824 resultados para Probabilistic decision process model
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Due to grave potential human, environmental and economical consequences of collisions at sea, collision avoidance has become an important safety concern in navigation. To reduce the risk of collisions at sea, appropriate collision avoidance actions need to be taken in accordance with the regulations, i.e., International Regulations for Preventing Collisions at Sea. However, the regulations only provide qualitative rules and guidelines, and therefore it requires navigators to decide on collision avoidance actions quantitatively by using their judgments which often leads to making errors in navigation. To better help navigators in collision avoidance, this paper develops a comprehensive collision avoidance decision making model for providing whether a collision avoidance action is required, when to take action and what action to be taken. The model is developed based on three types of collision avoidance actions, such as course change only, speed change only, and a combination of both. The model has potential to reduce the chance of making human error in navigation by assisting navigators in decision making on collision avoidance actions.
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This month, Jan Recker turns his attention to the technological side of BPM research and education. He engaged in a collaboration with two colleagues at Queensland University, Dr Marcello La Rosa and Eike Bernhard, on an initiative on the development of an advanced BPM technology - an Advanced Process Model Repository called Apromore. In this Column, they use the example of Apromore to showcase how BPM technologies are conceived, designed, developed and applied.
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In this short keynote paper, I will briefly explore the current state of research and practice surrounding the BPMN standard. On basis of this analysis I will offer a personal outlook into the key emerging areas where I believe more research will be required to further understand BPMN, its premise and promise, and how we can shape – and join together – the landscape of BPMN practice and development in academia and industry.
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This paper is concerned with the unsupervised learning of object representations by fusing visual and motor information. The problem is posed for a mobile robot that develops its representations as it incrementally gathers data. The scenario is problematic as the robot only has limited information at each time step with which it must generate and update its representations. Object representations are refined as multiple instances of sensory data are presented; however, it is uncertain whether two data instances are synonymous with the same object. This process can easily diverge from stability. The premise of the presented work is that a robot's motor information instigates successful generation of visual representations. An understanding of self-motion enables a prediction to be made before performing an action, resulting in a stronger belief of data association. The system is implemented as a data-driven partially observable semi-Markov decision process. Object representations are formed as the process's hidden states and are coordinated with motor commands through state transitions. Experiments show the prediction process is essential in enabling the unsupervised learning method to converge to a solution - improving precision and recall over using sensory data alone.
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This paper proposes a concrete approach for the automatic mitigation of risks that are detected during process enactment. Given a process model exposed to risks, e.g. a financial process exposed to the risk of approval fraud, we enact this process and as soon as the likelihood of the associated risk(s) is no longer tolerable, we generate a set of possible mitigation actions to reduce the risks' likelihood, ideally annulling the risks altogether. A mitigation action is a sequence of controlled changes applied to the running process instance, taking into account a snapshot of the process resources and data, and the current status of the system in which the process is executed. These actions are proposed as recommendations to help process administrators mitigate process-related risks as soon as they arise. The approach has been implemented in the YAWL environment and its performance evaluated. The results show that it is possible to mitigate process-related risks within a few minutes.
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The IEEE Wireless LAN standard has been a true success story by enabling convenient, efficient and low-cost access to broadband networks for both private and professional use. However, the increasing density and uncoordinated operation of wireless access points, combined with constantly growing traffic demands have started hurting the users' quality of experience. On the other hand, the emerging ubiquity of wireless access has placed it at the center of attention for network attacks, which not only raises users' concerns on security but also indirectly affects connection quality due to proactive measures against security attacks. In this work, we introduce an integrated solution to congestion avoidance and attack mitigation problems through cooperation among wireless access points. The proposed solution implements a Partially Observable Markov Decision Process (POMDP) as an intelligent distributed control system. By successfully differentiating resource hampering attacks from overload cases, the control system takes an appropriate action in each detected anomaly case without disturbing the quality of service for end users. The proposed solution is fully implemented on a small-scale testbed, on which we present our observations and demonstrate the effectiveness of the system to detect and alleviate both attack and congestion situations.
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This article proposes an approach for real-time monitoring of risks in executable business process models. The approach considers risks in all phases of the business process management lifecycle, from process design, where risks are defined on top of process models, through to process diagnosis, where risks are detected during process execution. The approach has been realized via a distributed, sensor-based architecture. At design-time, sensors are defined to specify risk conditions which when fulfilled, are a likely indicator of negative process states (faults) to eventuate. Both historical and current process execution data can be used to compose such conditions. At run-time, each sensor independently notifies a sensor manager when a risk is detected. In turn, the sensor manager interacts with the monitoring component of a business process management system to prompt the results to process administrators who may take remedial actions. The proposed architecture has been implemented on top of the YAWL system, and evaluated through performance measurements and usability tests with students. The results show that risk conditions can be computed efficiently and that the approach is perceived as useful by the participants in the tests.
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Most existing research on maintenance optimisation for multi-component systems only considers the lifetime distribution of the components. When the condition-based maintenance (CBM) strategy is adopted for multi-component systems, the strategy structure becomes complex due to the large number of component states and their combinations. Consequently, some predetermined maintenance strategy structures are often assumed before the maintenance optimisation of a multi-component system in a CBM context. Developing these predetermined strategy structure needs expert experience and the optimality of these strategies is often not proofed. This paper proposed a maintenance optimisation method that does not require any predetermined strategy structure for a two-component series system. The proposed method is developed based on the semi-Markov decision process (SMDP). A simulation study shows that the proposed method can identify the optimal maintenance strategy adaptively for different maintenance costs and parameters of degradation processes. The optimal maintenance strategy structure is also investigated in the simulation study, which provides reference for further research in maintenance optimisation of multi-component systems.
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Despite advances in the field of workflow flexibility, there is still insufficient support for dealing with unforeseen exceptions. In particular, it is challenging to find a solution which preserves the intent of the process as much as possible when such exceptions are encountered. This challenge can be alleviated by making the connection between a process and its objectives more explicit. This paper presents a demo illustrating the blended workflow approach where two specifications are fused together, a "classic" process model and a goal model. End users are guided by the process model but may deviate from this model whenever unexpected situations are encountered. The two models involved provide views on the process and the demo shows how one can switch between these views and how they are kept consistent by the blended workflow engine. A simple example involving the making of a doctor's appointment illustrates the potential advantages of the proposed approach to both researchers and developers.
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This extended abstract summarizes the state-of-the-art solution to the structuring problem for models that describe existing real world or envisioned processes. Special attention is devoted to models that allow for the true concurrency semantics. Given a model of a process, the structuring problem deals with answering the question of whether there exists another model that describes the process and is solely composed of structured patterns, such as sequence, selection, option for simultaneous execution, and iteration. Methods and techniques for structuring developed by academia as well as products and standards proposed by industry are discussed. Expectations and recommendations on the future advancements of the structuring problem are suggested.
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Two approaches are described, which aid the selection of the most appropriate procurement arrangements for a building project. The first is a multi-attribute technique based on the National Economic Development Office procurement path decision chart. A small study is described in which the utility factors involved were weighted by averaging the scores of five 'experts' for three hypothetical building projects. A concordance analysis is used to provide some evidence of any abnormal data sources. When applied to the study data, one of the experts was seen to be atypical. The second approach is by means of discriminant analysis. This was found to provide reasonably consistent predictions through three discriminant functions. The analysis also showed the quality criteria to have no significant impact on the decision process. Both approaches provided identical and intuitively correct answers in the study described. Some concluding remarks are made on the potential of discriminant analysis for future research and development in procurement selection techniques.
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Video presented as part of ACIS 2009 conference in Melbourne Australia. Movie showing the execution of a small prototype Hypbolic projection of a process model. Useful for the traversal of large process models, as the entire hierarchy can be visualised as a whole, maintaining a sense of context while moving through such complex topologies. Related ACIS Conference paper is at: http://eprints.qut.edu.au/29296/
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Video presented as part of ACIS 2009 conference in Melbourne Australia. This movie is a demonstration of the use of 3D Virtual Environments to visualise 3D BPMN Process Models, and in particular, to highlight any issues with the process model that are spatial in nature. This work is part of a paper accepted for the Asia-Pacific Conference on Conceptual Modelling (APCCM 2010) to be held in Brisbane - http://2010.apccm.org/
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Being able to innovate has become a critical capability for many contemporary organizations in an effort to sustain their operations in the long run. However, existing innovation models that attempt to guide organizations emphasize different aspects of innovation (e.g., products, services or business models), different stages of innovation (e.g., ideation, implementation or operation) or different skills (e.g., development or crowdsourcing) that are necessary to innovate, in turn creating isolated pockets of understanding about different aspects of innovation. In order to yield more predictable innovation outcomes organizations need to understand what exactly they need to focus on, what capabilities they need to have and what is necessary in order to take an idea to market. This paper aims at constructing a framework for innovation that contributes to this understanding. We will focus on a number of different stages in the innovation process and highlight different types and levels of organizational, technological, individual and process capabilities required to manage the organizational innovation process. Our work offers a comprehensive conceptualization of innovation as a multi-level process model, and provides a range of implications for further empirical and theoretical examination.