831 research outputs found

    Process mining methodology for health process tracking using real-time indoor location systems

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    [EN] The definition of efficient and accurate health processes in hospitals is crucial for ensuring an adequate quality of service. Knowing and improving the behavior of the surgical processes in a hospital can improve the number of patients that can be operated on using the same resources. However, the measure of this process is usually made in an obtrusive way, forcing nurses to get information and time data, affecting the proper process and generating inaccurate data due to human errors during the stressful journey of health staff in the operating theater. The use of indoor location systems can take time information about the process in an unobtrusive way, freeing nurses, allowing them to engage in purely welfare work. However, it is necessary to present these data in a understandable way for health professionals, who cannot deal with large amounts of historical localization log data. The use of process mining techniques can deal with this problem, offering an easily understandable view of the process. In this paper, we present a tool and a process mining-based methodology that, using indoor location systems, enables health staff not only to represent the process, but to know precise information about the deployment of the process in an unobtrusive and transparent way. We have successfully tested this tool in a real surgical area with 3613 patients during February, March and April of 2015.The authors want to acknowledge the work MySphera Company and Hospital General for their invaluable support. This work was supported in part by several projects; FASyS-Absolutely Safe and Healthy Factory (Spanish Ministry of Industry. CEN-20091034), MOSAIC-Models and simulation techniques for discovering diabetes influence factors (ICT-FP7-600914) and HEARTWAYS-Advanced Solutions for Supporting Cardiac Patients in Rehabilitation (ICT-SME-315659) EU Projects; and organizations like Tecnologias para la Salud y el Bienestar (TSB S.A.) and the Universitat Politecnica de Valencia.Fernández Llatas, C.; Lizondo, A.; Montón Sánchez, E.; Benedí Ruiz, JM.; Traver Salcedo, V. (2015). Process mining methodology for health process tracking using real-time indoor location systems. Sensors. 12:29821-29840. https://doi.org/10.3390/s151229769S298212984012Weske, M., van der Aalst, W. M. P., & Verbeek, H. M. W. (2004). Advances in business process management. Data & Knowledge Engineering, 50(1), 1-8. doi:10.1016/j.datak.2004.01.001Davidoff, F., Haynes, B., Sackett, D., & Smith, R. (1995). Evidence based medicine. BMJ, 310(6987), 1085-1086. doi:10.1136/bmj.310.6987.1085Reilly, B. M. (2004). The essence of EBM. BMJ, 329(7473), 991-992. doi:10.1136/bmj.329.7473.991Weiland, D. E. (1997). Why use clinical pathways rather than practice guidelines? The American Journal of Surgery, 174(6), 592-595. doi:10.1016/s0002-9610(97)00196-7Hunter, B., & Segrott, J. (2008). Re-mapping client journeys and professional identities: A review of the literature on clinical pathways. International Journal of Nursing Studies, 45(4), 608-625. doi:10.1016/j.ijnurstu.2007.04.001Lenz, R., Blaser, R., Beyer, M., Heger, O., Biber, C., Bäumlein, M., & Schnabel, M. (2007). IT support for clinical pathways—Lessons learned. International Journal of Medical Informatics, 76, S397-S402. doi:10.1016/j.ijmedinf.2007.04.012Blaser, R., Schnabel, M., Biber, C., Bäumlein, M., Heger, O., Beyer, M., … Kuhn, K. A. (2007). Improving pathway compliance and clinician performance by using information technology. International Journal of Medical Informatics, 76(2-3), 151-156. doi:10.1016/j.ijmedinf.2006.07.006Schuld, J., Schäfer, T., Nickel, S., Jacob, P., Schilling, M. K., & Richter, S. (2011). Impact of IT-supported clinical pathways on medical staff satisfaction. A prospective longitudinal cohort study. International Journal of Medical Informatics, 80(3), 151-156. doi:10.1016/j.ijmedinf.2010.10.012Rebuge, Á., & Ferreira, D. R. (2012). Business process analysis in healthcare environments: A methodology based on process mining. Information Systems, 37(2), 99-116. doi:10.1016/j.is.2011.01.003Fernández-Llatas, C., Meneu, T., Traver, V., & Benedi, J.-M. (2013). Applying Evidence-Based Medicine in Telehealth: An Interactive Pattern Recognition Approximation. International Journal of Environmental Research and Public Health, 10(11), 5671-5682. doi:10.3390/ijerph10115671Schilling, M., Richter, S., Jacob, P., & Lindemann, W. (2006). Klinische Behandlungspfade. DMW - Deutsche Medizinische Wochenschrift, 131(17), 962-967. doi:10.1055/s-2006-939876Zannini, L., Cattaneo, C., Peduzzi, P., Lopiccoli, S., & Auxilia, F. (2012). Experimenting clinical pathways in general practice: a focus group investigation with Italian general practitioners. Journal of Public Health Research, 1(2), 30. doi:10.4081/jphr.2012.e30Rubin, H. R. (2001). The advantages and disadvantages of process-based measures of health care quality. International Journal for Quality in Health Care, 13(6), 469-474. doi:10.1093/intqhc/13.6.469Liu, H., Darabi, H., Banerjee, P., & Liu, J. (2007). Survey of Wireless Indoor Positioning Techniques and Systems. IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews), 37(6), 1067-1080. doi:10.1109/tsmcc.2007.905750Li, N., & Becerik-Gerber, B. (2011). Performance-based evaluation of RFID-based indoor location sensing solutions for the built environment. Advanced Engineering Informatics, 25(3), 535-546. doi:10.1016/j.aei.2011.02.004Curran, K., Furey, E., Lunney, T., Santos, J., Woods, D., & McCaughey, A. (2011). An evaluation of indoor location determination technologies. Journal of Location Based Services, 5(2), 61-78. doi:10.1080/17489725.2011.562927Fernández-Llatas, C., Benedi, J.-M., García-Gómez, J., & Traver, V. (2013). Process Mining for Individualized Behavior Modeling Using Wireless Tracking in Nursing Homes. Sensors, 13(11), 15434-15451. doi:10.3390/s131115434Stübig, T., Zeckey, C., Min, W., Janzen, L., Citak, M., Krettek, C., … Gaulke, R. (2014). Effects of a WLAN-based real time location system on outpatient contentment in a Level I trauma center. International Journal of Medical Informatics, 83(1), 19-26. doi:10.1016/j.ijmedinf.2013.10.001Najera, P., Lopez, J., & Roman, R. (2011). Real-time location and inpatient care systems based on passive RFID. Journal of Network and Computer Applications, 34(3), 980-989. doi:10.1016/j.jnca.2010.04.011Huang, Z., Dong, W., Ji, L., Gan, C., Lu, X., & Duan, H. (2014). Discovery of clinical pathway patterns from event logs using probabilistic topic models. Journal of Biomedical Informatics, 47, 39-57. doi:10.1016/j.jbi.2013.09.003Caron, F., Vanthienen, J., Vanhaecht, K., Limbergen, E. V., De Weerdt, J., & Baesens, B. (2014). Monitoring care processes in the gynecologic oncology department. Computers in Biology and Medicine, 44, 88-96. doi:10.1016/j.compbiomed.2013.10.015Bouarfa, L., & Dankelman, J. (2012). Workflow mining and outlier detection from clinical activity logs. Journal of Biomedical Informatics, 45(6), 1185-1190. doi:10.1016/j.jbi.2012.08.003Disco https://fluxicon.com/disco/Van der Aalst, W., Weijters, T., & Maruster, L. (2004). Workflow mining: discovering process models from event logs. IEEE Transactions on Knowledge and Data Engineering, 16(9), 1128-1142. doi:10.1109/tkde.2004.47Wantland, D. J., Portillo, C. J., Holzemer, W. L., Slaughter, R., & McGhee, E. M. (2004). The Effectiveness of Web-Based vs. Non-Web-Based Interventions: A Meta-Analysis of Behavioral Change Outcomes. Journal of Medical Internet Research, 6(4), e40. doi:10.2196/jmir.6.4.e40Bellazzi, R., Montani, S., Riva, A., & Stefanelli, M. (2001). Web-based telemedicine systems for home-care: technical issues and experiences. Computer Methods and Programs in Biomedicine, 64(3), 175-187. doi:10.1016/s0169-2607(00)00137-1Van der Aalst, W. (2012). Process Mining. ACM Transactions on Management Information Systems, 3(2), 1-17. doi:10.1145/2229156.2229157MySphera Company http://mysphera.com/Van der Aalst, W. M. P., & de Medeiros, A. K. A. (2005). Process Mining and Security: Detecting Anomalous Process Executions and Checking Process Conformance. Electronic Notes in Theoretical Computer Science, 121, 3-21. doi:10.1016/j.entcs.2004.10.01

    PROCESS CONFORMANCE TESTING: A METHODOLOGY TO IDENTIFY AND UNDERSTAND PROCESS VIOLATIONS IN ENACTMENT OF SOFTWARE PROCESSES

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    Today's software development is driven by software processes and practices that when followed increase the chances of building high quality software products. Not following these guidelines results in increased risk that the goal for the software's quality characteristics cannot be reached. Current process analysis approaches are limited in identifying and understanding process deviations and ultimately fail in comprehending why a process does not work in a given environment and what steps of the process have to be changed and tailored. In this work I will present a methodology for formulating, identifying and investigating process violations in the execution of software processes. The methodology, which can be thought of as "Process Conformance Testing", consists of a four step iterative model, compromising templates and tools. A strong focus is set on identifying violations in a cost efficient and unobtrusive manner by utilizing automatically collected data gathered through commonly used software development tools, such as version control systems. To evaluate the usefulness and correctness of the model a series of four studies have been conducted in both classroom and professional environments. A total of eight different software processes have been investigated and tested. The results of the studies show that the steps and iterative character of the methodology are useful for formulating and tailoring violation detection strategies and investigating violations in classroom study environments and professional environments. All the investigated processes were violated in some way, which emphasizes the importance of conformance measurement. This is especially important when running an empirical study to evaluate the effectiveness of a software process, as the experimenters want to make sure they are evaluating the specified process and not a variation of it. Violation detection strategies were tailored based upon analysis of the history of violations and feedback from then enactors and mangers yielding greater precision of identification of non-conformities. The overhead cost of the approach is shown to be feasible with a 3.4% (professional environment) and 12.1% (classroom environment) overhead. One interesting side result is that process enactors did not always follow the process for good reason, e.g. the process was not tailored for the environment, it was not specified at the right level of granularity, or was too difficult to follow. Two specific examples in this thesis are XP Pair Switching and Test Driven Development. In XP Pair Switching, the practice was violated because the frequency of switching was too high. The definition of Test Driven Development is simple and clear but requires a fair amount of discipline to follow, especially by novice programmers

    Human Research Program Space Human Factors Engineering (SHFE) Standing Review Panel (SRP)

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    The Space Human Factors Engineering (SHFE) Standing Review Panel (SRP) evaluated 22 gaps and 39 tasks in the three risk areas assigned to the SHFE Project. The area where tasks were best designed to close the gaps and the fewest gaps were left out was the Risk of Reduced Safety and Efficiency dire to Inadequate Design of Vehicle, Environment, Tools or Equipment. The areas where there were more issues with gaps and tasks, including poor or inadequate fit of tasks to gaps and missing gaps, were Risk of Errors due to Poor Task Design and Risk of Error due to Inadequate Information. One risk, the Risk of Errors due to Inappropriate Levels of Trust in Automation, should be added. If astronauts trust automation too much in areas where it should not be trusted, but rather tempered with human judgment and decision making, they will incur errors. Conversely, if they do not trust automation when it should be trusted, as in cases where it can sense aspects of the environment such as radiation levels or distances in space, they will also incur errors. This will be a larger risk when astronauts are less able to rely on human mission control experts and are out of touch, far away, and on their own. The SRP also identified 11 new gaps and five new tasks. Although the SRP had an extremely large quantity of reading material prior to and during the meeting, we still did not feel we had an overview of the activities and tasks the astronauts would be performing in exploration missions. Without a detailed task analysis and taxonomy of activities the humans would be engaged in, we felt it was impossible to know whether the gaps and tasks were really sufficient to insure human safety, performance, and comfort in the exploration missions. The SRP had difficulty evaluating many of the gaps and tasks that were not as quantitative as those related to concrete physical danger such as excessive noise and vibration. Often the research tasks for cognitive risks that accompany poor task or information design addressed only part, but not all, of the gaps they were programmed to fill. In fact the tasks outlined will not close the gap but only scratch the surface in many cases. In other cases, the gap was written too broadly, and really should be restated in a more constrained way that can be addressed by a well-organized and complementary set of tasks. In many cases, the research results should be turned into guidelines for design. However, it was not clear whether the researchers or another group would construct and deliver these guidelines

    Analysing, visualising and supporting collaborative learning using interactive tabletops

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    The key contribution of this thesis is a novel approach to design, implement and evaluate the conceptual and technological infrastructure that captures student’s activity at interactive tabletops and analyses these data through Interaction Data Analytics techniques to provide support to teachers by enhancing their awareness of student’s collaboration. To achieve the above, this thesis presents a series of carefully designed user studies to understand how to capture, analyse and distil indicators of collaborative learning. We perform this in three steps: the exploration of the feasibility of the approach, the construction of a novel solution and the execution of the conceptual proposal, both under controlled conditions and in the wild. A total of eight datasets were analysed for the studies that are described in this thesis. This work pioneered in a number of areas including the application of data mining techniques to study collaboration at the tabletop, a plug-in solution to add user-identification to a regular tabletop using a depth sensor and the first multi-tabletop classroom used to run authentic collaborative activities associated with the curricula. In summary, while the mechanisms, interfaces and studies presented in this thesis were mostly explored in the context of interactive tabletops, the findings are likely to be relevant to other forms of groupware and learning scenarios that can be implemented in real classrooms. Through the mechanisms, the studies conducted and our conceptual framework this thesis provides an important research foundation for the ways in which interactive tabletops, along with data mining and visualisation techniques, can be used to provide support to improve teacher’s understanding about student’s collaboration and learning in small groups

    Individual Behavior Modeling with Sensors Using Process Mining

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    [EN] Understanding human behavior can assist in the adoption of satisfactory health interventions and improved care. One of the main problems relies on the definition of human behaviors, as human activities depend on multiple variables and are of dynamic nature. Although smart homes have advanced in the latest years and contributed to unobtrusive human behavior tracking, artificial intelligence has not coped yet with the problem of variability and dynamism of these behaviors. Process mining is an emerging discipline capable of adapting to the nature of high-variate data and extract knowledge to define behavior patterns. In this study, we analyze data from 25 in-house residents acquired with indoor location sensors by means of process mining clustering techniques, which allows obtaining workflows of the human behavior inside the house. Data are clustered by adjusting two variables: the similarity index and the Euclidean distance between workflows. Thereafter, two main models are created: (1) a workflow view to analyze the characteristics of the discovered clusters and the information they reveal about human behavior and (2) a calendar view, in which common behaviors are rendered in the way of a calendar allowing to detect relevant patterns depending on the day of the week and the season of the year. Three representative patients who performed three different behaviors: stable, unstable, and complex behaviors according to the proposed approach are investigated. This approach provides human behavior details in the manner of a workflow model, discovering user paths, frequent transitions between rooms, and the time the user was in each room, in addition to showing the results into the calendar view increases readability and visual attraction of human behaviors, allowing to us detect patterns happening on special days.This research was funded by ITACA SABIEN and partially supported by CONICYT REDI 170136.Dogan, O.; Martinez-Millana, A.; Rojas, E.; Sepulveda, M.; Munoz Gama, J.; Traver Salcedo, V.; Fernández Llatas, C. (2019). Individual Behavior Modeling with Sensors Using Process Mining. Electronics. 8(7):1-17. https://doi.org/10.3390/electronics8070766S11787Gubbi, J., Buyya, R., Marusic, S., & Palaniswami, M. (2013). Internet of Things (IoT): A vision, architectural elements, and future directions. Future Generation Computer Systems, 29(7), 1645-1660. doi:10.1016/j.future.2013.01.010Guo, B., Zhang, D., Wang, Z., Yu, Z., & Zhou, X. (2013). Opportunistic IoT: Exploring the harmonious interaction between human and the internet of things. Journal of Network and Computer Applications, 36(6), 1531-1539. doi:10.1016/j.jnca.2012.12.028Riley, W. T., Nilsen, W. J., Manolio, T. A., Masys, D. R., & Lauer, M. (2015). News from the NIH: potential contributions of the behavioral and social sciences to the precision medicine initiative. Translational Behavioral Medicine, 5(3), 243-246. doi:10.1007/s13142-015-0320-5Xue-Wen Chen, & Xiaotong Lin. (2014). Big Data Deep Learning: Challenges and Perspectives. IEEE Access, 2, 514-525. doi:10.1109/access.2014.2325029Atzori, L., Iera, A., & Morabito, G. (2010). The Internet of Things: A survey. Computer Networks, 54(15), 2787-2805. doi:10.1016/j.comnet.2010.05.010Mamlin, B. W., & Tierney, W. M. (2016). The Promise of Information and Communication Technology in Healthcare: Extracting Value From the Chaos. The American Journal of the Medical Sciences, 351(1), 59-68. doi:10.1016/j.amjms.2015.10.015Bayo-Monton, J.-L., Martinez-Millana, A., Han, W., Fernandez-Llatas, C., Sun, Y., & Traver, V. (2018). Wearable Sensors Integrated with Internet of Things for Advancing eHealth Care. Sensors, 18(6), 1851. doi:10.3390/s18061851Larry Jameson, J., & Longo, D. L. (2015). Precision Medicine—Personalized, Problematic, and Promising. Obstetrical & Gynecological Survey, 70(10), 612-614. doi:10.1097/01.ogx.0000472121.21647.38Chaaraoui, A. A., Climent-Pérez, P., & Flórez-Revuelta, F. (2012). A review on vision techniques applied to Human Behaviour Analysis for Ambient-Assisted Living. Expert Systems with Applications, 39(12), 10873-10888. doi:10.1016/j.eswa.2012.03.005Botia, J. A., Villa, A., & Palma, J. (2012). Ambient Assisted Living system for in-home monitoring of healthy independent elders. Expert Systems with Applications, 39(9), 8136-8148. doi:10.1016/j.eswa.2012.01.153Bamis, A., Lymberopoulos, D., Teixeira, T., & Savvides, A. (2010). The BehaviorScope framework for enabling ambient assisted living. Personal and Ubiquitous Computing, 14(6), 473-487. doi:10.1007/s00779-010-0282-zDogan, O., Bayo-Monton, J.-L., Fernandez-Llatas, C., & Oztaysi, B. (2019). Analyzing of Gender Behaviors from Paths Using Process Mining: A Shopping Mall Application. Sensors, 19(3), 557. doi:10.3390/s19030557Fernández-Llatas, C., Benedi, J.-M., García-Gómez, J., & Traver, V. (2013). Process Mining for Individualized Behavior Modeling Using Wireless Tracking in Nursing Homes. Sensors, 13(11), 15434-15451. doi:10.3390/s131115434Martinez-Millana, A., Lizondo, A., Gatta, R., Vera, S., Salcedo, V., & Fernandez-Llatas, C. (2019). Process Mining Dashboard in Operating Rooms: Analysis of Staff Expectations with Analytic Hierarchy Process. International Journal of Environmental Research and Public Health, 16(2), 199. doi:10.3390/ijerph16020199Fernandez-Llatas, C., Lizondo, A., Monton, E., Benedi, J.-M., & Traver, V. (2015). Process Mining Methodology for Health Process Tracking Using Real-Time Indoor Location Systems. Sensors, 15(12), 29821-29840. doi:10.3390/s151229769Mshali, H., Lemlouma, T., Moloney, M., & Magoni, D. (2018). A survey on health monitoring systems for health smart homes. International Journal of Industrial Ergonomics, 66, 26-56. doi:10.1016/j.ergon.2018.02.002Kim, E., Helal, S., & Cook, D. (2010). Human Activity Recognition and Pattern Discovery. IEEE Pervasive Computing, 9(1), 48-53. doi:10.1109/mprv.2010.7Li, N., & Becerik-Gerber, B. (2011). Performance-based evaluation of RFID-based indoor location sensing solutions for the built environment. Advanced Engineering Informatics, 25(3), 535-546. doi:10.1016/j.aei.2011.02.004Fang, S.-H., Wang, C.-H., Huang, T.-Y., Yang, C.-H., & Chen, Y.-S. (2012). An Enhanced ZigBee Indoor Positioning System With an Ensemble Approach. IEEE Communications Letters, 16(4), 564-567. doi:10.1109/lcomm.2012.022112.120131Álvarez-García, J. A., Barsocchi, P., Chessa, S., & Salvi, D. (2013). Evaluation of localization and activity recognition systems for ambient assisted living: The experience of the 2012 EvAAL competition. Journal of Ambient Intelligence and Smart Environments, 5(1), 119-132. doi:10.3233/ais-120192Byrne, C., Collier, R., & O’Hare, G. (2018). A Review and Classification of Assisted Living Systems. Information, 9(7), 182. doi:10.3390/info9070182Manzoor, A., Truong, H.-L., Calatroni, A., Roggen, D., Bouroche, M., Clarke, S., … Dustdar, S. (2013). Analyzing the impact of different action primitives in designing high-level human activity recognition systems. Journal of Ambient Intelligence and Smart Environments, 5(5), 443-461. doi:10.3233/ais-130223Lee, S., Ha, K., & Lee, K. (2006). A pyroelectric infrared sensor-based indoor location-aware system for the smart home. IEEE Transactions on Consumer Electronics, 52(4), 1311-1317. doi:10.1109/tce.2006.273150Conca, T., Saint-Pierre, C., Herskovic, V., Sepúlveda, M., Capurro, D., Prieto, F., & Fernandez-Llatas, C. (2018). Multidisciplinary Collaboration in the Treatment of Patients With Type 2 Diabetes in Primary Care: Analysis Using Process Mining. Journal of Medical Internet Research, 20(4), e127. doi:10.2196/jmir.8884Lee, J., Bagheri, B., & Kao, H.-A. (2015). A Cyber-Physical Systems architecture for Industry 4.0-based manufacturing systems. Manufacturing Letters, 3, 18-23. doi:10.1016/j.mfglet.2014.12.00

    Lisp, Jazz, Aikido -- Three Expressions of a Single Essence

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    The relation between Science (what we can explain) and Art (what we can't) has long been acknowledged and while every science contains an artistic part, every art form also needs a bit of science. Among all scientific disciplines, programming holds a special place for two reasons. First, the artistic part is not only undeniable but also essential. Second, and much like in a purely artistic discipline, the act of programming is driven partly by the notion of aesthetics: the pleasure we have in creating beautiful things. Even though the importance of aesthetics in the act of programming is now unquestioned, more could still be written on the subject. The field called "psychology of programming" focuses on the cognitive aspects of the activity, with the goal of improving the productivity of programmers. While many scientists have emphasized their concern for aesthetics and the impact it has on their activity, few computer scientists have actually written about their thought process while programming. What makes us like or dislike such and such language or paradigm? Why do we shape our programs the way we do? By answering these questions from the angle of aesthetics, we may be able to shed some new light on the art of programming. Starting from the assumption that aesthetics is an inherently transversal dimension, it should be possible for every programmer to find the same aesthetic driving force in every creative activity they undertake, not just programming, and in doing so, get deeper insight on why and how they do things the way they do. On the other hand, because our aesthetic sensitivities are so personal, all we can really do is relate our own experiences and share it with others, in the hope that it will inspire them to do the same. My personal life has been revolving around three major creative activities, of equal importance: programming in Lisp, playing Jazz music, and practicing Aikido. But why so many of them, why so different ones, and why these specifically? By introspecting my personal aesthetic sensitivities, I eventually realized that my tastes in the scientific, artistic, and physical domains are all motivated by the same driving forces, hence unifying Lisp, Jazz, and Aikido as three expressions of a single essence, not so different after all. Lisp, Jazz, and Aikido are governed by a limited set of rules which remain simple and unobtrusive. Conforming to them is a pleasure. Because Lisp, Jazz, and Aikido are inherently introspective disciplines, they also invite you to transgress the rules in order to find your own. Breaking the rules is fun. Finally, if Lisp, Jazz, and Aikido unify so many paradigms, styles, or techniques, it is not by mere accumulation but because they live at the meta-level and let you reinvent them. Working at the meta-level is an enlightening experience. Understand your aesthetic sensitivities and you may gain considerable insight on your own psychology of programming. Mine is perhaps common to most lispers. Perhaps also common to other programming communities, but that, is for the reader to decide..

    Open meta-modelling frameworks via meta-object protocols

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    Meta-modelling is central to Model-Driven Engineering. Many meta-modelling notations, approaches and tools have been proposed along the years, which widely vary regarding their supported modelling features. However, current approaches tend to be closed and rigid with respect to the supported concepts and semantics. Moreover, extending the environment with features beyond those natively supported requires highly technical knowledge. This situation hampers flexibility and interoperability of meta-modelling environments. In order to alleviate this situation, we propose open meta-modelling frameworks, which can be extended and configured via meta-object protocols (MOPs). Such environments offer extension points on events like element instantiation, model loading or property access, and enable selecting particular model elements over which the extensions are to be executed. We show how MOP-based mechanisms permit extending meta-modelling frameworks in a flexible way, and allow describing a wide range of meta-modelling concepts. As a proof of concept, we show and compare an implementation in the MetaDepth tool and an aspect-based implementation atop the Eclipse Modelling Framework (EMF). We have evaluated our approach by extending EMF and MetaDepth with modelling services not foreseen initially when they were created. The evaluation shows that MOP-based mechanisms permit extending meta-modelling frameworks in a flexible way, and are powerful enough to support the specification of a broad variety of meta-modelling featuresWork partially funded by projects RECOM and FLEXOR (Spanish MINECO,TIN2015-73968-JIN (AEI/FEDER/UE) and TIN2014-52129-R) and the R&D programme of the Madrid Region (S2013/ICE-3006
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