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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.

Process models play an important role in modern information systems and in service-based applications. Given the sizes of current process model repositories, there is a critical need for techniques allowing to retrieve process models fulfilling user needs. While the problem of process similarity search has been extensively studied in the literature, existing techniques do not use semantic annotations that are proposed by the semantic web services languages and models. In this paper, we propose a framework, called PSearch, for semantic annotated process models discovery. Given a user query formulated as a process model, Psearch uses a fast search technique that returns a ranked list of similar models in the repository. In a second step, a fine-grained matching method can be applied between the query and one or several matching candidates.

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.

Techniques for process discovery support the analysis of information systems by constructing process models from event logs that are recorded during system execution. In recent years, various algorithms to discover end-to-end process models have been proposed. Yet, they do not cater for domains in which process execution is highly flexible, as the unstructuredness of the resulting models renders them meaningless. It has therefore been suggested to derive insights about flexible processes by mining behavioral patterns, i.e., models of frequently recurring episodes of a process’ behavior. However, existing algorithms to mine such patterns suffer from imprecision and redundancy of the mined patterns and a comparatively high computational effort. In this work, we overcome these limitations with a novel algorithm, coined COBPAM (COmbination based Behavioral Pattern Mining). It exploits a partial order on potential patterns to discover only those that are compact and maximal, i.e. least redundant. Moreover, COBPAM exploits that complex patterns can be characterized as combinations of simpler patterns, which enables pruning of the pattern search space. Efficiency is improved further by evaluating potential patterns solely on parts of an event log. Experiments with real-world data demonstrates how COBPAM improves over the state-of-the-art in behavioral pattern mining.

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.

LNCS, volume 10190; TCCI, volume 10190; Workflow specification, in science as in business, can be a difficult task, since it requires a deep knowledge of the domain to be able to model the chaining of the steps that compose the process of interest, as well as awareness of the computational tools, e.g., services, that can be utilized to enact such steps. To assist designers in this task, we investigate in this paper a methodology that consists in exploiting existing workflow specifications that are stored and shared in repositories, to identify workflow fragments that can be re-utilized and re-purposed by designers when specifying new workflows. Specifically, we present a method for identifying fragments that are frequently used across workflows in existing repositories, and therefore are likely to incarnate patterns that can be reused in new workflows. We present a keyword-based search method for identifying the fragments that are relevant for the needs of a given workflow designer. We go on to present an algorithm for composing the retrieved fragments with the initial (incomplete) workflow that the user designed, based on compatibility rules that we identified, and showcase how the algorithm operates using an example from eScience.

International audience; Current methods for service discovery based on service behavior – described as a process model (PM) – still return a large number of services offering similar functionalities. In this paper, we propose an evaluation strategy for service retrieval that is based on fuzzy linguistic quantifiers and relies on the similarity between service PMs and on the satisfaction of preferences defined on quality of service (QoS) attributes. Query and target PMs are represented as annotated graphs; QoS preferences are modelled by means of fuzzy sets.; Les méthodes actuelles pour la découverte de services basées sur le comportement – décrit comme un processus métier (PM) – retournent encore un grand nombre de services offrant des fonctionnalités similaires. Dans cet article, nous proposons une stratégie d’évaluation pour la recherche de services basée sur les quantificateurs linguistiques flous et qui prend en compte la similarité structurelle entre les PM de services et la satisfaction des préférences sur les attributs de qualité de services (QdS). La requête utilisateur et les PM cibles sont représentés à l’aide de graphes annotés ; les préférences de QdS sont modélisées au moyen des ensembles flous.

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 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.

Business Process Management Systems (BPMSs) are software platforms that support the definition, execution, and tracking of business processes. BPMSs have the ability of logging information about the business processes they support. Proper analysis of BPMS execution logs can yield important knowledge and help organizations improve the quality of their business processes and services to their business partners. This paper presents a set of integrated tools that supports business and IT users in managing process execution quality by providing several features, such as analysis, prediction, monitoring, control, and optimization. We refer to this set of tools as the Business Process Intelligence (BPI) tool suite. Experimental results presented in this paper are very encouraging. We plan to investigate further enhancements on the BPI tools suite, including automated exception prevention, and refinement of process data preparation stage, as well as integrating other data mining techniques.

International audience; Public repositories of scientific and business workflows are gaining growing attention as a means to enable understanding, reuse and ultimately the reproducibility of the processes such workflows incarnate. However, as the number of workflows hosted by such repositories grows, their users face difficulties when it come to exploring and querying workflows. In this paper, we explore a functionality that can help repository administrators to index their workflows, and users to identify the workflows that are of interest to them. In particular, we investigate the problem of finding frequent and similar fragments in workflows using graph mining techniques. Our objective is not to come up with yet another graph mining or similarity technique. Instead, we explore different representations that can be used for encoding workflows before assessing their similarity taking into consideration the effectiveness and efficiency of the mining algorithm.

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.

International audience; The growth of the number of published services rendered searching for a specific service within repositories a critical issue. In this paper, we present an approach to extend structure-based service discovery by making it sensitive to user preferences over service quality defined at different granularity levels of the service structure.

International audience; The increasing number of published web services rendered the searching for a service within repositories a critical issue in many application domains. Recent approaches resorted to service structure and to preferences over quality attributes to reduce selectivity rate. In this paper, we present S-MatchMaker, a tool for service discovery based on both service structure and quality preferences. The tool implements several algorithms that can be coupled in different ways to provide a personalized solution for service discovery.

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.

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 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.

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.