Model-driven Enterprise Systems Configuration

Enterprise Systems potentially lead to significant efficiency gains but require a well-conducted configuration process. A promising idea to manage and simplify the configuration process is based on the premise of using reference models for this task. Our paper continues along this idea and delivers a two-fold contribution: first, we present a generic process for the task of model-driven Enterprise Systems configuration including the steps of (a) Specification of configurable reference models, (b) Configuration of configurable reference models, (c) Transformation of configured reference models to regular build time models, (d) Deployment of the generated build time models, (e) Controlling of implementation models to provide input to the configuration, and (f) Consolidation of implementation models to provide input to reference model specification. We discuss inputs and outputs as well as the involvement of different roles and validation mechanisms. Second, we present an instantiation case of this generic process for Enterprise Systems configuration based on Configurable EPCs

Lifecycle Management for Business Process Variants

This chapter deals with advanced concepts for the configuration and management of business process variants. Typically, for a particular business process, different variants exist. Each of them constitutes an adjustment of a master process (e.g., a reference process) to specific requirements building the process context. Contemporary Business Process Management tools do not adequately support the modeling and management of such process variants. Either the variants have to be specified in separate process models or they are expressed in terms of conditional branches within the same process model. Both methods can result in high model redundancies, which make model adaptations a time-consuming and error-prone task. In this chapter, we discuss advanced concepts of our Provop approach, which provides a flexible and powerful solution for managing business process variants along their lifecycle. Such variant support will foster more systematic process configuration as well as process maintenance

Towards Situational Reference Model Mining - Main Idea, Procedure Model & Case Study

This contribution introduces the concept of Situational Reference Model Mining, i.e. the idea that automatically derived reference models, although based on the same input data, are intended for different use cases and thus have to meet different requirements. These requirements determine the reference model character and thus the technique that is best suited for mining it. Situational Reference Model Mining is based on well-known design principles for reference modeling, such as configuration, aggregation, specialization, instantiation, and analogy. We present a procedure model for Situational Reference Model Mining and demonstrate its usefulness by means of a case study. Existing techniques for Reference Model Mining are examined and mapped to their underlying design principles. This way, we are not only able to provide reference model designers with concrete guidelines regarding their choice of mining technique, but also point out research gaps for the development of new approaches to reference model mining

Towards Situational Reference Model Mining - Main Idea, Procedure Model & Case Study

This contribution introduces the concept of Situational Reference Model Mining, i.e. the idea that automatically derived reference models, although based on the same input data, are intended for different use cases and thus have to meet different requirements. These requirements determine the reference model character and thus the technique that is best suited for mining it. Situational Reference Model Mining is based on well-known design principles for reference modeling, such as configuration, aggregation, specialization, instantiation, and analogy. We present a procedure model for Situational Reference Model Mining and demonstrate its usefulness by means of a case study. Existing techniques for Reference Model Mining are examined and mapped to their underlying design principles. This way, we are not only able to provide reference model designers with concrete guidelines regarding their choice of mining technique, but also point out research gaps for the development of new approaches to reference model mining

VIVACE: A framework for the systematic evaluation of variability support in process-aware information systems

Context: The increasing adoption of process-aware information systems (PAISs) such as workflow management systems, enterprise resource planning systems, or case management systems, together with the high variability in business processes (e.g., sales processes may vary depending on the respective products and countries), has resulted in large industrial process model repositories. To cope with this business process variability, the proper management of process variants along the entire process lifecycle becomes crucial. Objective: The goal of this paper is to develop a fundamental understand-ing of business process variability. In particular, the paper will provide a framework for assessing and comparing process variability approaches and the support they provide for the different phases of the business process life

VIVACE: A framework for the systematic evaluation of variability support in process-aware information systems

Context: The increasing adoption of process-aware information systems (PAISs) such as workflow management systems, enterprise resource planning systems, or case management systems, together with the high variability in business processes (e.g., sales processes may vary depending on the respective products and countries), has resulted in large industrial process model repositories. To cope with this business process variability, the proper management of process variants along the entire process lifecycle becomes crucial. Objective: The goal of this paper is to develop a fundamental understanding of business process variability. In particular, the paper will provide a framework for assessing and comparing process variability approaches and the support they provide for the different phases of the business process lifecycle (i.e., process analysis and design, configuration, enactment, diagnosis, and evolution). Method: We conducted a systematic literature review (SLR) in order to discover how process variability is supported by existing approaches. Results: The SLR resulted in 63 primary studies which were deeply analyzed. Based on this analysis, we derived the VIVACE framework. VIVACE allows assessing the expressiveness of a process modeling language regarding the explicit specification of process variability. Furthermore, the support provided by a process-aware information system to properly deal with process model variants can be assessed with VIVACE as well. Conclusions: VIVACE provides an empirically-grounded framework for process engineers that enables them to evaluate existing process variability approaches as well as to select that variability approach meeting their requirements best. Finally, it helps process engineers in implementing PAISs supporting process variability along the entire process lifecycle. (C) 2014 Elsevier B.V. All rights reserved.This work has been developed with the support of MICINN under the project EVERYWARE TIN2010-18011.Ayora Esteras, C.; Torres Bosch, MV.; Weber, B.; Reichert, M.; Pelechano Ferragud, V. (2015). VIVACE: A framework for the systematic evaluation of variability support in process-aware information systems. Information and Software Technology. 57:248-276. https://doi.org/10.1016/j.infsof.2014.05.009S2482765

VIVACE: A Framework for the Systematic Evaluation of Variability Support in Process-Aware Information Systems

CONTEXT The increasing adoption of process-aware information systems (PAISs) such as workflow management systems, enterprise resource planning systems, or case management systems, together with the high variability in business processes (e.g., sales processes may vary depending on the respective products and countries), has resulted in large industrial process model repositories. To cope with this business process variability, the proper management of process variants along the entire process lifecycle becomes crucial. OBJECTIVE The goal of this paper is to develop a fundamental understanding of business process variability. In particular, the paper will provide a framework for assessing and comparing process variability approaches and the support they provide for the different phases of the business process lifecycle (i.e., process analysis and design, configuration, enactment, diagnosis, and evolution). METHOD We conducted a systematic literature review (SLR) in order to discover how process variability is supported by existing approaches. RESULTS The SLR resulted in 63 primary studies which were deeply analyzed. Based on this analysis, we derived the VIVACE framework. VIVACE allows assessing the expressiveness of a process modeling language regarding the explicit specification of process variability. Furthermore, the support provided by a process-aware information system to properly deal with process model variants can be assessed with VIVACE as well. CONCLUSIONS VIVACE provides an empirically-grounded framework for process engineers that enables them to evaluate existing process variability approaches as well as to select that variability approach meeting their requirements best. Finally, it helps process engineers in implementing PAISs supporting process variability along the entire process lifecycle

Performance and scalability of indexed subgraph query processing methods

Graph data management systems have become very popular as graphs are the natural data model for many applications. One of the main problems addressed by these systems is subgraph query processing; i.e., given a query graph, return all graphs that contain the query. The naive method for processing such queries is to perform a subgraph isomorphism test against each graph in the dataset. This obviously does not scale, as subgraph isomorphism is NP-Complete. Thus, many indexing methods have been proposed to reduce the number of candidate graphs that have to underpass the subgraph isomorphism test. In this paper, we identify a set of key factors-parameters, that influence the performance of related methods: namely, the number of nodes per graph, the graph density, the number of distinct labels, the number of graphs in the dataset, and the query graph size. We then conduct comprehensive and systematic experiments that analyze the sensitivity of the various methods on the values of the key parameters. Our aims are twofold: first to derive conclusions about the algorithms’ relative performance, and, second, to stress-test all algorithms, deriving insights as to their scalability, and highlight how both performance and scalability depend on the above factors. We choose six wellestablished indexing methods, namely Grapes, CT-Index, GraphGrepSX, gIndex, Tree+∆, and gCode, as representative approaches of the overall design space, including the most recent and best performing methods. We report on their index construction time and index size, and on query processing performance in terms of time and false positive ratio. We employ both real and synthetic datasets. Specifi- cally, four real datasets of different characteristics are used: AIDS, PDBS, PCM, and PPI. In addition, we generate a large number of synthetic graph datasets, empowering us to systematically study the algorithms’ performance and scalability versus the aforementioned key parameters

Automatiser le support de la variabilité dans les modèles de processus configurables

Today's fast changing environment imposes new challenges for effective management of business processes. In such a highly dynamic environment, the business process design becomes time-consuming, error-prone, and costly. Therefore, seeking reuse and adaptability is a pressing need for a successful business process design. Configurable reference models recently introduced were a step toward enabling a process design by reuse while providing flexibility. A configurable process model is a generic model that integrates multiple process variants of a same business process in a given domain through variation points. These variation points are referred to as configurable elements and allow for multiple design options in the process model. A configurable process model needs to be configured according to a specific requirement by selecting one design option for each configurable element.Recent research activities on configurable process models have led to the specification of configurable process modeling notations as for example configurable Event-Driven Process Chain (C-EPC) that extends the EPC notation with configurable elements. Since then, the issue of building and configuring configurable process models has been investigated. On the one hand, as configurable process models tend to be very complex with a large number of configurable elements, many automated approaches have been proposed to assist their design. However, existing approaches propose to recommend entire configurable process models which are difficult to reuse, cost much computation time and may confuse the process designer. On the other hand, the research results on configurable process model design highlight the need for means of support to configure the process. Therefore, many approaches proposed to build a configuration support system for assisting end users selecting desirable configuration choices according to their requirements. However, these systems are currently manually created by domain experts which is undoubtedly a time-consuming and error-prone task.In this thesis, we aim at automating the support of the variability in configurable process models. Our objective is twofold: (i) assisting the configurable process design in a fin-grained way using configurable process fragments that are close to the designers interest and (ii) automating the creation of configuration support systems in order to release the process analysts from the burden of manually building them. In order to achieve the first objective, we propose to learn from the experience gained through past process modeling in order to assist the process designers with configurable process fragments. The proposed fragments inspire the process designer to complete the design of the ongoing process. To achieve the second objective, we realize that previously designed and configured process models contain implicit and useful knowledge for process configuration. Therefore, we propose to benefit from the experience gained through past process modeling and configuration in order to assist process analysts building their configuration support systems. Such systems assist end users interactively configuring the process by recommending suitable configuration decisions.L'évolution rapide dans les environnements métier d'aujourd'hui impose de nouveaux défis pour la gestion efficace et rentable des processus métiers. Dans un tel environnement très dynamique, la conception des processus métiers devient une tâche fastidieuse, source d'erreurs et coûteuse. Par conséquent, l'adoption d'une approche permettant la réutilisation et l'adaptabilité devient un besoin urgent pour une conception de processus prospère. Les modèles de processus configurables récemment introduits représentent l'une des solutions recherchées permettant une conception de processus par la réutilisation, tout en offrant la flexibilité. Un modèle de processus configurable est un modèle générique qui intègre de multiples variantes de procédés d'un même processus métier à travers des points de variation. Ces points de variation sont appelés éléments configurables et permettent de multiples options de conception dans le modèle de processus. Un modèle de processus configurable doit être configuré selon une exigence spécifique en sélectionnant une option de conception pour chaque élément configurable.Les activités de recherche récentes sur les modèles de processus configurables ont conduit à la spécification des langages de modélisation de processus configurables comme par exemple configurable Event-Driven Process Chain (C-EPC) qui étend la notation de l'EPC avec des éléments configurables. Depuis lors, la question de la conception et de la configuration des modèles de processus configurables a été étudiée. D'une part, puisque les modèles de processus configurables ont tendance à être très complexe avec un grand nombre d'éléments configurables, de nombreuses approches automatisées ont été proposées afin d'assister leur conception. Cependant, les approches existantes proposent de recommander des modèles de processus configurables entiers qui sont difficiles à réutiliser, nécessitent un temps complexe de calcul et peuvent confondre le concepteur du processus. D'autre part, les résultats de la recherche sur la conception des modèles de processus configurables ont mis en évidence la nécessité des moyens de soutien pour configurer le processus. Par conséquent, de nombreuses approches ont proposé de construire un système de support de configuration pour aider les utilisateurs finaux à sélectionner les choix de configuration souhaitables en fonction de leurs exigences. Cependant, ces systèmes sont actuellement créés manuellement par des experts du domaine qui est sans aucun doute une tâche fastidieuse et source d'erreurs .Dans cette thèse, nous visons à automatiser le soutien de la variabilité dans les modèles de processus configurables. Notre objectif est double: (i) assister la conception des processus configurables d'une manière à ne pas confondre les concepteurs par des recommandations complexes et (i) assister la création des systèmes de soutien de configuration afin de libérer les analystes de processus de la charge de les construire manuellement. Pour atteindre le premier objectif, nous proposons d'apprendre de l'expérience acquise grâce à la modélisation des processus passés afin d'aider les concepteurs de processus avec des fragments de processus configurables. Les fragments proposés inspirent le concepteur du processus pour compléter la conception du processus en cours. Pour atteindre le deuxième objectif, nous nous rendons compte que les modèles de processus préalablement conçus et configurés contiennent des connaissances implicites et utiles pour la configuration de processus. Par conséquent, nous proposons de bénéficier de l'expérience acquise grâce à la modélisation et à la configuration passées des processus afin d'aider les analystes de processus dans la construction de leurs systèmes de support de configuration

Enhancing Variability Modeling in Process-Aware Information Systems through Change Patterns

[EN] The increasing adoption of process-aware information systems (PAISs) together with the high variability in business processes has resulted in collections of process families. These families correspond to a business process model and its variants, which can comprise hundreds or thousands of different ways of realizing this process. Modeling and managing process variability in this context can be very challenging due to the size of these families. Motivated by this challenge, several approaches enabling process variability have been developed. However, with these approaches PAIS engineers usually are required to model and manage one by one all the elements of a process family and ensure its correctness by their own. This can be tedious and error-prone especially when a process family comprises hundreds or thousands of process variants. For example, variability may not be properly reflected since PAIS engineers need to be aware of each variation of each process variant. Thus, there is a need of methods that allow PAIS engineers to model process variability more explicitly, especially at a level of abstraction higher than the one provided by the existing process variability approaches. However, how process variability is represented in existing approaches becomes critical for these methods (e.g., what language constructs are used to model process variability). In this context, the use of modeling patterns (reusable solutions to a commonly occurring problem) is a promising way to address these issues. For example, patterns have been proved as an efficient solution to model individual business processes. The objective of this thesis is to enhance the modeling of variability in process families through change patterns. First, we conduct a systematic study to analyze existing process variability approaches regarding their expressiveness with respect to process variability modeling as well as their process support. Thus, we can identify how process variability is actually modeled by existing approaches (i.e., a core set of variability-specific language constructs). In addition, based on the obtained empirical evidence, we derive the VIVACE framework, a complete characterization of process variability which comprises also a core set of features fostering process variability. VIVACE enables PAIS engineers to evaluate existing process variability approaches as well as to select that variability approach meeting their requirements best. In addition, it helps process engineers in dealing with PAISs supporting process variability. Second, to facilitate variability modeling in process families, based on the identified language constructs, we present a set of 10 change patterns and show how they can be implemented in a process variability approach. In particular, these patterns support process family modeling and evolution and are able to ensure process family correctness. In order to prove their effectiveness and analyze their suitability, we applied these change patterns in a real scenario. More concretely, we conduct a case study with a safety standard with a high degree of variability. The case study results show that the application of the change patterns can reduce the effort for process family modeling in a 34% and for evolution in a 40%. In addition, we have analyzed how PAIS engineers apply the patterns and their perceptions of this application. Most of them expressed some benefit when applying the change patterns, did not perceived an increase of mental effort for applying the patterns, and agreed upon the usefulness and ease of use of the patterns.[ES] La creciente adopción de sistemas de información dirigidos por procesos de negocio (PAIS) junto con la alta variabilidad en dichos procesos, han dado lugar a la aparición de colecciones de familias de procesos. Estas familias están constituidas por un modelo de proceso de negocio y sus variantes, las cuales pueden comprender entre cientos y miles de diferentes formas de llevar a cabo ese proceso. Gestionar la variabilidad en este contexto puede resultar muy difícil dado el tamaño que estas familias pueden alcanzar. Motivados por este desafío, se han desarrollado varias soluciones que permiten la gestión de la variabilidad en los procesos de negocio. Sin embargo, con estas soluciones los ingenieros deben crear y gestionar uno por uno todos los elementos de las familias de procesos y asegurar ellos mismos su corrección. Esto puede resultar tedioso y propenso a errores especialmente cuando las familias están compuestas de miles de variantes. Por ejemplo, la variabilidad puede no quedar adecuadamente representada ya que los ingenieros deben ser conscientes de todas y cada una de las variaciones de todas las variantes. Así, son necesarios nuevos métodos que permitan modelar la variabilidad de los procesos de una manera más explícita, a un nivel de abstracción más alto del proporcionado por las soluciones actuales. Sin embargo, cómo se representa la variabilidad en estos métodos resulta crítico (ej.: qué primitivas se utilizan). En este contexto, el uso de patrones de modelado (soluciones reutilizables a un problema recurrente) resultan un camino prometedor. Por ejemplo, los patrones han sido probados como una solución eficaz para gestionar procesos de negocio individuales. El objetivo de esta tesis es mejorar el modelado de la variabilidad en las familias de procesos a través del uso de patrones de cambio. En primer lugar, hemos llevado a cabo un estudio sistemático con el fin de analizar las soluciones existentes que permiten gestionar la variabilidad en los procesos, así como el soporte que estas proporcionan. Así, hemos sido capaces de identificar y analizar cuál es el conjunto básico de primitivas específicas para representar la variabilidad. Además, basándonos en la evidencia empírica obtenida, hemos derivado el marco de evaluación VIVACE, el cual recoge las primitivas de variabilidad y un conjunto básico de características que favorecen la variabilidad en los procesos. El principal objetivo de VIVACE es conformar una completa caracterización de la variabilidad en los procesos de negocio. Asimismo, VIVACE permite evaluar las soluciones que gestionan la variabilidad en los procesos, así como seleccionar la solución que se ajuste mejor a sus necesidades. Finalmente, VIVACE puede ayudar a los ingenieros a gestionar PAISs con variabilidad. En segundo lugar, para facilitar el modelado de la variabilidad en las familias de procesos, basándonos en las primitivas identificadas, hemos definido un conjunto de 10 patrones de cambio y hemos mostrado cómo estos patrones pueden ser implementados. En particular, estos patrones ayudan al modelado y la evolución de familias de procesos y son capaces de garantizar la corrección de la propia familia. Para probar su efectividad y analizar su idoneidad, hemos aplicado estos patrones de cambio en un escenario real. En concreto, hemos llevado a cabo un caso de estudio con un estándar de seguridad con un alto nivel de variabilidad. Los resultados de este caso demuestran que la aplicación de nuestros patrones de cambio puede reducir el esfuerzo para el modelado de familias de procesos en un 34% y para la evolución de esos modelos en un 40%. Además, hemos analizado cómo los ingenieros aplican los patrones y cuáles son sus percepciones de esta aplicación. Como resultado, la mayoría de ellos encontró beneficios al aplicar los patrones. Además, no percibieron un aumento en el esfuerzo mental necesario para aplicarlos y estuvieron de acuerdo en la utilid[CA] La creixent adopció de sistemes d'informació dirigits per processos de negoci (PAIS) junt amb l'alta variabilitat en eixos processos, han donat lloc a la aparició de col·leccions de famílies de processos. Estes famílies es formen de un model de procés de negoci i les seues variants, les quals poden comprendre entre cents i milers de diferents formes de dur a terme eixe procés. Modelar la variabilitat dels processos en este context pot resultar molt difícil donat la grandària que aquestes famílies poden aconseguir. Motivats per este desafiament, s'han desenvolupat diverses solucions que permeten la gestió de la variabilitat en els processos de negoci. No obstant, amb aquestes solucions els enginyers que treballen amb PAIS han de crear i gestionar un a un tots els elements de les famílies de processos i assegurar ells mateixos la seua correcció. Això pot resultar tediós i propens a errors especialment quan les famílies es componen de cents o milers de variants. Per exemple, la variabilitat pot no quedar adequadament representada ja que els enginyers han de ser conscients de totes i cadascuna una de les variacions de totes les variants. Per quest motiu, son necessaris nous mètodes que permeten als enginyers de PAIS modelar la variabilitat dels processos de manera més explícita, sobretot a un nivell d'abstracció més alt del proporcionat per les solucions actuals. No obstant, com es representa la variabilitat en aquestos mètodes resulta crític (ex.: quines primitives s'utilitzen per a modelar la variabilitat en els processos). En aquest context, l'ús de patrons de modelatge (solucions reutilitzables a un problema recurrent) resulten un camí prometedor. Per exemple, els patrons han sigut provats com una solució eficaç per modelar i gestionar processos de negoci individuals. L'objectiu d'aquesta tesi 'es millorar el modelatge de la variabilitat en les famílies de processos a través de l'ús de patrons de canvi. En primer lloc, hem dut a terme un estudi sistemàtic per a analitzar les solucions existents per a gestionar la variabilitat en els processos, així com el suport que aquestes proporcionen. D'aquesta manera, som capaços d'identificar i analitzar quin 'es el conjunt bàsic de primitives específiques per a representar la variabilitat. A més, basant-nos en l'evidència empírica obtinguda, hem derivat el marc d'evacuació VIVACE, el qual arreplega les primitives de variabilitat i un conjunt bàsic de característiques que afavoreixen la variabilitat en els processos. Així mateix, VIVACE permet als enginyers de PAIS avaluar les solucions per a gestionar la variabilitat en els processos, així com seleccionar la solució que s'ajusta millor a les seues necessitats. Finalment, VIVACE també pot ajudar als enginyers a gestionar PAISs que donen suport a aquesta variabilitat. En segon lloc, per a facilitar el modelatge de la variabilitat en les famílies de processos, basant-nos en les primitives identificades, hem definit un conjunt de 10 patrons de canvi i hem mostrat com aquestos poden ser implementats. En particular, estos patrons ajuden al modelatge i l'evolució de famílies de processos i garanteixen la correcció de la pròpia família. Per a provar la seua efectivitat i analitzar la seua idoneïtat, hem aplicat els patrons de canvi en un escenari real. En particular, hem dut a terme un cas d'estudi amb un estàndard de seguretat amb un alt nivell de variabilitat. Els resultats de aquest cas demostren que l'aplicació dels nostres patrons de canvi poden reduir l'esforç per al modelatge de famílies de processos en un 34% i per a l'evolució de eixos models en un 40%. A més, hem analitzat com els enginyers de PAIS apliquen els patrons i quines son les seues percepcions d'esta aplicació. Com a resultat, la majoria d'ells va trobar beneficis al aplicar els patrons de canvi. A més, no van percebre un augment en l'esforç mental necessari per a aplicar-los i van estar d'acord en la utilitat i fAyora Esteras, C. (2015). Enhancing Variability Modeling in Process-Aware Information Systems through Change Patterns [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/58426TESI