494,390 research outputs found

    New Zealand Building Project Cost and Its Influential Factors: A Structural Equation Modelling Approach

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    Construction industry significantly contributes to New Zealand's economic development. However, the delivery of construction projects is usually plagued by cost overruns, which turn potentially successful projects into money-losing ventures, resulting in various other unexpected negative impacts. The objectives of the study were to identify, classify, and assess the impacts of the factors affecting project cost in New Zealand. The proposed research model was examined with structural equation modelling. Recognising the lack of a systematic approach for assessing the influencing factors associated with project cost, this study identified 30 influencing factors from various sources and quantified their relative impacts. The research data were gathered through a questionnaire survey circulated across New Zealand construction industry. A total of 283 responses were received, with a 37% response rate. A model was developed for testing the relationship between project cost and the influential factors. The proposed research model was examined with structural equation modelling (SEM). According to the results of the analysis, market and industry conditions factor has the most significant effect on project cost, while regulatory regime is the second-most significant influencing factor, followed by key stakeholders' perspectives. The findings can improve project cost performance through the identification and evaluation of the cost-influencing factors. The results of such analysis enable industry professionals to better understand cost-related risks in the complex environment

    A systematic review of data quality issues in knowledge discovery tasks

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    Hay un gran crecimiento en el volumen de datos porque las organizaciones capturan permanentemente la cantidad colectiva de datos para lograr un mejor proceso de toma de decisiones. El desafío mas fundamental es la exploración de los grandes volúmenes de datos y la extracción de conocimiento útil para futuras acciones por medio de tareas para el descubrimiento del conocimiento; sin embargo, muchos datos presentan mala calidad. Presentamos una revisión sistemática de los asuntos de calidad de datos en las áreas del descubrimiento de conocimiento y un estudio de caso aplicado a la enfermedad agrícola conocida como la roya del café.Large volume of data is growing because the organizations are continuously capturing the collective amount of data for better decision-making process. The most fundamental challenge is to explore the large volumes of data and extract useful knowledge for future actions through knowledge discovery tasks, nevertheless many data has poor quality. We presented a systematic review of the data quality issues in knowledge discovery tasks and a case study applied to agricultural disease named coffee rust

    User-centered visual analysis using a hybrid reasoning architecture for intensive care units

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    One problem pertaining to Intensive Care Unit information systems is that, in some cases, a very dense display of data can result. To ensure the overview and readability of the increasing volumes of data, some special features are required (e.g., data prioritization, clustering, and selection mechanisms) with the application of analytical methods (e.g., temporal data abstraction, principal component analysis, and detection of events). This paper addresses the problem of improving the integration of the visual and analytical methods applied to medical monitoring systems. We present a knowledge- and machine learning-based approach to support the knowledge discovery process with appropriate analytical and visual methods. Its potential benefit to the development of user interfaces for intelligent monitors that can assist with the detection and explanation of new, potentially threatening medical events. The proposed hybrid reasoning architecture provides an interactive graphical user interface to adjust the parameters of the analytical methods based on the users' task at hand. The action sequences performed on the graphical user interface by the user are consolidated in a dynamic knowledge base with specific hybrid reasoning that integrates symbolic and connectionist approaches. These sequences of expert knowledge acquisition can be very efficient for making easier knowledge emergence during a similar experience and positively impact the monitoring of critical situations. The provided graphical user interface incorporating a user-centered visual analysis is exploited to facilitate the natural and effective representation of clinical information for patient care

    Information Systems Skills Differences between High-Wage and Low-Wage Regions: Implications for Global Sourcing

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    Developing Information Systems (IS) skills for a company’s workforce has always been challenging, but global sourcing growth has caused the determination of needed IS skills to be more complex. The increased use of outsourcing to an IS service provider and from high-wage regions to low-wage regions has affected what IS skills are required globally and how to distribute the workforce to meet these needs. To understand what skills are needed in locations that seek and those that provide outsourcing, we surveyed IS service provider managers in global locations. Results from 126 reporting units provide empirical evidence that provider units in low-wage regions value technical skills more than those in high-wage regions. Despite the emphasis on commodity skills in low-wage areas, high- and low-wage providers value project management skills. Low-wage regions note global and virtual teamwork more than high-wage regions do. The mix of skills and the variation by region have implications for domestic and offshore sourcing. Service providers can vary their staffing models in global regions which has consequences for recruiting, corporate training, and curriculum
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