. None of the previous works have fully dealt with the problem of automatically transforming email logs into event logs to eventually deduce the undocumented business processes. Towards this aim, we work in this thesis on a framework that induces business process information from emails. We introduce approaches that contribute in the following: (1) discovering for each email the process topic it is concerned by, (2) finding out the business process instance that each email belongs to, (3) extracting business process activities from emails and associating these activities with metadata describing them, (4) improving the performance of business process instances discovery and business activities discovery from emails by making use of the relation between these two problems, and finally (5) preliminary estimating the real timestamp of a business process activity instead of using the email timestamp. Using the results of the mentioned approaches, an event log is generated which can be used for deducing the business process models of an email log. The efficiency of all of the above approaches is proven by applying several experiments on the open Enron email dataset.; Les informations échangées dans les textes des courriels sont généralement concernées par des événements complexes ou des processus métier dans lesquels les entités qui échangent des courriels collaborent pour atteindre les objectifs finaux des processus. Ainsi, le flux d’informations dans les courriels envoyés et reçus constitue une partie essentielle, les activités métier de l’entreprise. L’extraction d’informations sur les processus métier à partir des courriels peut aider à améliorer la gestion des courriels pour les utilisateurs. Il peut également être utilisé pour trouver des réponses riches à plusieurs questions analytiques sur les employés et les organisations. Aucun des travaux précédents n’a résolu le problème de la transformation automatique des journaux de courriels en journaux d’événements pour éventuellement en déduire les processus métier non documentés. Dans ce but, nous travaillons dans cette thèse sur un framework qui induit des informations de processus métier à partir d’emails. Nous introduisons des approches qui contribuent à ce qui suit : (1) découvrir pour chaque courriel le sujet de processus qui le concerne, (2) découvrir l’instance de processus métier à laquelle appartient chaque courriel, (3) extraire les activités de processus métier des courriels et associer ces activités aux métadonnées qui les décrivent, (4) améliorer la performance de la découverte des instances de processus métier et des activités métier en utilisant la relation entre ces deux problèmes, et enfin (5) estimer au préalable la date/heure réelle d’un activité métier. En utilisant les résultats des approches mentionnées, un journal d’événements est généré qui peut être utilisé pour déduire les modèles de processus métier d’un journal de courriels. L’efficacité de toutes les approches ci-dessus est prouvée par l’application de plusieurs expériences sur l’ensemble de données de courriel ouvert d’Enron.

The last years witnessed the success of the Linked Open Data (LOD) project as well as a significantly growing amount of semantic data sources available on the web. However, there are still a lot of data not being published as fully materialized knowledge bases like as sensor data, dynamic data, data with limited access patterns, etc. Such data is in general available through web APIs or web services. Integrating such data to the LOD or in mashups would have a significant added value. However, discovering such services requires a lot of efforts from developers and a good knowledge of the existing service repositories that the current service discovery systems do not efficiently overcome.In this thesis, we propose novel approaches and frameworks to search for semantic web services from a data integration perspective. Firstly, we introduce LIDSEARCH, a SPARQL-driven framework to search for linked data and semantic web services. Moreover, we propose an approach to enrich semantic service descriptions with Input-Output relations from ontologies to facilitate the automation of service discovery and composition. To achieve such a purpose, we apply natural language processing techniques and deep-learning-based text similarity techniques to leverage I/O relations from text to ontologies.We validate our work with proof-of-concept frameworks and use OWLS-TC as a dataset for conducting our experiments on service search and enrichment.; Ces dernières années ont témoigné du succès du projet Linked Open Data (LOD) et de la croissance du nombre de sources de données sémantiques disponibles sur le web. Cependant, il y a encore beaucoup de données qui ne sont pas encore mises à disposition dans le LOD telles que les données sur demande, les données de capteurs etc. Elles sont néanmoins fournies par des API des services Web. L'intégration de ces données au LOD ou dans des applications de mashups apporterait une forte valeur ajoutée. Cependant, chercher de tels services avec les outils de découverte de services existants nécessite une connaissance préalable des répertoires de services ainsi que des ontologies utilisées pour les décrire.Dans cette thèse, nous proposons de nouvelles approches et des cadres logiciels pour la recherche de services web sémantiques avec une perspective d'intégration de données. Premièrement, nous introduisons LIDSEARCH, un cadre applicatif piloté par SPARQL pour chercher des données et des services web sémantiques.De plus, nous proposons une approche pour enrichir les descriptions sémantiques de services web en décrivant les relations ontologiques entre leurs entrées et leurs sorties afin de faciliter l'automatisation de la découverte et de la composition de services. Afin d'atteindre ce but, nous utilisons des techniques de traitement automatique de la langue et d'appariement de textes basées sur le deep-learning pour mieux comprendre les descriptions des services.Nous validons notre travail avec des preuves de concept et utilisons les services et les ontologies d'OWLS-TC pour évaluer nos approches proposées de sélection et d'enrichissement.