BPM software and process modelling languages in practice

IT has turned out to be a key factor for the purposes of gaining maturity in Business Process Management (BPM). This book presents a worldwide investigation that was conducted among companies from the ‘Forbes Global 2000’ list to explore the current usage of software throughout the BPM life cycle and to identify the companies’ requirements concerning process modelling. The responses from 130 companies indicate that, at the present time, it is mainly software for process description and analysis that is required, while process execution is supported by general software such as databases, ERP systems and office tools. The resulting complex system landscapes give rise to distinct requirements for BPM software, while the process modelling requirements can be equally satisfied by the most common languages (BPMN, UML, EPC).

Experiences in the assessment of the requirement management process

The focus of this paper is to outline the practical experiences and the lessons learned derived from the assessment of the requirements management process in two industrial case studies. Furthermore this paper explains the main structure of an alternative assessment approach that has been used in the appraisal of the two case studies. The assessment approach helped us to know the current state of the organizational requirement management process. We have to point out that these practical experiences and the lessons learned can be helpful to reduce risks and costs of the on-site assessment process.

Assessment of the requirement management process using a two-stage questionnaire

This research advocates the idea that although requirements management process is not carried out in many organizations there is some people within the organization that perform some requirements management practices. However, these practices are usually not documented and as consequence are not spread across the organization. This paper proposes an assessment methodology based on a two-stage questionnaire to identify which practices of the requirements management process are performed but not documented, which practices require to be prioritized and which are not implemented due to bad management or unawareness. In order to validate the assessment methodology, the questionnaire was applied to an industrial case study.

Introduction: Ontology Engineering in a Networked World

While ontology engineering is rapidly entering the mainstream, expert ontology engineers are a scarce resource. Hence, there is a need for practical methodologies and technologies, which can assist a variety of user types with ontology development tasks. To address this need, this book presents a scenario-based methodology, the NeOn Methodology, which provides guidance for all main activities in ontology engineering. The context in which we consider these activities is that of a networked world, where reuse of existing resources is commonplace, ontologies are developed collaboratively, and managing relationships between ontologies becomes an essential aspect of the ontological engineering process. The description of both the methodology and the ontology engineering activities is grounded in a comprehensive software environment, the NeOn Toolkit and its plugins, which provides integrated support for all the activities described in the book. Here we provide an introduction for the whole book, while the rest of the content is organized into 4 parts: (1) the NeOn Methodology Framework, (2) the set of ontology engineering activities, (3) the NeOn Toolkit and plugins, and (4) three use cases. Primary goals of this book are (a) to disseminate the results from the NeOn project in a structured and comprehensive form, (b) to make it easier for students and practitioners to adopt ontology engineering methods and tools, and (c) to provide a textbook for undergraduate and postgraduate courses on ontology engineering.

Model-based Self-awareness Patterns for Autonomy

Los sistemas técnicos son cada vez más complejos, incorporan funciones más avanzadas, están más integrados con otros sistemas y trabajan en entornos menos controlados. Todo esto supone unas condiciones más exigentes y con mayor incertidumbre para los sistemas de control, a los que además se demanda un comportamiento más autónomo y fiable. La adaptabilidad de manera autónoma es un reto para tecnologías de control actualmente. El proyecto de investigación ASys propone abordarlo trasladando la responsabilidad de la capacidad de adaptación del sistema de los ingenieros en tiempo de diseño al propio sistema en operación. Esta tesis pretende avanzar en la formulación y materialización técnica de los principios de ASys de cognición y auto-consciencia basadas en modelos y autogestión de los sistemas en tiempo de operación para una autonomía robusta. Para ello el trabajo se ha centrado en la capacidad de auto-conciencia, inspirada en los sistemas biológicos, y se ha explorado la posibilidad de integrarla en la arquitectura de los sistemas de control. Además de la auto-consciencia, se han explorado otros temas relevantes: modelado funcional, modelado de software, tecnología de los patrones, tecnología de componentes, tolerancia a fallos. Se ha analizado el estado de la técnica en los ámbitos pertinentes para las cuestiones de la auto-consciencia y la adaptabilidad en sistemas técnicos: arquitecturas cognitivas, control tolerante a fallos, y arquitecturas software dinámicas y computación autonómica. El marco teórico de ASys existente de sistemas autónomos cognitivos ha sido adaptado para servir de base para este análisis de autoconsciencia y adaptación y para dar sustento conceptual al posterior desarrollo de la solución. La tesis propone una solución general de diseño para la construcción de sistemas autónomos auto-conscientes. La idea central es la integración de un meta-controlador en la arquitectura de control del sistema autónomo, capaz de percibir la estado funcional del sistema de control y, si es necesario, reconfigurarlo en tiempo de operación. Esta solución de metacontrol se ha formalizado en cuatro patrones de diseño: i) el Patrón Metacontrol, que define la integración de un subsistema de metacontrol, responsable de controlar al propio sistema de control a través de la interfaz proporcionada por su plataforma de componentes, ii) el patrón Bucle de Control Epistémico, que define un bucle de control cognitivo basado en el modelos y que se puede aplicar al diseño del metacontrol, iii) el patrón de Reflexión basada en Modelo Profundo propone una solución para construir el modelo ejecutable utilizado por el meta-controlador mediante una transformación de modelo a modelo a partir del modelo de ingeniería del sistema, y, finalmente, iv) el Patrón Metacontrol Funcional, que estructura el meta-controlador en dos bucles, uno para el control de la configuración de los componentes del sistema de control, y otro sobre éste, controlando las funciones que realiza dicha configuración de componentes; de esta manera las consideraciones funcionales y estructurales se desacoplan. La Arquitectura OM y el metamodelo TOMASys son las piezas centrales del marco arquitectónico desarrollado para materializar la solución compuesta de los patrones anteriores. El metamodelo TOMASys ha sido desarrollado para la representación de la estructura y su relación con los requisitos funcionales de cualquier sistema autónomo. La Arquitectura OM es un patrón de referencia para la construcción de una metacontrolador integrando los patrones de diseño propuestos. Este meta-controlador se puede integrar en la arquitectura de cualquier sistema control basado en componentes. El elemento clave de su funcionamiento es un modelo TOMASys del sistema decontrol, que el meta-controlador usa para monitorizarlo y calcular las acciones de reconfiguración necesarias para adaptarlo a las circunstancias en cada momento. Un proceso de ingeniería, complementado con otros recursos, ha sido elaborado para guiar la aplicación del marco arquitectónico OM. Dicho Proceso de Ingeniería OM define la metodología a seguir para construir el subsistema de metacontrol para un sistema autónomo a partir del modelo funcional del mismo. La librería OMJava proporciona una implementación del meta-controlador OM que se puede integrar en el control de cualquier sistema autónomo, independientemente del dominio de la aplicación o de su tecnología de implementación. Para concluir, la solución completa ha sido validada con el desarrollo de un robot móvil autónomo que incorpora un meta-controlador con la Arquitectura OM. Las propiedades de auto-consciencia y adaptación proporcionadas por el meta-controlador han sido validadas en diferentes escenarios de operación del robot, en los que el sistema era capaz de sobreponerse a fallos en el sistema de control mediante reconfiguraciones orquestadas por el metacontrolador. ABSTRACT Technical systems are becoming more complex, they incorporate more advanced functionalities, they are more integrated with other systems and they are deployed in less controlled environments. All this supposes a more demanding and uncertain scenario for control systems, which are also required to be more autonomous and dependable. Autonomous adaptivity is a current challenge for extant control technologies. The ASys research project proposes to address it by moving the responsibility for adaptivity from the engineers at design time to the system at run-time. This thesis has intended to advance in the formulation and technical reification of ASys principles of model-based self-cognition and having systems self-handle at runtime for robust autonomy. For that it has focused on the biologically inspired capability of self-awareness, and explored the possibilities to embed it into the very architecture of control systems. Besides self-awareness, other themes related to the envisioned solution have been explored: functional modeling, software modeling, patterns technology, components technology, fault tolerance. The state of the art in fields relevant for the issues of self-awareness and adaptivity has been analysed: cognitive architectures, fault-tolerant control, and software architectural reflection and autonomic computing. The extant and evolving ASys Theoretical Framework for cognitive autonomous systems has been adapted to provide a basement for this selfhood-centred analysis and to conceptually support the subsequent development of our solution. The thesis proposes a general design solution for building self-aware autonomous systems. Its central idea is the integration of a metacontroller in the control architecture of the autonomous system, capable of perceiving the functional state of the control system and reconfiguring it if necessary at run-time. This metacontrol solution has been formalised into four design patterns: i) the Metacontrol Pattern, which defines the integration of a metacontrol subsystem, controlling the domain control system through an interface provided by its implementation component platform, ii) the Epistemic Control Loop pattern, which defines a modelbased cognitive control loop that can be applied to the design of such a metacontroller, iii) the Deep Model Reflection pattern proposes a solution to produce the online executable model used by the metacontroller by model-to-model transformation from the engineering model, and, finally, iv) the Functional Metacontrol pattern, which proposes to structure the metacontroller in two loops, one for controlling the configuration of components of the controller, and another one on top of the former, controlling the functions being realised by that configuration; this way the functional and structural concerns become decoupled. The OM Architecture and the TOMASys metamodel are the core pieces of the architectural framework developed to reify this patterned solution. The TOMASys metamodel has been developed for representing the structure and its relation to the functional requirements of any autonomous system. The OM architecture is a blueprint for building a metacontroller according to the patterns. This metacontroller can be integrated on top of any component-based control architecture. At the core of its operation lies a TOMASys model of the control system. An engineering process and accompanying assets have been constructed to complete and exploit the architectural framework. The OM Engineering Process defines the process to follow to develop the metacontrol subsystem from the functional model of the controller of the autonomous system. The OMJava library provides a domain and application-independent implementation of an OM Metacontroller than can be used in the implementation phase of OMEP. Finally, the complete solution has been validated in the development of an autonomous mobile robot that incorporates an OM metacontroller. The functional selfawareness and adaptivity properties achieved thanks to the metacontrol system have been validated in different scenarios. In these scenarios the robot was able to overcome failures in the control system thanks to reconfigurations performed by the metacontroller.

Modelling spatial decisión support systems focused on the development of a data warehouse: applying software engineering

Nowadays, organizations have plenty of data stored in DB databases, which contain invaluable information. Decision Support Systems DSS provide the support needed to manage this information and planning médium and long-term ?the modus operandi? of these organizations. Despite the growing importance of these systems, most proposals do not include its total evelopment, mostly limiting itself on the development of isolated parts, which often have serious integration problems. Hence, methodologies that include models and processes that consider every factor are necessary. This paper will try to fill this void as it proposes an approach for developing spatial DSS driven by the development of their associated Data Warehouse DW, without forgetting its other components. To the end of framing the proposal different Engineering Software focus (The Software Engineering Process and Model Driven Architecture) are used, and coupling with the DB development methodology, (and both of them adapted to DW peculiarities). Finally, an example illustrates the proposal.

Towards a model-driven engineering approach of data mining

Nowadays, data mining is based on low-level specications of the employed techniques typically bounded to a specic analysis platform. Therefore, data mining lacks a modelling architecture that allows analysts to consider it as a truly software-engineering process. Here, we propose a model-driven approach based on (i) a conceptual modelling framework for data mining, and (ii) a set of model transformations to automatically generate both the data under analysis (via data-warehousing technology) and the analysis models for data mining (tailored to a specic platform). Thus, analysts can concentrate on the analysis problem via conceptual data-mining models instead of low-level programming tasks related to the underlying-platform technical details. These tasks are now entrusted to the model-transformations scaffolding.

Integrating the development of data mining and data warehouses via model-driven engineering

Data mining is one of the most important analysis techniques to automatically extract knowledge from large amount of data. Nowadays, data mining is based on low-level specifications of the employed techniques typically bounded to a specific analysis platform. Therefore, data mining lacks a modelling architecture that allows analysts to consider it as a truly software-engineering process. Bearing in mind this situation, we propose a model-driven approach which is based on (i) a conceptual modelling framework for data mining, and (ii) a set of model transformations to automatically generate both the data under analysis (that is deployed via data-warehousing technology) and the analysis models for data mining (tailored to a specific platform). Thus, analysts can concentrate on understanding the analysis problem via conceptual data-mining models instead of wasting efforts on low-level programming tasks related to the underlying-platform technical details. These time consuming tasks are now entrusted to the model-transformations scaffolding. The feasibility of our approach is shown by means of a hypothetical data-mining scenario where a time series analysis is required.

Process optimization of an aerobic upflow sludge blanket reactor system

Response of an aerobic upflow sludge blanket (AUSB) reactor system to the changes in operating conditions was investigated by varying two principle operating variables: the oxygenation pressure and the flow recirculation rate. The oxygenation pressure was varied between 0 and 25 psig (relative), while flow recirculation rates were between 1,300 and 600% correspondingly. The AUSB reactor system was able to handle a volumetric loading of as high as 3.8 kg total organic carbon (TOC)/m(3) day, with a removal efficiency of 92%. The rate of TOC removal by AUSB was highest at a pressure of 20 psig and it decreased when the pressure was increased to 25 psig and the flow recirculation rate was reduced to 600%. The TOC removal rate also decreased when the operating pressure was reduced to 0 and 15 psig, with corresponding increase in flow recirculation rates to 1,300 and 1,000%, respectively. Maintenance of a high dissolved oxygen level and a high flow recirculation rate was found to improve the substrate removal capacity of the AUSB system. The AUSB system was extremely effective in retaining the produced biomass despite a high upflow velocity and the overall sludge yield was only 0.24-0.32 g VSS/g TOC removed. However, the effluent TOC was relatively high due to the system's operation at a high organic loading.

Developing context-aware pervasive computing applications: Models and approach

There is growing interest in the use of context-awareness as a technique for developing pervasive computing applications that are flexible, adaptable, and capable of acting autonomously on behalf of users. However, context-awareness introduces a variety of software engineering challenges. In this paper, we address these challenges by proposing a set of conceptual models designed to support the software engineering process, including context modelling techniques, a preference model for representing context-dependent requirements, and two programming models. We also present a software infrastructure and software engineering process that can be used in conjunction with our models. Finally, we discuss a case study that demonstrates the strengths of our models and software engineering approach with respect to a set of software quality metrics.

A rule based model for the objective determination of the e-business requirements of a manufacturing organisation

The starting point of this research was the belief that manufacturing and similar industries need help with the concept of e-business, especially in assessing the relevance of possible e-business initiatives. The research hypotheses was that it should be possible to produce a systematic model that defines, at a useful level of detail, the probable e-business requirements of an organisation based on objective criteria with an accuracy of 85%-90%. This thesis describes the development and validation of such a model. A preliminary model was developed from a variety of sources, including a survey of current and planned e-business activity and representative examples of e-business material produced by e-business solution providers. The model was subject to a process of testing and refinement based on recursive case studies, with controls over the improving accuracy and stability of the model. Useful conclusions were also possible as to the relevance of e-business functions to the case study participants themselves. Techniques were evolved to synthesise the e-business requirements of an organisation and present them at a management summary level of detail. The results of applying these techniques to all the case studies used in this research were discussed. The conclusion of the research was that the case study methodology employed was successful. A model was achieved suitable for practical application in a manufacturing organisation requiring help with a requirements definition process.

Supporting decision-making for self-adaptive systems:from goal models to dynamic decision networks

Context/Motivation - Different modeling techniques have been used to model requirements and decision-making of self-adaptive systems (SASs). Specifically, goal models have been prolific in supporting decision-making depending on partial and total fulfilment of functional (goals) and non-functional requirements (softgoals). Different goalrealization strategies can have different effects on softgoals which are specified with weighted contribution-links. The final decision about what strategy to use is based, among other reasons, on a utility function that takes into account the weighted sum of the different effects on softgoals. Questions/Problems - One of the main challenges about decisionmaking in self-adaptive systems is to deal with uncertainty during runtime. New techniques are needed to systematically revise the current model when empirical evidence becomes available from the deployment. Principal ideas/results - In this paper we enrich the decision-making supported by goal models by using Dynamic Decision Networks (DDNs). Goal realization strategies and their impact on softgoals have a correspondence with decision alternatives and conditional probabilities and expected utilities in the DDNs respectively. Our novel approach allows the specification of preferences over the softgoals and supports reasoning about partial satisfaction of softgoals using probabilities. We report results of the application of the approach on two different cases. Our early results suggest the decision-making process of SASs can be improved by using DDNs. © 2013 Springer-Verlag.

Requirements-aware systems:a research agenda for RE for self-adaptive systems

Requirements are sensitive to the context in which the system-to-be must operate. Where such context is well-understood and is static or evolves slowly, existing RE techniques can be made to work well. Increasingly, however, development projects are being challenged to build systems to operate in contexts that are volatile over short periods in ways that are imperfectly understood. Such systems need to be able to adapt to new environmental contexts dynamically, but the contextual uncertainty that demands this self-adaptive ability makes it hard to formulate, validate and manage their requirements. Different contexts may demand different requirements trade-offs. Unanticipated contexts may even lead to entirely new requirements. To help counter this uncertainty, we argue that requirements for self-adaptive systems should be run-time entities that can be reasoned over in order to understand the extent to which they are being satisfied and to support adaptation decisions that can take advantage of the systems' self-adaptive machinery. We take our inspiration from the fact that explicit, abstract representations of software architectures used to be considered design-time-only entities but computational reflection showed that architectural concerns could be represented at run-time too, helping systems to dynamically reconfigure themselves according to changing context. We propose to use analogous mechanisms to achieve requirements reflection. In this paper we discuss the ideas that support requirements reflection as a means to articulate some of the outstanding research challenges.

Software engineering for self-adaptive systems:a research roadmap

The goal of this roadmap paper is to summarize the state-of-the-art and to identify critical challenges for the systematic software engineering of self-adaptive systems. The paper is partitioned into four parts, one for each of the identified essential views of self-adaptation: modelling dimensions, requirements, engineering, and assurances. For each view, we present the state-of-the-art and the challenges that our community must address. This roadmap paper is a result of the Dagstuhl Seminar 08031 on "Software Engineering for Self-Adaptive Systems," which took place in January 2008. © 2009 Springer Berlin Heidelberg.

Run-time model evaluation for requirements model-driven self-adaptation

A self-adaptive system adjusts its configuration to tolerate changes in its operating environment. To date, requirements modeling methodologies for self-adaptive systems have necessitated analysis of all potential system configurations, and the circumstances under which each is to be adopted. We argue that, by explicitly capturing and modelling uncertainty in the operating environment, and by verifying and analysing this model at runtime, it is possible for a system to adapt to tolerate some conditions that were not fully considered at design time. We showcase in this paper our tools and research results. © 2012 IEEE.