Surgical workflow technique
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As the workflow/BPM systems and their applications are prevailing in the wide and variety industries, we can easily predict not only that very large-scale workflow systems (VLSW) become more prevalent and much more needed in the markets, but also that the quality of workflow (QOW) and its related topics be issued in the near future. Particularly, in the QOW issues such as work flow knowledges/intelligence, workflow validations, workflow verifications, workflow mining and workflow rediscovery problems, the toughest challenging and the m ost impeccable issue is the workflow knowledge m ining and discovery problems that are based upon workflow enactment event history information logged by workflow engines equipped with a certain logging mechanism. Therefore, having an efficient event logging m echanism is the most valuable as well as A and Ω of those QOW issues and solutions. In this paper, we propose a workflow enactment event logging mechanism supporting three types of event log information ― workcase event type, activity event type and workite m event type, and descr ibe the im plementation details of the mechanism so as to be embedded into the e-Chautauqua system that has been recently developed by the CTRL research group as a very large scale workflow management system. Finally, we summarize the implications of the mechanism and its log information on workflow knowledge mining and discovery techniques.
Workflow Management Coalition
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Scientific workflow management systems are become more and more important in e-Science because they provide support for scientists in computer simulations, experiments and calculation. In the process of developing a workflow, the workflow would be modified and rerun many times. Re-executing all the preceding, unchanged steps in the workflow may be redundant and time consuming. In this paper, we propose a smart rerun strategy applies to most scientific workflow management systems. In order to gain better support for PaaS-based scientific workflow platform, we improve the smart rerun strategy by using caching technique.
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e-Science usually involves a great number of data sets, computing resources, and large teams managed and developed by research laboratories, universities, or governments. Science processes, if deployed in the workflow forms, can be managed more effectively and executed more automatically. Scientific workflows have therefore emerged and been adopted as a paradigm to organize and orchestrate activities in e-Science processes. Differing with workflows applied in the business world, however, scientific workflows need to take account of specific characteristics of science processes and make corresponding changes to accommodate those specific characteristics. A task-based scientific workflow modeling and executing approach is therefore proposed in this chapter for orchestrating e-Science with the workflow paradigm. Besides, this chapter also discusses some related work in the scientific workflow field.
e-Science
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The use of workflows to support and realize computer simulations, experiments and calculations is well-accepted in the e-Science domain. The different tasks and the parameters of the simulation are therefore specified in workflow models. Scientists typically work in a trial-and-error manner which means they do not know how the final workflow of a simulation has to look like. Therefore, they use a maybe insufficient workflow model as a basis and try to improve this model over multiple iterations to get a better approximation to the problem to solve. So in each iteration multiple trials are based on different variants of the same workflow model. Towards the goal of building variants of workflow models and enabling the reuse of existing scientific workflows in a controlled and well-defined manner, in this paper, we identify how configurable workflow models will support scientists to customize existing workflow models by their configuration. Therefore, we introduce possible configuration options for scientific workflows and how scientists can specify them. Furthermore, we show how configurable workflow models are a first step towards enabling the collaboration among scientists in creating scientific workflows.
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Workflow enacting systems are a popular technology in business and e-science alike to flexibly define and enact complex data processing tasks. Since the construction of a workflow for a specific task can become quite complex, efforts are currently underway to increase the re-use of workflows through the implementation of specialized workflow repositories. While existing methods to exploit the knowledge in these repositories usually consider workflows as an atomic entity, our work is based on the fact that workflows can naturally be viewed as graphs. Hence, in this paper we investigate the use of graph kernels for the problems of workflow discovery, workflow recommendation, and workflow pattern extraction, paying special attention on the typical situation of few labeled and many unlabeled workflows. To empirically demonstrate the feasibility of our approach we investigate a dataset of bioinformatics workflows retrieved from the website myexperiment.org. 1
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Workflow reuse is a major benefit of workflow systems and shared workflow repositories, but there are barely any studies that quantify the degree of reuse of workflows or the practical barriers that may stand in the way of successful reuse. In our own work, we hypothesize that defining workflow fragments improves reuse, since end-to-end workflows may be very specific and only partially reusable by others. This paper reports on a study of the current use of workflows and workflow fragments in labs that use the LONI Pipeline, a popular workflow system used mainly for neuroimaging research that enables users to define and reuse workflow fragments. We present an overview of the benefits of workflows and workflow fragments reported by users in informal discussions. We also report on a survey of researchers in a lab that has the LONI Pipeline installed, asking them about their experiences with reuse of workflow fragments and the actual benefits they perceive. This leads to quantifiable indicators of the reuse of workflows and workflow fragments in practice. Finally, we discuss barriers to further adoption of workflow fragments and workflow reuse that motivate further work.
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Бұл зерттеужұмысындaКaно моделітурaлы жәнеоғaн қaтыстытолықмәліметберілгенжәнеуниверситетстуденттерінебaғыттaлғaн қолдaнбaлы (кейстік)зерттеужүргізілген.АхметЯссaуи университетініңстуденттеріүшін Кaно моделіқолдaнылғaн, олaрдың жоғaры білімберусaпaсынa қоятынмaңыздытaлaптaры, яғнисaпaлық қaжеттіліктері,олaрдың мaңыздылығытурaлы жәнесaпaлық қaжеттіліктерінеқaтыстыөз университетінқaлaй бaғaлaйтындығытурaлы сұрaқтaр қойылғaн. Осы зерттеудіңмaқсaты АхметЯсaуи университетіндетуризмменеджментіжәнеқaржы бaкaлaвриaт бaғдaрлaмaлaрыныңсaпaсынa қaтыстыстуденттердіңқaжеттіліктерінaнықтaу, студенттердіңқaнaғaттaну, қaнaғaттaнбaу дәрежелерінбелгілеу,білімберусaпaсын aнықтaу мен жетілдіружолдaрын тaлдaу болыптaбылaды. Осы мaқсaтқaжетуүшін, ең aлдыменКaно сaуaлнaмaсы түзіліп,116 студенткеқолдaнылдыжәнебілімберугежәнеоның сaпaсынa қaтыстыстуденттердіңтaлaптaры мен қaжеттіліктерітоптықжұмыстaрaрқылыaнықтaлды. Екіншіден,бұл aнықтaлғaн тaлaптaр мен қaжеттіліктерКaно бaғaлaу кестесіменжіктелді.Осылaйшa, сaпa тaлaптaры төрт сaнaтқa бөлінді:болуытиіс, бір өлшемді,тaртымдыжәнебейтaрaп.Соңындa,қaнaғaттaну мен қaнaғaттaнбaудың мәндеріесептелдіжәнестуденттердіңқaнaғaттaну мен қaнaғaттaнбaу деңгейлерінжоғaрылaту мен төмендетудеосытaлaптaр мен қaжеттіліктердіңрөліaйқын aнықтaлды.Түйінсөздер:сaпa, сaпaлық қaжеттіліктер,білімберусaпaсы, Кaно моделі.
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The nationally-recognized Susquehanna
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