2 resultados para Data mining, alberi decisionali, incertezza, classificazione

em Aquatic Commons


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This report describes cases relating to the management of national marine sanctuaries in which certain scientific information was required so managers could make decisions that effectively protected trust resources. The cases presented represent only a fraction of difficult issues that marine sanctuary managers deal with daily. They include, among others, problems related to wildlife disturbance, vessel routing, marine reserve placement, watershed management, oil spill response, and habitat restoration. Scientific approaches to address these problems vary significantly, and include literature surveys, data mining, field studies (monitoring, mapping, observations, and measurement), geospatial and biogeographic analysis, and modeling. In most cases there is also an element of expert consultation and collaboration among multiple partners, agencies with resource protection responsibilities, and other users and stakeholders. The resulting management responses may involve direct intervention (e.g., for spill response or habitat restoration issues), proposal of boundary alternatives for marine sanctuaries or reserves, changes in agency policy or regulations, making recommendations to other agencies with resource protection responsibilities, proposing changes to international or domestic shipping rules, or development of new education or outreach programs. (PDF contains 37 pages.)

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As academic libraries are increasingly supported by a matrix of databases functions, the use of data mining and visualization techniques offer significant potential for future collection development and service initiatives based on quantifiable data. While data collection techniques are still not standardized and results may be skewed because of granularity problems, faulty algorithms, and a host of other factors, useful baseline data is extractable and broad trends can be identified. The purpose of the current study is to provide an initial assessment of data associated with science monograph collection at the Marston Science Library (MSL), University of Florida. These sciences fall within the major Library of Congress Classification schedules of Q, S, and T, excluding R, TN, TR, and TT. Overall strategy of this project is to look at the potential science audiences within the university community and analyze data related to purchasing and circulation patterns, e-book usage, and interlibrary loan statistics. While a longitudinal study from 2004 to the present would be ideal, this paper presents the results from the academic year July 1, 2008 to June 30, 2009 which was chosen as the pilot period because all data reservoirs identified above were available.