315,526 research outputs found

    Enhancing modeling and change support for process families through change patterns

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    The increasing adoption of process-aware information systems (PAISs), together with the variability of business processes (BPs), has resulted in large collections of related process model variants (i.e., process families). To effectively deal with process families, several proposals (e.g., C-EPC, Provop) exist that extend BP modeling languages with variability-specific constructs. While fostering reuse and reducing modeling efforts, respective constructs imply additional complexity and demand proper support for process designers when creating and modifying process families. Recently, generic and language independent adaptation patterns were successfully introduced for creating and evolving single BP models. However, they are not sufficient to cope with the specific needs for modeling and evolving process families. This paper suggests a complementary set of generic and language-independent change patterns specifically tailored to the needs of process families. When used in combination with existing adaptation patterns, change patterns for process families will enable the modeling and evolution of process families at a high-level of abstraction. Further, they will serve as reference for implementing tools or comparing proposals managing process families. © 2013 Springer-Verlag.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. (2013). Enhancing modeling and change support for process families through change patterns. En Enterprise, Business-Process and Information Systems Modeling, BPMDS 2013. Springer Verlag. 246-260. https://doi.org/10.1007/978-3-642-38484-4_18S246260van der Aalst, W.M.P., ter Hofstede, A.H.M., Barros, B.: Workflow Patterns. Distributed and Parallel Databases 14(1), 5–51 (2003)Aghakasiri, Z., Mirian-Hosseinabadi, S.H.: Workflow change patterns: Opportunities for extension and reuse. In: Proc. SERA 2009, pp. 265–275 (2009)Ayora, C., Torres, V., Reichert, M., Weber, B., Pelechano, V.: Towards run-time flexibility for process families: Open issues and research challenges. In: La Rosa, M., Soffer, P. (eds.) BPM 2012 Workshops. LNBIP, vol. 132, pp. 477–488. Springer, Heidelberg (2013)Ayora, C., Torres, V., Weber, B., Reichert, M., Pelechano, V.: Change patterns for process families. Technical Report, PROS-TR-2012-06, http://www.pros.upv.es/technicalreports/PROS-TR-2012-06.pdfDadam, P., Reichert, M.: The ADEPT project: a decade of research and development for robust and flexible process support. Com. Sci. - R&D 23, 81–97 (2009)Dijkman, R., La Rosa, M., Reijers, H.A.: Managing large collections of business process models - Current techniques and challenges. Comp. in Ind. 63(2), 91–97 (2012)Döhring, M., Zimmermann, B., Karg, L.: Flexible workflows at design- and runtime using BPMN2 adaptation patterns. In: Abramowicz, W. (ed.) BIS 2011. LNBIP, vol. 87, pp. 25–36. Springer, Heidelberg (2011)Gottschalk, F.: Configurable process models. Ph.D. thesis, Eindhoven University of Technology, The Netherlands (2009)Grambow, G., Oberhauser, R., Reichert, M.: Contextual injection of quality measures into software engineering processes. Intl. J. Adv. in Software 4, 76–99 (2011)Gschwind, T., Koehler, J., Wong, J.: Applying patterns during business process modeling. In: Dumas, M., Reichert, M., Shan, M.-C. (eds.) BPM 2008. LNCS, vol. 5240, pp. 4–19. Springer, Heidelberg (2008)Günther, C.W., Rinderle, S., Reichert, M., van der Aalst, W.M.P.: Change mining in adaptive process management systems. In: Meersman, R., Tari, Z. (eds.) OTM 2006. LNCS, vol. 4275, pp. 309–326. Springer, Heidelberg (2006)Hallerbach, A., Bauer, T., Reichert, M.: Context-based configuration of process variants. In: Proc. TCoB 2008, pp. 31–40 (2008)Hallerbach, A., Bauer, T., Reichert, M.: Capturing variability in business process models: the Provop approach. J. of Software Maintenance 22(6-7), 519–546 (2010)Kitchenham, B., Charters, S.: Guidelines for performing Systematic Literature Reviews in Software Engineering, Technical Report EBSE/EPIC–2007–01 (2007)Kulkarni, V., Barat, S., Roychoudhury, S.: Towards business application product lines. In: France, R.B., Kazmeier, J., Breu, R., Atkinson, C. (eds.) MODELS 2012. LNCS, vol. 7590, pp. 285–301. Springer, Heidelberg (2012)Küster, J.M., Gerth, C., Förster, A., Engels, G.: Detecting and resolving process model differences in the absence of a change log. In: Dumas, M., Reichert, M., Shan, M.-C. (eds.) BPM 2008. LNCS, vol. 5240, pp. 244–260. Springer, Heidelberg (2008)Küster, J.M., Gerth, C., Engels, G.: Dynamic computation of change operations in version management of business process models. In: Kühne, T., Selic, B., Gervais, M.-P., Terrier, F. (eds.) ECMFA 2010. LNCS, vol. 6138, pp. 201–216. Springer, Heidelberg (2010)Lanz, A., Weber, B., Reichert, M.: Time patterns for process-aware information systems. Requirements Engineering, 1–29 (2012)La Rosa, M., van der Aalst, W.M.P., Dumas, M., ter Hofstede, A.H.M.: Questionnaire-based variability modeling for system configuration. Software and System Modeling 8(2), 251–274 (2009)Lerner, B.S., Christov, S., Osterweil, L.J., Bendraou, R., Kannengiesser, U., Wise, A.: Exception Handling Patterns for Process Modeling. IEEE Transactions on Software Engineering 36(2), 162–183 (2010)Li, C., Reichert, M., Wombacher, A.: Mining business process variants: Challenges, scenarios, algorithms. Data Knowledge & Engineering 70(5), 409–434 (2011)Marrella, A., Mecella, M., Russo, A.: Featuring automatic adaptivity through workflow enactment and planning. In: Proc. CollaborateCom 2011, pp. 372–381 (2011)Müller, D., Herbst, J., Hammori, M., Reichert, M.: IT support for release management processes in the automotive industry. In: Dustdar, S., Fiadeiro, J.L., Sheth, A.P. (eds.) BPM 2006. LNCS, vol. 4102, pp. 368–377. Springer, Heidelberg (2006)Reichert, M., Weber, B.: Enabling flexibility in process-aware information systems: challenges, methods, technologies. Springer (2012)Reinhartz-Berger, I., Soffer, P., Sturm, A.: Organizational reference models: supporting an adequate design of local business processes. IBPIM 4(2), 134–149 (2009)Rosemann, M., van der Aalst, W.M.P.: A configurable reference modeling language. Information Systems 32(1), 1–23 (2007)Russell, N., ter Hofstede, A.H.M., Edmond, D., van der Aalst, W.M.P.: Workflow data patterns. Technical Report FIT-TR-2004-01, Queensland Univ. of Technology (2004)Russell, N., ter Hofstede, A.H.M., Edmond, D., van der Aalst, W.M.P.: Workflow resource patterns. Technical Report WP 127, Eindhoven Univ. of Technology (2004)Russell, N., van der Aalst, W.M.P., ter Hofstede, A.H.M.: Workflow Exception Patterns. In: Martinez, F.H., Pohl, K. (eds.) CAiSE 2006. 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    Report from GI-Dagstuhl Seminar 16394: Software Performance Engineering in the DevOps World

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    This report documents the program and the outcomes of GI-Dagstuhl Seminar 16394 "Software Performance Engineering in the DevOps World". The seminar addressed the problem of performance-aware DevOps. Both, DevOps and performance engineering have been growing trends over the past one to two years, in no small part due to the rise in importance of identifying performance anomalies in the operations (Ops) of cloud and big data systems and feeding these back to the development (Dev). However, so far, the research community has treated software engineering, performance engineering, and cloud computing mostly as individual research areas. We aimed to identify cross-community collaboration, and to set the path for long-lasting collaborations towards performance-aware DevOps. The main goal of the seminar was to bring together young researchers (PhD students in a later stage of their PhD, as well as PostDocs or Junior Professors) in the areas of (i) software engineering, (ii) performance engineering, and (iii) cloud computing and big data to present their current research projects, to exchange experience and expertise, to discuss research challenges, and to develop ideas for future collaborations

    Quality-aware model-driven service engineering

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    Service engineering and service-oriented architecture as an integration and platform technology is a recent approach to software systems integration. Quality aspects ranging from interoperability to maintainability to performance are of central importance for the integration of heterogeneous, distributed service-based systems. Architecture models can substantially influence quality attributes of the implemented software systems. Besides the benefits of explicit architectures on maintainability and reuse, architectural constraints such as styles, reference architectures and architectural patterns can influence observable software properties such as performance. Empirical performance evaluation is a process of measuring and evaluating the performance of implemented software. We present an approach for addressing the quality of services and service-based systems at the model-level in the context of model-driven service engineering. The focus on architecture-level models is a consequence of the black-box character of services

    Model Based Development of Quality-Aware Software Services

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    Modelling languages and development frameworks give support for functional and structural description of software architectures. But quality-aware applications require languages which allow expressing QoS as a first-class concept during architecture design and service composition, and to extend existing tools and infrastructures adding support for modelling, evaluating, managing and monitoring QoS aspects. In addition to its functional behaviour and internal structure, the developer of each service must consider the fulfilment of its quality requirements. If the service is flexible, the output quality depends both on input quality and available resources (e.g., amounts of CPU execution time and memory). From the software engineering point of view, modelling of quality-aware requirements and architectures require modelling support for the description of quality concepts, support for the analysis of quality properties (e.g. model checking and consistencies of quality constraints, assembly of quality), tool support for the transition from quality requirements to quality-aware architectures, and from quality-aware architecture to service run-time infrastructures. Quality management in run-time service infrastructures must give support for handling quality concepts dynamically. QoS-aware modeling frameworks and QoS-aware runtime management infrastructures require a common evolution to get their integration
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