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Event logs that are recorded by information systems provide a valuable starting point for the analysis of processes in various domains, reaching from healthcare, through logistics, to e-commerce. Specifically, behavioral patterns discovered from an event log enable operational insights, even in scenarios where process execution is rather unstructured and shows a large degree of variability. While such behavioral patterns capture frequently recurring episodes of a process' behavior, they are not limited to sequential behavior but include notions of concurrency and exclusive choices. Existing algorithms to discover behavioral patterns are context-agnostic, though. They neglect the context in which patterns are observed, which severely limits the granularity at which behavioral regularities are identified. In this paper, we therefore present an approach to discover contextual behavioral patterns. Contextual patterns may be frequent solely in a certain partition of the event log, which enables fine-granular insights into the aspects that influence the conduct of a process. Moreover, we show how to analyze the discovered contextual behavioral patterns in terms of causal relations between context information and the patterns, as well as correlations between the patterns themselves. Experiments with real-world event logs demonstrate the effectiveness of our techniques in obtaining fine-granular process insights.

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.

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.

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.

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.

This chapter presents process matching techniques that are useful in a series of process model analytics tasks. After presenting some application scenarios such as behavior-based Web service discovery, scientific workflow discovery, and process similarity search, the chapter presents techniques that compare process models’ different perspectives: interface, business protocol, and process model. Finally, the chapter provides an analysis of existing techniques along with challenges and open problems.

This chapter looks at business process data analysis and provides an overview of different aspects of business data analysis techniques and approaches from process/dataspaces to data provenance and data-based querying techniques. After providing an overview of warehousing process data, data services, and dataspaces, the chapter discusses the importance of supporting big data analytics over process execution data. It presents a holistic view of the process executions over various information systems and services (i.e., process space) followed by a brief overview of process mining to highlight the interpretation of the information in the enterprise in the context of process mining. Finally, the chapter focuses on process artifacts and introduces crosscutting aspects in processes data such as time and provenance.

This chapter provides an overview of open-source and commercial software for process analytics. The software for process analytics can be applied to the rich source of events that document the execution of processes and activities within BPM systems, in order to support decision-making in organizations. After providing a summary and comparison of existing open-source and commercial software for process analytics, including real-world use case scenarios, the chapter starts a discussion and future directions on some of the emerging and hot trends in the business process management area such as process spaces, big data for processes, crowdsourcing, social BPM, and process management on the cloud.

This chapter looks at the business process querying techniques and languages and provides an overview of the various techniques for querying business processes. After discussing different techniques for querying the business process execution logs and their related artifacts, the chapter provides an overview of different approaches for utilizing business process querying techniques for ensuring business process compliance to their specifications and business rules.

This chapter provides an overview of the technological landscape surrounding business process management and sets the stage for understanding the different aspects of analyzing business processes with the aim of improving them. The goal of this chapter is to develop an advanced recognition of the potential gaps and thereby an appreciation for key areas of improvement needed to target successful future growth in process analytics. After presenting an overview of the quintessential facets/dimensions often used to describe process types, the chapter examines the various identified implementation technologies and surveys the relevant support tools categorized according to process paradigm.

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.

This book constitutes the revised selected papers of the 12th International Conference on Service-Oriented Computing, ICSOC 2014, held in Paris, France, in November 2014.The conference hosted the following seven workshops: 10th International Workshop in Engineering Service-Oriented Applications, WESOA 2014; First Workshop on Resource Management in Service-Oriented Computing, RMSOC 2014; First International Workshop on Knowledge Aware Service Oriented Applications, Performance Assessment and Auditing in Service Computing, KASA 2014; Workshop on Intelligent Service Clouds, ISC 2014; Third International Workshop on Self-Managing Pervasive Service Systems, SeMaPS 2014; First International Workshop on Formal Modeling and Verification of Service-Based Systems, FOR-MOVES 2014; 4th International Workshop on Cloud Computing and Scientific Applications, CCSA 2014.The papers included in this volume were carefully reviewed and selected from numerous submissions. They address various topics in the service-oriented computing domain and its emerging applications.

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.

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.