277,102 research outputs found

    Addressing the evolution of automated user behaviour patterns by runtime model interpretation

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    The final publication is available at Springer via http://dx.doi.org/10.1007/s10270-013-0371-3The use of high-level abstraction models can facilitate and improve not only system development but also runtime system evolution. This is the idea of this work, in which behavioural models created at design time are also used at runtime to evolve system behaviour. These behavioural models describe the routine tasks that users want to be automated by the system. However, usersÂż needs may change after system deployment, and the routine tasks automated by the system must evolve to adapt to these changes. To facilitate this evolution, the automation of the specified routine tasks is achieved by directly interpreting the models at runtime. This turns models into the primary means to understand and interact with the system behaviour associated with the routine tasks as well as to execute and modify it. Thus, we provide tools to allow the adaptation of this behaviour by modifying the models at runtime. This means that the system behaviour evolution is performed by using high-level abstractions and avoiding the costs and risks associated with shutting down and restarting the system.This work has been developed with the support of MICINN, under the project EVERYWARE TIN2010-18011, and the support of the Christian Doppler Forschungsgesellschaft and the BMWFJ, Austria.Serral Asensio, E.; Valderas Aranda, PJ.; Pelechano Ferragud, V. (2013). Addressing the evolution of automated user behaviour patterns by runtime model interpretation. Software and Systems Modeling. https://doi.org/10.1007/s10270-013-0371-3SWeiser, M.: The computer of the 21st century. Sci. Am. 265, 66–75 (1991)Serral, E., Valderas, P., Pelechano, V.: Context-adaptive coordination of pervasive services by interpreting models during runtime. Comput. J. 56(1), 87–114 (2013)Ajila, S.A., Alam, S.: Using a formal language constructs for software model evolution. In: Third IEEE International Conference on Semantic Computing (IEEE-ICSC 2009). Berkeley, CA, USA, pp. 390–395 (2009)Bennett, K., Rajlich, V.: Software Maintenance and Evolution: A Roadmap. In: 22nd International Conference on Software Engineering (ICSE 2000). Limerick, Ireland, pp. 75–87 (2000)Mens, T.: The ERCIM working group on software evolution: the past and the future. In: Proceedings of the Joint International and Annual ERCIM Workshops on Principles of Software Evolution (IWPSE) and Software Evolution (Evol) Workshops. ACM (2009)Mens, T., Wermelinger, M., Ducasse, S., Demeyer, S., Hirschfeld, R.: Challenges in software evolution. In: Report of the ChaSE 2005 Workshop Organised by the ERCIM Working Group on Software Evolution. IWPSE-05. Lisbon, Portugal, pp. 13–22 (2005)Hirschfeld, R., Kawamura, K., Berndt, H.: Dynamic service adaptation for runtime system extensions. In: Wireless On-Demand Network Systems, pp. 227–240. Springer, Berlin, Heidelberg, Madonna di Campiglio, Italy (2004)Lientz, B.P., Swanson, E.B.: Software maintenance management: a study of the maintenance of computer applications software in 487 data processing organizations. Addison-Wesley, Reading, MA (1980)Buckley, J., Mens, T., Zenger, M., Rashid, A., Kniesel, G.: Towards a taxonomy of software change. J. Softw. Maint. Evolut. Res. Pract. 17(5), 309–332 (2003)Hardian, B., Indulska, J., Henricksen, K.: Balancing autonomy and user control in context-aware systems—a survey. In: CoMoRea, IEEE PerCom Workshops 2006. (2006)Biegel, G., Cahill, V.: A framework for developing mobile, context-aware applications. In: The 2nd IEEE Conference on Pervasive Computing and Communication (PerCom), pp. 361–365 (2004)Hofer, T., Schwinger, W., Pichler, M., Leonhartsberger, G., Altmann, J.: Context-awareness on mobile devices—the hydrogen approach. In: The 36th Annual Hawaii International Conference on System Sciences, pp. 292–302 (2002)Dey, A.K.: Understanding and using context. Pers. Ubiquitous Comput. 5(1), 4–7 (2001)Sheng, Q.Z., Benatallah, B.: ContextUML: a UML-based modelling language for model-driven development of context-aware web services. In: Proceedings of the International Conference on Mobile, Business (ICMB’05). pp. 206–212 (2005)Henricksen, K., Indulska, J.: A software engineering framework for context-aware pervasive computing. In: Proceedings of the Second IEEE Annual Conference on Pervasive Computing and Communications (PerCom 2004), pp. 77–86. IEEE, Orlando, FL, USA (2004)Baldauf, M., Dustdar, S., Rosenberg, F.: A survey on context-aware systems. Int. J. Ad Hoc Ubiquitous Comput. 2(4), 263–277 (2007)Ye, J., Coyle, L., Dobson, S., Nixon, P.: Ontology-based models in pervasive computing systems. Knowl. Eng. Rev. 22(4), 315–347 (2007)Chen, H., Finin, T., Joshi, A.: An ontology for context-aware pervasive computing environments. Special Issue on Ontologies for Distributed Systems. Knowl. Eng. Rev. 18(3), 197–207 (2004)Welty, C., McGuinness, D.L.: OWL Web Ontology Language Guide. vol. W3C Recomm. W3C Recommendation 10 Feb 2004 (2004)Shepherd, A.: HTA as a framework for task analysis. Ergonomics 41, 1537–1552 (1998)Serral, E., Valderas, P., Pelechano, V.: Towards the model driven development of context-aware pervasive systems. Special Issue on Context Modelling, Reasoning and Management. PMC 6(2), 254–280 (2010)Serral, E.: Automating Routine Tasks in Smart Environments. A Context-aware Model-driven Approach, Technical University of Valencia (2011)Mellor, S.J., Balcer, M.J.: Executable UML: A Foundation for Model Driven Architecture. Addison-Wesley, Indianapolis (2002)Muñoz, J., Ferragud, D.V.P.: Model Driven Development of Pervasive Systems. Building a Software Factory. Universidad PolitĂ©cnica de Valencia, Valencia (2008)Juric, M.B., Sarang, P.: Business Process Execution Language for Web Services: BPEL and BPEL4WS (2006)Loke, S.W., Smanchat, S., Ling, S., Indrawan, M.: Formal mirror models: an approach to just-in-time reasoning for device ecologies. Int. J. Smart Home 2(1), 15–32 (2008)Code Generation conference. http://www.codegeneration.net/cg2010/ (2010)Guy, M.: Report 2: API Good Practice Good practice for provision of and consuming APIs. UKOLN (2009)Bloch, J.: How to design a good API and why it matters. pp. 506–507 (2005)Sirin, E., Parsia, B., Grau, B.C., Kalyanpur, A., Katz, Y.: Pellet: A practical OWL-DL reasoner. J. Web Semant. 5(2), 51–53 (2007)Bernstein, P.: Multiversion concurrency control—theory and algorithms. ACM Trans. Database Syst. 8(4), 465–484 (1983)Cooper, S., Dann, W., Pausch, R.: Alice: a 3-D tool for introductory programming concepts. J. Comput. Sci. Coll. 15, 107–116 (2000)PĂ©rez, F., Valderas, P.: Allowing end-users to actively participate within the elicitation of pervasive system requirements through immediate visualization. In: Fourth International Workshop on Requirements Engineering Visualization (REV), pp. 31–40. IEEE, Atlanta, Georgia, USA (2009)Lieberman, H., PaternĂł, F., Wulf, V.: End User Development. Springer, Dordrecht (2006)Nielsen, J.: Usability Engineering. Morgan Kaufmann Publishers Inc, San Francisco (1993)Van Welie, M., TrĂŠtteberg, H.: Interaction Patterns in User, Interfaces. pp. 13–16 (2000)Galitz, W.O.: The Essential Guide to User Interface Design: An Introduction to GUI Design Principles and Techniques. Wiley, New York (2002)Kitchenham, B., Pickard, L., Pfleeger, S.L.: Case studies for method and tool evaluation. Softw. IEEE 12(4), 52–62 (1995)Wohlin, C., Runeson, P., Höst, M., Ohlsson, M.C., Regnell, B., WesslĂ©n, A.: Experimentation in Software Engineering. Springer, Berlin (2012)Jones, J.V.: Applied software measurement: assuring productivity & quality (2nd ed’97). McGraw-Hill, New York (1997)Strang, T., Linnhoff-Popien, C.: A context modeling survey. In: First International Workshop on Advanced Context Modelling, Reasoning And Management at UbiComp (2004)Lewis, J.R.: Psychometric Evaluation of an After-Scenario Questionnaire for Computer Usability Studies? The ASQ. SIGCHI Bulletin (1991)Cook, D.J., Youngblood, M., Heierman, I.I.I.E.O., Gopalratnam, K., Rao, S., Litvin, A., Khawaja, F.: MavHome: An Agent-based Smart Home. In: First IEEE International Conference on Pervasive Computing and, Communications (PerCom’03), pp. 521–524 (2003)Hagras, H., Callaghan, V., Colley, M., Clarke, G., Pounds-Cornish, A., Duman, H.: Creating an ambient-intelligence environment using embedded agents. IEEE Intell. Syst. 19(6), 12–20 (2004)Rashidi, P., Cook, D.J.: Keeping the resident in the loop: adapting the smart home to the user. IEEE Trans. Syst. Man Cybern. 39(5), 949–959 (2009)Webb, G.I., Pazzani, M.J., Billsus, D.: Machine learning for user modeling. User model. User-Adapt Interact. 11(1–2), 19–29 (2001)Valiant, L.G.: A theory of the learnable. Commun. ACM 27(11), 1134–1142 (1984)Serral, E., Valderas, P., Pelechano, V.: (2011) Improving the cold-start problem in user task automation by using models at runtime. In: Information Systems Development, pp. 671–683. (2011)GarcĂ­a-Herranz, M., Haya, P.A., Esquivel, A., Montoro, G., AlamĂĄn, X.: Easing the smart home: semi-automatic adaptation in perceptive environments. J. Univers. Comput. Sci. 14(9), 1529–1544 (2008)Henricksen, K., Indulska, J., Rakotonirainy, A.: Using context and preferences to implement self-adapting pervasive computing applications. Sofw. Pract. Exp. 36(11–12), 1307–1330 (2006)Johnson, P.: Tasks and situations: considerations for models and design principles in human computer interaction, pp. 1199–1204. HCI International. Munich, Germany (1999)Cook, D.J., Das, S.K.: Smart environments: technologies, protocols, and applications, vol. 43. Wiley-Interscience, New York (2005)PaternĂČ, F.: ConcurTaskTrees: an Engineered approach to model-based design of interactive systems. In: The Handbook of Analysis for Human-Computer Interaction, pp. 483–500 (2002)Pribeanu, C., Limbourg, Q., Vanderdonckt1, J.: Task modelling for context-sensitive user interfaces. In: Interactive Systems: Design, Specification, and Verification (DSV-IS), pp. 49–68. Springer, Berlin, Heidelberg 2001, Glasgow, Scotland, UK (2001)Souchon, N., Limbourg, Q., Vanderdonckt., J.: Task modelling in multiple contexts of use. In: Interactive Systems: Design, Specification, and Verification (DSV-IS), pp. 59–73 (2002)Huang, R., Cao, Q., Zhou, J., Sun, D., Su, Q.: Context-aware active task discovery for pervasive computing. In: International Conference on Computer Science and Software Engineering, pp. 463–466. IEEE, Wuhan, China (2008)Sousa, J.P., Poladian, V., Garlan, D., Schmerl, B.: Task-based adaptation for ubiquitous computing. IEEE Trans. Syst. Man Cybern. 36(3), 328–340 (2006)Masuoka, R., Parsia, B., Labrou, Y.: Task Computing—The Semantic Web Meets Pervasive Computing. In: 2nd International Semantic Web Conference on the Semantic Web (ISWC 2003), pp. 866–881. vol. LNCS 2870. Sanibel Island, FL, USA (2003)Oreizy, P., Gorlick, M.M., Taylor, R.N., Heimbigner, D., Johnson, G., Medvidovic, N., Quilici, A., Rosenblum, D.S., Wolf, A.L.: An architecture-based approach to self-adaptive software. IEEE Intell. Syst. Their Appl. 14(3), 54–62 (1999)Floch, J., Hallsteinsen, S., Stav, E., Eliassen, F., Lund, K., GjĂžrven, E.: Using Architecture Models for Runtime Adaptability. IEEE Software. 23(2), 62–70 (2006)Morin, B., JĂ©zĂ©quel, J.-M., Fleurey, F., Solberg, A.: Models at runtime to support dynamic adaptation. IEEE Comput. Soc. pp. 46–53 (2009)Cetina, C., Giner, P., Fons, J., Pelechano, V.: Using feature models for developing self-configuring smart homes. In: Fifth International Conference on Autonomic and Autonomous Systems, pp. 179–188. IEEE, Valencia, Spain (2009)Garlan, D., Schmerl, B.: Using architectural models at runtime: research challenges. In: Proceedings of the European Workshop on Software Architectures, pp. 200–205. Springer, Berlin, Heidelberg, St Andrews, UK (2004)Blumendorf, M., Lehmann, G., Feuerstack, S., Albayrak, S.: Executable models for human-computer interaction. In: Interactive Systems, Design, Specification, and Verification Workshop (DSV-IS 2008), pp. 238–251. Springer Berlin Heidelberg, Kingston, Canada (2008)Ballagny, C., Hameurlain, N., Barbier, F.: MOCAS: a state-based component model for self-adaptation. In: Third IEEE International Conference on Self-Adaptive and Self-Organizing Systems, pp. 206–215. IEEE, San Francisco, California (2009)Amoui, M., Derakhshanmanesh, M., Ebert, J., Tahvildari, L.: Achieving dynamic adaptation via management and interpretation of runtime models. J. Syst. Softw. 85(12), 2720–2737 (2012)Blair, G., Bencomo, N., France, R.B.: [email protected]. IEEE Comput. 42, 22–27 (2009)Zhang, J., Cheng, B.H.C.: Model based development of dynamically adaptive software. In: International Conference on Software Engineering (ICSE’06), pp. 371–380. ACM, Shanghai, China (2006

    A Framework for Evaluating Model-Driven Self-adaptive Software Systems

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    In the last few years, Model Driven Development (MDD), Component-based Software Development (CBSD), and context-oriented software have become interesting alternatives for the design and construction of self-adaptive software systems. In general, the ultimate goal of these technologies is to be able to reduce development costs and effort, while improving the modularity, flexibility, adaptability, and reliability of software systems. An analysis of these technologies shows them all to include the principle of the separation of concerns, and their further integration is a key factor to obtaining high-quality and self-adaptable software systems. Each technology identifies different concerns and deals with them separately in order to specify the design of the self-adaptive applications, and, at the same time, support software with adaptability and context-awareness. This research studies the development methodologies that employ the principles of model-driven development in building self-adaptive software systems. To this aim, this article proposes an evaluation framework for analysing and evaluating the features of model-driven approaches and their ability to support software with self-adaptability and dependability in highly dynamic contextual environment. Such evaluation framework can facilitate the software developers on selecting a development methodology that suits their software requirements and reduces the development effort of building self-adaptive software systems. This study highlights the major drawbacks of the propped model-driven approaches in the related works, and emphasise on considering the volatile aspects of self-adaptive software in the analysis, design and implementation phases of the development methodologies. In addition, we argue that the development methodologies should leave the selection of modelling languages and modelling tools to the software developers.Comment: model-driven architecture, COP, AOP, component composition, self-adaptive application, context oriented software developmen

    Technical debt-aware elasticity management in cloud computing environments

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    Elasticity is the characteristic of cloud computing that provides the underlying primitives to dynamically acquire and release shared computational resources on demand. Moreover, it unfolds the advantage of the economies of scale in the cloud, which refers to a drop in the average costs of these computing capacities as a result of the dynamic sharing capability. However, in practice, it is impossible to achieve elasticity adaptations that obtain perfect matches between resource supply and demand, which produces dynamic gaps at runtime. Moreover, elasticity is only a capability, and consequently it calls for a management process with far-sighted economics objectives to maximise the value of elasticity adaptations. Within this context, we advocate the use of an economics-driven approach to guide elasticity managerial decisions. We draw inspiration from the technical debt metaphor in software engineering and we explore it in a dynamic setting to present a debt-aware elasticity management. In particular, we introduce a managerial approach that assesses the value of elasticity decisions to adapt the resource provisioning. Additionally, the approach pursues strategic decisions that value the potential utility produced by the unavoidable gaps between the ideal and actual resource provisioning over time. As part of experimentation, we built a proof of concept and the results indicate that value-oriented adaptations in elasticity management lead to a better economics performance in terms of lower operating costs and higher quality of service over time. This thesis contributes (i) an economics-driven approach towards elasticity management; (ii) a technical debt-aware model to reason about elasticity adaptations; (iii) a debt-aware learning elasticity management approach; and (iv) a multi-agent elasticity management for multi-tenant applications hosted in the cloud

    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

    City Data Fusion: Sensor Data Fusion in the Internet of Things

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    Internet of Things (IoT) has gained substantial attention recently and play a significant role in smart city application deployments. A number of such smart city applications depend on sensor fusion capabilities in the cloud from diverse data sources. We introduce the concept of IoT and present in detail ten different parameters that govern our sensor data fusion evaluation framework. We then evaluate the current state-of-the art in sensor data fusion against our sensor data fusion framework. Our main goal is to examine and survey different sensor data fusion research efforts based on our evaluation framework. The major open research issues related to sensor data fusion are also presented.Comment: Accepted to be published in International Journal of Distributed Systems and Technologies (IJDST), 201
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