940 resultados para visitor information, network services, data collecting, data analysis, statistics, locating


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Technical report documentation page title: Bibliography of tunneling literature.

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This paper examines the impact of innovation on the performance of US business service firms. We distinguish between different levels of innovation (new-to-market and new-to-firm) in our analysis, and allow explicitly for sample selection issues. Reflecting the literature, which highlights the importance of external interaction in service innovation, we pay particular attention to the role of external innovation linkages and their effect on business performance. We find that the presence of service innovation and its extent has a consistently positive effect on growth, but no effect on productivity. There is evidence that the growth effect of innovation can be attributed, at least in part, to the external linkages maintained by innovators in the process of innovation. External linkages have an overwhelmingly positive effect on (innovator) firm performance, regardless of whether innovation is measured as a discrete or continuous variable, and regardless of the level of innovation considered.

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The local area network (LAN) interconnecting computer systems and soft- ware can make a significant contribution to the hospitality industry. The author discusses the advantages and disadvantages of such systems.

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Successful introduction of information technology applications in various operations of hotel management is vital to most service firms. In recent decades, technologies of information, automation, and communication are increasingly recognized as essential components of a hotel company’s strategic plan. In this study, 62 super-deluxe hotels (5 star), deluxe hotels (4 star), and tourist hotels (3 star) in Korea are examined for differences in the impact of information technology services on guest’ satisfaction, guest convenience, and operational efficiency. The findings generally suggest that the impacts of information technology-enhanced services vary according to the category of hotels in Korea. The results of the study are expected to assist managers in the selections and implementation of information technology systems in their hotel.

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In recent years many real time applications need to handle data streams. We consider the distributed environments in which remote data sources keep on collecting data from real world or from other data sources, and continuously push the data to a central stream processor. In these kinds of environments, significant communication is induced by the transmitting of rapid, high-volume and time-varying data streams. At the same time, the computing overhead at the central processor is also incurred. In this paper, we develop a novel filter approach, called DTFilter approach, for evaluating the windowed distinct queries in such a distributed system. DTFilter approach is based on the searching algorithm using a data structure of two height-balanced trees, and it avoids transmitting duplicate items in data streams, thus lots of network resources are saved. In addition, theoretical analysis of the time spent in performing the search, and of the amount of memory needed is provided. Extensive experiments also show that DTFilter approach owns high performance.

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Beyond the classical statistical approaches (determination of basic statistics, regression analysis, ANOVA, etc.) a new set of applications of different statistical techniques has increasingly gained relevance in the analysis, processing and interpretation of data concerning the characteristics of forest soils. This is possible to be seen in some of the recent publications in the context of Multivariate Statistics. These new methods require additional care that is not always included or refered in some approaches. In the particular case of geostatistical data applications it is necessary, besides to geo-reference all the data acquisition, to collect the samples in regular grids and in sufficient quantity so that the variograms can reflect the spatial distribution of soil properties in a representative manner. In the case of the great majority of Multivariate Statistics techniques (Principal Component Analysis, Correspondence Analysis, Cluster Analysis, etc.) despite the fact they do not require in most cases the assumption of normal distribution, they however need a proper and rigorous strategy for its utilization. In this work, some reflections about these methodologies and, in particular, about the main constraints that often occur during the information collecting process and about the various linking possibilities of these different techniques will be presented. At the end, illustrations of some particular cases of the applications of these statistical methods will also be presented.

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This thesis introduces heat demand forecasting models which are generated by using data mining algorithms. The forecast spans one full day and this forecast can be used in regulating heat consumption of buildings. For training the data mining models, two years of heat consumption data from a case building and weather measurement data from Finnish Meteorological Institute are used. The thesis utilizes Microsoft SQL Server Analysis Services data mining tools in generating the data mining models and CRISP-DM process framework to implement the research. Results show that the built models can predict heat demand at best with mean average percentage errors of 3.8% for 24-h profile and 5.9% for full day. A deployment model for integrating the generated data mining models into an existing building energy management system is also discussed.

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Microarray data analysis is one of data mining tool which is used to extract meaningful information hidden in biological data. One of the major focuses on microarray data analysis is the reconstruction of gene regulatory network that may be used to provide a broader understanding on the functioning of complex cellular systems. Since cancer is a genetic disease arising from the abnormal gene function, the identification of cancerous genes and the regulatory pathways they control will provide a better platform for understanding the tumor formation and development. The major focus of this thesis is to understand the regulation of genes responsible for the development of cancer, particularly colorectal cancer by analyzing the microarray expression data. In this thesis, four computational algorithms namely fuzzy logic algorithm, modified genetic algorithm, dynamic neural fuzzy network and Takagi Sugeno Kang-type recurrent neural fuzzy network are used to extract cancer specific gene regulatory network from plasma RNA dataset of colorectal cancer patients. Plasma RNA is highly attractive for cancer analysis since it requires a collection of small amount of blood and it can be obtained at any time in repetitive fashion allowing the analysis of disease progression and treatment response.