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

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

Adaptive Process Management in Cyber-Physical Domains

The increasing application of process-oriented approaches in new challenging cyber-physical domains beyond business computing (e.g., personalized healthcare, emergency management, factories of the future, home automation, etc.) has led to reconsider the level of flexibility and support required to manage complex processes in such domains. A cyber-physical domain is characterized by the presence of a cyber-physical system coordinating heterogeneous ICT components (PCs, smartphones, sensors, actuators) and involving real world entities (humans, machines, agents, robots, etc.) that perform complex tasks in the “physical” real world to achieve a common goal. The physical world, however, is not entirely predictable, and processes enacted in cyber-physical domains must be robust to unexpected conditions and adaptable to unanticipated exceptions. This demands a more flexible approach in process design and enactment, recognizing that in real-world environments it is not adequate to assume that all possible recovery activities can be predefined for dealing with the exceptions that can ensue. In this chapter, we tackle the above issue and we propose a general approach, a concrete framework and a process management system implementation, called SmartPM, for automatically adapting processes enacted in cyber-physical domains in case of unanticipated exceptions and exogenous events. The adaptation mechanism provided by SmartPM is based on declarative task specifications, execution monitoring for detecting failures and context changes at run-time, and automated planning techniques to self-repair the running process, without requiring to predefine any specific adaptation policy or exception handler at design-time

Modeling Interactive Enterprise Architecture Visualizations: An Extended Architecture Description

Enterprise Architectures consist of a multitude of architecture elements, which relate in manifold ways to each other. Due to the high number of relationships between these elements, architectural analysis mechanisms are essential for all stakeholders to keep track and to work out relevant model characteristics. In practice EAs are often analyzed using visualizations by hand. However, the visualizations are often static and there are only few interaction possibilities. As a result, new visualizations have to be created or configured by experts if information demands change. In addition, hardly any tools are used for analysis of complex model characteristics. In this article we introduce an extended conceptualization of the architecture description that defines the structure of interactive visualizations and the integration of further tools to flexibly respond to the information demands of stakeholders. In addition, we develop a so-called Architecture Cockpit that realizes the extended conceptualization in a prototype. At the end we demonstrate and evaluate our approach through a practical test in a company in the finance and insurance industry

Towards the Applicability of Measuring the Electrodermal Activity in the Context of Process Model Comprehension: Feasibility Study

Process model comprehension is essential in order to understand the Five Ws (i.e., who, what, where, when, and why) pertaining to the processes of organizations. However, research in this context showed that a proper comprehension of process models often poses a challenge in practice. For this reason, a vast body of research exists studying the factors having an influence on process model comprehension. In order to point research towards a neuro-centric perspective in this context, the paper at hand evaluates the appropriateness of measuring the electrodermal activity (EDA) during the comprehension of process models. Therefore, a preliminary test run and a feasibility study were conducted relying on an EDA and physical activity sensor to record the EDA during process model comprehension. The insights obtained from the feasibility study demonstrated that process model comprehension leads to an increased activity in the EDA. Furthermore, EDA-related results indicated significantly that participants were confronted with a higher cognitive load during the comprehension of complex process models. In addition, the experiences and limitations we have learned in measuring the EDA during the comprehension of process models are discussed in this paper. In conclusion, the feasibility study demonstrated that the measurement of the EDA could be an appropriate method to obtain new insights in process model comprehension

Measuring the Cognitive Complexity in the Comprehension of Modular Process Models

Modularization in process models is a method to cope with the inherent complexity in such models (e.g., model size reduction). Modularization is capable to increase the quality, the ease of reuse, and the scalability of process models. Prior conducted research studied the effects of modular process models to enhance their comprehension. However, the effects of modularization on cognitive factors during process model comprehension are less understood so far. Therefore, this paper presents the results of two exploratory studies (i.e., a survey research study with N = 95 participants; a follow-up eye tracking study with N = 19 participants), in which three types of modularization (i.e., horizontal, vertical, orthogonal) were applied to process models expressed in terms of the Business Process Model and Notation (BPMN) 2.0. Further, the effects of modularization on the cognitive load, the level of acceptability, and the performance in process model comprehension were investigated. In general, the results revealed that participants were confronted with challenges during the comprehension of modularized process models. Further, performance in the comprehension of modularized process models showed only a few significant differences, however, the results obtained regarding the cognitive load revealed that the complexity and concept of modularization in process models were misjudged initially. The insights unraveled that the attitude towards the application and the behavioral intention to apply modularization in process model is still not clear. In this context, horizontal modularization appeared to be the best comprehensible modularization approach leading to a more fine-grained comprehension of respective process models. The findings indicate that alterations in modular process models (e.g., change in the representation) are important to foster and enable their comprehension. Finally, based on our results, implications for research and practice as well as directions for future work are discussed in this paper

A Model-Driven Framework for Enabling Flexible and Robust Mobile Data Collection Applications

In the light of the ubiquitous digital transformation, smart mobile technology has become a salient factor for enabling large-scale data collection scenarios. Structured instruments (e.g., questionnaires) are frequently used to collect data in various application domains, like healthcare, psychology, and social sciences. In current practice, instruments are usually distributed and filled out in a paper-based fashion (e.g., paper-and-pencil questionnaires). The widespread use of smart mobile devices, like smartphones or tablets, offers promising perspectives for the controlled collection of accurate data in high quality. The design, implementation and deployment of mobile data collection applications, however, is a challenging endeavor. First, various mobile operating systems need to be properly supported, taking their short release cycles into account. Second, domain-specific peculiarities need to be flexibly aligned with mobile application development. Third, domain-specific usability guidelines need to be obeyed. Altogether, these challenges turn both programming and maintaining of mobile data collection applications into a costly, time-consuming, and error-prone endeavor. The Ph.D. thesis at hand presents an advanced framework that shall enable domain experts to transform paper-based instruments to mobile data collection applications. The latter, in turn, can then be deployed to and executed on heterogeneous smart mobile devices. In particular, the framework shall empower domain experts (i.e., end-users) to flexibly design and create robust mobile data collection applications on their own; i.e., without need to involve IT experts or mobile application developers. As major benefit, the framework enables the development of sophisticated mobile data collection applications by orders of magnitude faster compared to current approaches, and relieves domain experts from manual tasks like, for example, digitizing and analyzing the collected data