Thematische Bibliographien / Cluster monitoring

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Auswahl der wissenschaftlichen Literatur zum Thema „Cluster monitoring“

Autor: Grafiati

Veröffentlicht am 28. Juni 2021

Zuletzt aktualisiert am 1. Februar 2022

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Zeitschriftenartikel zum Thema "Cluster monitoring"

Getmanets, O., A. Nekos und M. Pelikhatyi. „CLUSTER ANALYSIS AND RADIATION MONITORING OF ENVIRONMENT“. Visnyk of Taras Shevchenko National University of Kyiv. Geology, Nr. 3 (86) (2019): 75–79. http://dx.doi.org/10.17721/1728-2713.86.11.

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Building a background radiation field on the ground on the basis of measurement data taken at a finite number of points is one of the most important tasks of radiation monitoring. The aim of the work: to study the possibility of applying cluster analysis for the tasks of radiation monitoring of the environment. Cluster analysis is a multidimensional statistical analysis. Its main purpose is to split the set of objects under study (observation points) into homogeneous groups or clusters, that is, the task of classifying data and identifying the corresponding structure in them is solved. Methods of research: the measurements of the power of the ambient dose of continuous X-ray and gamma radiation on the terrain by using the MKS-05 dosimeter "TERRA-0"; processing of the obtained data by cluster analysis methods using the computer program "Statistics-10", wherein each cluster point is characterized by three coordinates: two coordinates on the ground and the power of the ambient dose of radiation at a given point; Euclidean distance was chosen as the distance between two points. Results: after processing data using various clustering methods: the method of Complete Linkage, the method of Weighted pair-group average and the Ward's method, it was found that the results of the analysis practically coincide with each other, that proves the reliability of the application of cluster analysis for the tasks of radiation monitoring of the environment and mapping of radiation pollution. Conclusions: the concept of a "radiation cluster" was first formulated in this work, combining coordinates on a plane with an ambient dose rate;the possibility of using cluster analysis to construct a map of radiation pollution of the environment has been proved by sequential projectionfrom more connected to less connected radiation clusters onto the plane of the controlled zone. In this sense, cluster analysis is similar to the operator approach to the construction of the radiation field. For further research, it is of some interest to study the issues of integration of cluster analysis with geographic information systems.

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Crooks, David, Mark Mitchell, Stuart Purdie, Gareth Roy, Samuel Cadellin Skipsey und David Britton. „Monitoring in a grid cluster“. Journal of Physics: Conference Series 513, Nr. 6 (11.06.2014): 062010. http://dx.doi.org/10.1088/1742-6596/513/6/062010.

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Nazarov, Alexey N. „Processing streams in a monitoring cloud cluster“. Russian Technological Journal 7, Nr. 6 (10.01.2020): 56–67. http://dx.doi.org/10.32362/2500-316x-2019-7-6-56-67.

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The creation of monitoring clusters based on cloud computing technologies is a promising direction for the development of systems for continuous monitoring of objects for various purposes in the web space. Hadoop web-programming environment is the technological basis for the development of algorithmic and software solutions for the synthesis of monitoring clusters, including information security and information counteraction systems. The International Telecommunication Union’ (ITU) recommendations Y. 3510 present the requirements for cloud infrastructure that require monitoring the performance of deployed applications based on the collection of real-world statistics. Often, computing resources of monitoring clusters of cloud data centers are allocated for continuous parallel processing of high-speed streaming data, which imposes new requirements to monitoring technologies, necessitating the creation and research of new models of parallel computing. The need to use service monitoring plays an important role in the cloud computing industry, especially for SLA/QoS assessment, as the application or service may experience problems even if the virtual machines on which the work is taking place appear to be operational. This requires to study the methodological possibilities of organization to study of parallel processing high-speed streaming services with the processing of huge amounts of bit data, and, simultaneously, to estimate the necessary computational resource. In the conditions of high dynamics of changes in the bit rate of information generation from the source, a model of the bit rate of Discretized Stream (DStream) formation is proposed, which has a common application. Based on the poly-burst nature of the bit rate model, a model of group content traffic of any sources of different services processed in the cloud cluster was created. The obtained results made it possible to develop mathematical models of parallel DStreams from sources processed in a cloud cluster via Hadoop technology using the micro-batch architecture of the Spark Streaming module. These models take into account the flow of requests for maintenance from sources of different services, on the one hand, and, on the other hand, the needs of services in bit rate, taking into account the multichannel traffic of sources of various services. At the same time, analytical relations are obtained to calculate the required performance of the Hadoop cluster at a given value of the probability of batch loss.

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Terry, L. Irene, und Gloria DeGrandi-Hoffman. „MONITORING WESTERN FLOWER THRIPS (THYSANOPTERA: THRIPIDAE) IN “GRANNY SMITH” APPLE BLOSSOM CLUSTERS“. Canadian Entomologist 120, Nr. 11 (November 1988): 1003–16. http://dx.doi.org/10.4039/ent1201003-11.

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AbstractThe efficiency and accuracy of sampling western flower thrips (Frankliniella occidentalis [Pergande]) from “Granny Smith” apple blossom clusters were analyzed during 1986–1987 to develop a sampling plan for research purposes. The accuracy of the “shake” method was compared with an “extraction” process of each of three blossom cluster types: pink, open, and petalless (petal fall). Thrip extractions from combined clusters revealed that a 9-s and 6-s “shake” removed 84 and 74%, of the thrips, respectively, but a 3-s “shake” removed 53%, and was more variable. Open blossom clusters always had higher thrips densities than either pink or petal fall clusters, regardless of the bloom state. The effects of cardinal position within trees were not consistent over time. Clusters from the top of the canopy had more thrips than lower canopy clusters, and apical clusters had more thrips than basal clusters during peak bloom. Variance component analyses indicated that thrips counts from clusters within tree were more variable than counts among trees, even when cluster types were analyzed separately. Two sets of indices (Iwao’s regression of mean crowding on mean density and Taylor’s regression of log variance on log mean density) for each cluster type indicated aggregated spatial patterns. Precision level sampling plans were developed using Iwao’s regression coefficients.

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Borisov, Vadim, Maksim Dli, Artem Vasiliev, Yaroslav Fedulov, Elena Kirillova und Nikolay Kulyasov. „Energy System Monitoring Based on Fuzzy Cognitive Modeling and Dynamic Clustering“. Energies 14, Nr. 18 (15.09.2021): 5848. http://dx.doi.org/10.3390/en14185848.

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A feature of energy systems (ESs) is the diversity of objects, as well as the variety and manifold of the interconnections between them. A method for monitoring ESs clusters is proposed based on the combined use of a fuzzy cognitive approach and dynamic clustering. A fuzzy cognitive approach allows one to represent the interdependencies between ESs objects in the form of fuzzy impact relations, the analysis results of which are used to substantiate indicators for fuzzy clustering of ESs objects and to analyze the stability of clusters and ESs. Dynamic clustering methods are used to monitor the cluster structure of ESs, namely, to assess the drift of cluster centers, to determine the disappearance or emergence of new clusters, and to unite or separate clusters of ESs.

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Marino, Stefano, und Arturo Alvino. „Vegetation Indices Data Clustering for Dynamic Monitoring and Classification of Wheat Yield Crop Traits“. Remote Sensing 13, Nr. 4 (03.02.2021): 541. http://dx.doi.org/10.3390/rs13040541.

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Monitoring the spatial and temporal variability of yield crop traits using remote sensing techniques is the basis for the correct adoption of precision farming. Vegetation index images are mainly associated with yield and yield-related physiological traits, although quick and sound strategies for the classification of the areas with plants with homogeneous agronomic crop traits are still to be explored. A classification technique based on remote sensing spectral information analysis was performed to discriminate between wheat cultivars. The study analyzes the ability of the cluster method applied to the data of three vegetation indices (VIs) collected by high-resolution UAV at three different crop stages (seedling, tillering, and flowering), to detect the yield and yield component dynamics of seven durum wheat cultivars. Ground truth data were grouped according to the identified clusters for VI cluster validation. The yield crop variability recorded in the field at harvest showed values ranging from 2.55 to 7.90 t. The ability of the VI clusters to identify areas with similar agronomic characteristics for the parameters collected and analyzed a posteriori revealed an already important ability to detect areas with different yield potential at seedling (5.88 t ha−1 for the first cluster, 4.22 t ha−1 for the fourth). At tillering, an enormous difficulty in differentiating the less productive areas in particular was recorded (5.66 t ha−1 for cluster 1 and 4.74, 4.31, and 4.66 t ha−1 for clusters 2, 3, and 4, respectively). An excellent ability to group areas with the same yield production at flowering was recorded for the cluster 1 (6.44 t ha−1), followed by cluster 2 (5.6 t ha−1), cluster 3 (4.31 t ha−1), and cluster 4 (3.85 t ha−1). Agronomic crop traits, cultivars, and environmental variability were analyzed. The multiple uses of VIs have improved the sensitivity of k-means clustering for a new image segmentation strategy. The cluster method can be considered an effective and simple tool for the dynamic monitoring and assessment of agronomic traits in open field wheat crops.

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ВЫЖИТОВИЧ, Александр, Aleksandr VYZHITOVICH, Олег ЛЯМЗИН und Oleg LYAMZIN. „MODELING THE MONITORING SERVICE FUNCTION IN A MEMBER ORGANIZATION OF A CLUSTER STRUCTURE“. Services in Russia and abroad 11, Nr. 4 (04.07.2017): 44–54. http://dx.doi.org/10.22412/1995-042x-11-4-4.

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The article is devoted to the issues of improving the enterprise monitoring services in the context of its inclusion in the cluster structure. One of the most promising areas for increasing the activity of territories is the functioning of clusters in various sectors of the economy. Against the backdrop of the activity on their creation and state support, the issue of the significance of the control service regarding the work of enterprises as participants in clusters, in the economic literature is insufficiently disclosed. The problem of organizing an adequate internal control at the enterprise as part of the overall problem of the forming its effective appraisal and analytical system remains largely unresolved. The article is focused on the organization activity when forming integration cluster interactions, namely approaches and tools for modeling the function of the internal control service of an organization when joining a cluster. The purpose of the research is to test the possibility of modeling the process of qualitative expertise in the implementation of the cluster initiative and develop, on this basis, proposals for the organization of internal control of an enterprise as a participant in the cluster. This is supposed to be done through the business game "Internal Audit in a Cluster" based on the assignment of a conditional Board of Directors, where the task of forming an expert opinion on the need to implement measures for an enterprise as a member of a cluster should be decided for the internal audit unit. The authors offer an approach for monitoring the state of internal control based on the game method "traffic light". Game experiment allows to improve the expertise quality of proposals for participation in the cluster and to form the directions of work in the internal control system. The application of a business game with the participation of professional practitioners makes it possible to test a new management technology for identifying and assessing enterprise risks in cluster projects, to improve the quality of control as a service function for enterprise management bodies. The research results can be used by the participants of clusters created and planned to be created for building and improving their internal control system, conducting strategic sessions, developing cluster’s internal documents.

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Markov, L. S., V. B. Kurmashev und A. F. Buruk. „Peculiarities of Regional Cluster Policy Monitoring“. World of Economics and Management 18, Nr. 3 (2018): 91–103. http://dx.doi.org/10.25205/2542-0429-2018-18-3-91-103.

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Fang, Kun, Chengyin Liu und Jun Teng. „Cluster-based optimal wireless sensor deployment for structural health monitoring“. Structural Health Monitoring 17, Nr. 2 (03.02.2017): 266–78. http://dx.doi.org/10.1177/1475921717689967.

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A well-designed wireless sensor deployment method not only directly influences the number of deployed sensors and data accuracy, but also influences on network topology. As most of the energy cost comes from the transmission and receiving of data packets, clustering optimization in wireless sensor network becomes an important issue for energy-efficient coordination among the densely deployed nodes for data communication. In a typical hierarchical wireless sensor network, total intra-cluster communication distance and total distance of cluster heads to base station depend on number of cluster heads. This work presents a novel approach by selecting the number of clusters in hierarchical wireless sensor network. We analyze and demonstrate the validity of the cluster optimization for wireless sensor deployment using an example of a numerically simulated simply supported truss, in terms of efficient use of the constrained wireless sensor network resources. Followed by a cluster-based optimization framework, we show how to adopt our approach to achieve scalable and efficient deployment, through a comprehensive optimization study of a realistic wireless structural health monitoring system. Finally, we suggest optimal deployment scheme based on the comparative performance evaluation results in the case study.

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Trachenko, M. B., und O. D. Gaisha. „Evaluation of the Effectiveness of Financing Industrial Clusters“. Russian Economic Journal, Nr. 5 (November 2019): 36–47. http://dx.doi.org/10.33983/0130-9757-2019-5-36-47.

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The article is solving an actual problem — development of a system of indicators to evaluate the effectiveness of financing industrial clusters in Russia. The article analyzes the cluster models of Russian and foreign authors, identifies their strengths and weaknesses. A universal information model of the cluster was developed, reflecting the interaction of the participants among themselves and with external stakeholders of the cluster development. The developed model has three control loops: internal cluster stakeholders, cluster, cluster's region. Each has the specificity of the movement of inventory and cash flows, information interaction in the implementation of cluster policy, and reflects the interests of various stakeholders of industrial clusters. The model lays the groundwork to justify a three-tier system of indicators to evaluate the effectiveness of financing industrial clusters. The subsystems of the indicators of the impact of the industrial cluster on the regional economy, of the indicators of the industrial cluster development and the subsystem of the indicators of the financial condition of enterprises participating in the industrial cluster are highlighted in the proposed system. The study used the methods of bibliographic and logical analysis, synthesis and systems approach, mathematical methods of statistical data processing. The developed system of indicators for assessing the effectiveness of financing industrial clusters can be used to conduct current and subsequent monitoring of financing the implementation of cluster programs, to prepare decisions on the allocation of budgetary funds by state and municipal authorities, and to potential investors to determine the most promising investment instruments.

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Dissertationen zum Thema "Cluster monitoring"

Worm, Stefan. „Monitoring of large-scale Cluster Computers“. Master's thesis, Universitätsbibliothek Chemnitz, 2007. http://nbn-resolving.de/urn:nbn:de:swb:ch1-200700032.

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The constant monitoring of a computer is one of the essentials to be up-to-date about its state. This may seem trivial if one is sitting right in front of it but when monitoring a computer from a certain distance it is not as simple anymore. It gets even more difficult if a large number of computers need to be monitored. Because the process of monitoring always causes some load on the network and the monitored computer itself, it is important to keep these influences as low as possible. Especially for a high-performance cluster that was built from a lot of computers, it is necessary that the monitoring approach works as efficiently as possible and does not influence the actual operations of the supercomputer. Thus, the main goals of this work were, first of all, analyses to ensure the scalability of the monitoring solution for a large computer cluster as well as to prove the functionality of it in practise. To achieve this, a classification of monitoring activities in terms of the overall operation of a large computer system was accomplished first. Thereafter, methods and solutions were presented which are suitable for a general scenario to execute the process of monitoring as efficient and scalable as possible. During the course of this work, conclusions from the operation of an existing cluster for the operation of a new, more powerful system were drawn to ensure its functionality as good as possible. Consequently, a selection of applications from an existing pool of solutions was made to find one that is most suitable for the monitoring of the new cluster. The selection took place considering the special situation of the system like the usage of InfiniBand as the network interconnect. Further on, an additional software was developed which can read and process the different status information of the InfiniBand ports, unaffected by the vendor of the hardware. This functionality, which so far had not been available in free monitoring applications, was exemplarily realised for the chosen monitoring software. Finally, the influence of monitoring activities on the actual tasks of the cluster was of interest. To examine the influence on the CPU and the network, the self-developed plugin as well as a selection of typical monitoring values were used exemplarily. It could be proven that no impact on the productive application for typical monitoring intervals can be expected and only for atypically short intervals a minor influence could be determined
Die ständige Überwachung eines Computers gehört zu den essentiellen Dingen, die zu tun sind um immer auf dem Laufenden zu sein, wie der aktuelle Zustand des Rechners ist. Dies ist trivial, wenn man direkt davor sitzt, aber wenn man einen Computer aus der Ferne beobachten soll ist dies schon nicht mehr so einfach möglich. Schwieriger wird es dann, wenn es eine große Anzahl an Rechnern zu überwachen gilt. Da der Vorgang der Überwachung auch immer etwas Netzwerklast und Last auf dem zu überwachenden Rechner selber verursacht, ist es wichtig diese Einflüsse so gering wie möglich zu halten. Gerade dann, wenn man viele Computer zu einem leistungsfähigen Cluster zusammen geschalten hat ist es notwendig, dass diese Überwachungslösung möglichst effizient funktioniert und die eigentliche Arbeit des Supercomputers nicht stört. Die Hauptziele dieser Arbeit sind deshalb Analysen zur Sicherstellung der Skalierbarkeit der Überwachungslösung für einen großen Computer Cluster, sowie der praktische Nachweis der Funktionalität dieser. Dazu wurde zuerst eine Einordnung des Monitorings in den Gesamtbetrieb eines großen Computersystems vorgenommen. Danach wurden Methoden und Lösungen aufgezeigt, welche in einem allgemeinen Szenario geeignet sind, um den ganzheitlichen Vorgang der Überwachung möglichst effizient und skalierbar durchzuführen. Im weiteren Verlauf wurde darauf eingegangen welche Lehren aus dem Betrieb eines vorhandenen Clusters für den Betrieb eines neuen, leistungsfähigeren Systems gezogen werden können um dessen Funktion möglichst gut gewährleisten zu können. Darauf aufbauend wurde eine Auswahl getroffen, welche Anwendung aus einer Menge existierende Lösungen heraus, zur Überwachung des neuen Clusters besonders geeignet ist. Dies fand unter Berücksichtigung der spezielle Situation, zum Beispiel der Verwendung von InfiniBand als Verbindungsnetzwerk, statt. Im Zuge dessen wurde eine zusätzliche Software entwickelt, welche die verschiedensten Statusinformationen der InfiniBand Anschlüsse auslesen und verarbeiten kann, unabhängig vom Hersteller der Hardware. Diese Funktionalität, welche im Bereich der freien Überwachungsanwendungen bisher ansonsten noch nicht verfügbar war, wurde beispielhaft für die gewählte Monitoring Software umgesetzt. Letztlich war der Einfluss der Überwachungsaktivitäten auf die eigentlichen Anwendungen des Clusters von Interesse. Dazu wurden exemplarisch das selbst entwickelte Plugin sowie eine Auswahl an typischen Überwachungswerten benutzt, um den Einfluss auf die CPU und das Netzwerk zu untersuchen. Dabei wurde gezeigt, dass für typische Überwachungsintervalle keine Einschränkungen der eigentlichen Anwendung zu erwarten sind und dass überhaupt nur für untypisch kurze Intervalle ein geringer Einfluss festzustellen war

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Worm, Stefan Mehlan Torsten. „Monitoring of large-scale Cluster Computers“. [S.l. : s.n.], 2007.

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Bucciarelli, Mark. „Cluster sampling methods for monitoring route-level transit ridership“. Thesis, Massachusetts Institute of Technology, 1991. http://hdl.handle.net/1721.1/13485.

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Bank, Mathias. „AIM - A Social Media Monitoring System for Quality Engineering“. Doctoral thesis, Universitätsbibliothek Leipzig, 2013. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-115894.

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In the last few years the World Wide Web has dramatically changed the way people are communicating with each other. The growing availability of Social Media Systems like Internet fora, weblogs and social networks ensure that the Internet is today, what it was originally designed for: A technical platform in which all users are able to interact with each other. Nowadays, there are billions of user comments available discussing all aspects of life and the data source is still growing. This thesis investigates, whether it is possible to use this growing amount of freely provided user comments to extract quality related information. The concept is based on the observation that customers are not only posting marketing relevant information. They also publish product oriented content including positive and negative experiences. It is assumed that this information represents a valuable data source for quality analyses: The original voices of the customers promise to specify a more exact and more concrete definition of \"quality\" than the one that is available to manufacturers or market researchers today. However, the huge amount of unstructured user comments makes their evaluation very complex. It is impossible for an analysis protagonist to manually investigate the provided customer feedback. Therefore, Social Media specific algorithms have to be developed to collect, pre-process and finally analyze the data. This has been done by the Social Media monitoring system AIM (Automotive Internet Mining) that is the subject of this thesis. It investigates how manufacturers, products, product features and related opinions are discussed in order to estimate the overall product quality from the customers\\\' point of view. AIM is able to track different types of data sources using a flexible multi-agent based crawler architecture. In contrast to classical web crawlers, the multi-agent based crawler supports individual crawling policies to minimize the download of irrelevant web pages. In addition, an unsupervised wrapper induction algorithm is introduced to automatically generate content extraction parameters which are specific for the crawled Social Media systems. The extracted user comments are analyzed by different content analysis algorithms to gain a deeper insight into the discussed topics and opinions. Hereby, three different topic types are supported depending on the analysis needs. * The creation of highly reliable analysis results is realized by using a special context-aware taxonomy-based classification system. * Fast ad-hoc analyses are applied on top of classical fulltext search capabilities. * Finally, AIM supports the detection of blind-spots by using a new fuzzified hierarchical clustering algorithm. It generates topical clusters while supporting multiple topics within each user comment. All three topic types are treated in a unified way to enable an analysis protagonist to apply all methods simultaneously and in exchange. The systematically processed user comments are visualized within an easy and flexible interactive analysis frontend. Special abstraction techniques support the investigation of thousands of user comments with minimal time efforts. Hereby, specifically created indices show the relevancy and customer satisfaction of a given topic
In den letzten Jahren hat sich das World Wide Web dramatisch verändert. War es vor einigen Jahren noch primär eine Informationsquelle, in der ein kleiner Anteil der Nutzer Inhalte veröffentlichen konnte, so hat sich daraus eine Kommunikationsplattform entwickelt, in der jeder Nutzer aktiv teilnehmen kann. Die dadurch enstehende Datenmenge behandelt jeden Aspekt des täglichen Lebens. So auch Qualitätsthemen. Die Analyse der Daten verspricht Qualitätssicherungsmaßnahmen deutlich zu verbessern. Es können dadurch Themen behandelt werden, die mit klassischen Sensoren schwer zu messen sind. Die systematische und reproduzierbare Analyse von benutzergenerierten Daten erfordert jedoch die Anpassung bestehender Tools sowie die Entwicklung neuer Social-Media spezifischer Algorithmen. Diese Arbeit schafft hierfür ein völlig neues Social Media Monitoring-System, mit dessen Hilfe ein Analyst tausende Benutzerbeiträge mit minimaler Zeitanforderung analysieren kann. Die Anwendung des Systems hat einige Vorteile aufgezeigt, die es ermöglichen, die kundengetriebene Definition von \"Qualität\" zu erkennen

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Neema, Isak. „Surveying and monitoring crimes in Namibia through the likrlihood based cluster analysis“. Thesis, University of Reading, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.518226.

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Živčák, Adam. „Správa Raspberry Pi 4 clusteru pomocí Nix“. Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2021. http://www.nusl.cz/ntk/nusl-445519.

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The scope of this thesis is to design and implement a system for deploying, managing and monitoring a Raspberry Pi cluster using Nix technologies. The thesis describes the benefits of the functional approach of Nix and the subsystems that are based on it. The thesis also results in a supporting web application, providing an intuitive environment for working with cluster configuration deployments and clearly displaying information about the utilization of individual nodes using dashboards. The final part of the thesis is devoted to testing cluster performance using sample distributed computing jobs.

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Chen, Yajuan. „Cluster_Based Profile Monitoring in Phase I Analysis“. Diss., Virginia Tech, 2014. http://hdl.handle.net/10919/46810.

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Profile monitoring is a well-known approach used in statistical process control where the quality of the product or process is characterized by a profile or a relationship between a response variable and one or more explanatory variables. Profile monitoring is conducted over two phases, labeled as Phase I and Phase II. In Phase I profile monitoring, regression methods are used to model each profile and to detect the possible presence of out-of-control profiles in the historical data set (HDS). The out-of-control profiles can be detected by using the statis-tic. However, previous methods of calculating the statistic are based on using all the data in the HDS including the data from the out-of-control process. Consequently, the ability of using this method can be distorted if the HDS contains data from the out-of-control process. This work provides a new profile monitoring methodology for Phase I analysis. The proposed method, referred to as the cluster-based profile monitoring method, incorporates a cluster analysis phase before calculating the statistic. Before introducing our proposed cluster-based method in profile monitoring, this cluster-based method is demonstrated to work efficiently in robust regression, referred to as cluster-based bounded influence regression or CBI. It will be demonstrated that the CBI method provides a robust, efficient and high breakdown regression parameter estimator. The CBI method first represents the data space via a special set of points, referred to as anchor points. Then a collection of single-point-added ordinary least squares regression estimators forms the basis of a metric used in defining the similarity between any two observations. Cluster analysis then yields a main cluster containing at least half the observations, with the remaining observations comprising one or more minor clusters. An initial regression estimator arises from the main cluster, with a group-additive DFFITS argument used to carefully activate the minor clusters through a bounded influence regression frame work. CBI achieves a 50% breakdown point, is regression equivariant, scale and affine equivariant and distributionally is asymptotically normal. Case studies and Monte Carlo results demonstrate the performance advantage of CBI over other popular robust regression procedures regarding coefficient stabil-ity, scale estimation and standard errors. The cluster-based method in Phase I profile monitoring first replaces the data from each sampled unit with an estimated profile, using some appropriate regression method. The estimated parameters for the parametric profiles are obtained from parametric models while the estimated parameters for the nonparametric profiles are obtained from the p-spline model. The cluster phase clusters the profiles based on their estimated parameters and this yields an initial main cluster which contains at least half the profiles. The initial estimated parameters for the population average (PA) profile are obtained by fitting a mixed model (parametric or nonparametric) to those profiles in the main cluster. Profiles that are not contained in the initial main cluster are iteratively added to the main cluster provided their statistics are "small" and the mixed model (parametric or nonparametric) is used to update the estimated parameters for the PA profile. Those profiles contained in the final main cluster are considered as resulting from the in-control process while those not included are considered as resulting from an out-of-control process. This cluster-based method has been applied to monitor both parametric and nonparametric profiles. A simulated example, a Monte Carlo study and an application to a real data set demonstrates the detail of the algorithm and the performance advantage of this proposed method over a non-cluster-based method is demonstrated with respect to more accurate estimates of the PA parameters and improved classification performance criteria. When the profiles can be represented by vectors, the profile monitoring process is equivalent to the detection of multivariate outliers. For this reason, we also compared our proposed method to a popular method used to identify outliers when dealing with a multivariate response. Our study demonstrated that when the out-of-control process corresponds to a sustained shift, the cluster-based method using the successive difference estimator is clearly the superior method, among those methods we considered, based on all performance criteria. In addition, the influence of accurate Phase I estimates on the performance of Phase II control charts is presented to show the further advantage of the proposed method. A simple example and Monte Carlo results show that more accurate estimates from Phase I would provide more efficient Phase II control charts.
Ph. D.

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Chan, Sik-foon Joyce. „Application of cluster analysis to identify sources of particulate matter in Hong Kong /“. Hong Kong : University of Hong Kong, 1995. http://sunzi.lib.hku.hk/hkuto/record.jsp?B1470920X.

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Tedder, O. W. S. „Monitoring the spin environment of coupled quantum dots : towards the deterministic generation of photonic cluster states“. Thesis, University College London (University of London), 2018. http://discovery.ucl.ac.uk/10049622/.

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Indium gallium arsenide self-assembled quantum dots have attracted a lot of attention due to their ability to trap single electrons and holes whose spin can be manipulated optically. This makes them attractive as qubits and light sources in various quantum computing and communication schemes. However, the spin of electrons and holes rapidly decoheres due to hyper-fine interaction with the atomic nuclei of the dot. The theme of this thesis is to find ways of overcoming this decoherence, in particular to allow generation of photonic cluster states from quantum dots. This was first approached by designing theoretical schemes to measure and compensate for the source of the decoherence, which were experimentally tested. Two new systems were then theoretically designed where the effects of decoherence could be mitigated. It is shown theoretically that exciting a quantum dot with a laser of well-defined polarisation and monitoring the polarisation of emitted photons, it is possible to determine the vector polarisation of the nuclear spin ensemble. It is shown through simulation that this measurement can be performed on and possibly faster than the time-scale of nuclear fluctuations. The fundamental concept behind the measurement procedure is proved in an experiment using coupled quantum dots. Through the course of the experiment anomalous behaviour of the dots was discovered. A second theoretical proposal is made for a system allowing the fast application of an effective field to compensate for the decoherence mechanism. It is then shown by simulation that a coupled dot system with a prepared in-plane nuclear spin polarisation, can allow optical spin rotation and entanglement generation. A different system is then theoretically proposed where the electron spin in quantum dot can be replaced with another qubit, such as embedded manganese atoms. It is shown through simulation that this system also allows the generation of photonic cluster states.

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Yang, Weishuai. „Scalable and effective clustering, scheduling and monitoring of self-organizing grids“. Diss., Online access via UMI:, 2008.

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Bücher zum Thema "Cluster monitoring"

Statistics, Guyana Bureau of. Guyana multiple indicator cluster survey, 2006: Monitoring the situation of children and women. Georgetown: Bureau of Statistics, 2008.

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Thailand. Samnakngān Sathiti hǣng Chāt. Thailand: Multiple indicator cluster survey 2012 : monitoring the situation of children and women. [Bangkok, Thailand]: NSO, National Statistical Office, 2013.

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United Nations Children's Fund. (UNICEF). Monitoring the situation of children and women: Vanuatu multiple indicator cluster survey 2007. Port Vila, Vanuatu: Ministry of Health, 2008.

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Montiel, Cristina. Monitoring Congress: A cluster analysis of legislative voting patterns during the Aquino administration, 1987-89. Quezon City, Philippines: Center for Social Policy and Public Affairs, Ateneo de Manila University, 1990.

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Statistics, Guyana Bureau of. Guyana multiple indicator cluster survey, summary report 2006: Monitoring the situation of children and women. Georgetown: Bureau of Statistics, 2008.

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UNICEF. Multiple indicator cluster survey in the Republic of Kazakhstan, 2010-2011: Monitoring the situation of children and women. Astana: Agency of Statistics Republic of Kazkahstan, 2012.

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Burma. Department of Health Planning. Monitoring progress toward the goals of national programme of action for Myanmar's children through multiple indicator cluster survey, 1997. Yangon]: Dept. of Health Planning, Ministry of Health with collaboration of UNICEF, 1999.

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Kochetova, Zhanna, Natal'ya Maslova und Oleg Bazarskiy. Aviation and missile clusters and the environment. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1544137.

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The monograph introduces a new concept - the aviation and missile cluster as a new class of objects of geo-ecological monitoring, united by the solution of identical strategic tasks of the state, the interconnection of its structural elements, the identity of priority contaminants and products of their transformation. The scientific and methodological apparatus of complex geoecological monitoring of territories under the influence of objects of aviation and space activities is presented, including predictive models of the spread and transformation of priority contaminants in environmental objects, taking into account their physical and chemical properties, geographical and climatic features of the studied territory; algorithms and methods for assessing the environmental situation in the area of the aviation and rocket cluster to support management decisions on conducting rehabilitation and preventive medical and environmental measures. The proposed scientific and methodological apparatus improves the quality of the assessment of the geoecological situation while reducing the cost of monitoring the territory of the aviation and missile cluster. The scientific results obtained by the authors based on the results of eleven-year geoecological monitoring of a typical aviation and rocket cluster located within the city of Voronezh and including an airfield of state aviation and a test complex of launch vehicles are presented. For a wide range of readers interested in environmental problems of scientific and technological progress.

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Service, Ghana Statistical, Hrsg. Monitoring the situation of children, women, and men: Multiple indicator cluster survey, 2006. [Accra: Ghana Statistical Service, 2006.

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Monitoring the situation of children, women, and men: Multiple indicator cluster survey, 2006. [Accra: Ghana Statistical Service, 2006.

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Buchteile zum Thema "Cluster monitoring"

Krogh, Jesper Wisborg, und Mikiya Okuno. „Monitoring MySQL NDB Cluster“. In Pro MySQL NDB Cluster, 519–50. Berkeley, CA: Apress, 2017. http://dx.doi.org/10.1007/978-1-4842-2982-8_16.

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Krogh, Jesper Wisborg, und Mikiya Okuno. „Sources for Monitoring Data“. In Pro MySQL NDB Cluster, 457–517. Berkeley, CA: Apress, 2017. http://dx.doi.org/10.1007/978-1-4842-2982-8_15.

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Krogh, Jesper Wisborg, und Mikiya Okuno. „Monitoring Solutions and the Operating System“. In Pro MySQL NDB Cluster, 441–55. Berkeley, CA: Apress, 2017. http://dx.doi.org/10.1007/978-1-4842-2982-8_14.

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Focht, Erich, und Andreas Jeutter. „AggMon: Scalable Hierarchical Cluster Monitoring“. In Sustained Simulation Performance 2012, 51–64. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32454-3_5.

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Paul, Kristina Ayers. „Collaborative Evaluation for Program Monitoring“. In Total School Cluster Grouping & Differentiation, 87–96. 2. Aufl. New York: Routledge, 2021. http://dx.doi.org/10.4324/9781003239239-7.

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Fink, O., L. Jenni, H. Nguyen und N. Ponnudurai. „Cluster analysis of condition monitoring data“. In Risk, Reliability and Safety: Innovating Theory and Practice, 1978–85. Taylor & Francis Group, 6000 Broken Sound Parkway NW, Suite 300, Boca Raton, FL 33487-2742: CRC Press, 2016. http://dx.doi.org/10.1201/9781315374987-300.

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Balaton, Zoltán, Péter Kacsuk, Norbert Podhorszki und Ferenc Vajda. „From Cluster Monitoring to Grid Monitoring Based on GRM“. In Euro-Par 2001 Parallel Processing, 874–81. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-44681-8_121.

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Ayarza, Veronica Castro, und Sussy Bayona-Oré. „Cluster Monitoring and Integration in Technology Company“. In Advances in Intelligent Systems and Computing, 253–65. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-33547-2_19.

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Sosnowski, Janusz, Marcin Kubacki und Henryk Krawczyk. „Monitoring Event Logs within a Cluster System“. In Complex Systems and Dependability, 257–71. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-30662-4_17.

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Kuczynski, Tomasz, Roman Wyrzykowski und Grzegorz Studzinski. „Cluster Monitoring and Management in the WebCI Environment“. In Parallel Processing and Applied Mathematics, 375–82. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24669-5_49.

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Konferenzberichte zum Thema "Cluster monitoring"

Stefanov, Konstantin, und Vladimir Voevodin. „Distributed Modular Monitoring (DiMMon) Approach to Supercomputer Monitoring“. In 2015 IEEE International Conference on Cluster Computing (CLUSTER). IEEE, 2015. http://dx.doi.org/10.1109/cluster.2015.83.

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Sacerdoti, Katz, Massie und Culler. „Wide area cluster monitoring with Ganglia“. In Proceedings IEEE International Conference on Cluster Computing CLUSTR-03. IEEE, 2003. http://dx.doi.org/10.1109/clustr.2003.1253327.

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Evans, Richard Todd. „Democratizing Parallel Filesystem Monitoring“. In 2020 IEEE International Conference on Cluster Computing (CLUSTER). IEEE, 2020. http://dx.doi.org/10.1109/cluster49012.2020.00065.

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Rohl, Thomas, Jan Eitzinger, Georg Hager und Gerhard Wellein. „LIKWID Monitoring Stack: A Flexible Framework Enabling Job Specific Performance monitoring for the masses“. In 2017 IEEE International Conference on Cluster Computing (CLUSTER). IEEE, 2017. http://dx.doi.org/10.1109/cluster.2017.115.

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Baker und Smith. „GridRM: an extensible resource monitoring system“. In Proceedings IEEE International Conference on Cluster Computing CLUSTR-03. IEEE, 2003. http://dx.doi.org/10.1109/clustr.2003.1253317.

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Brown, Connor, Benjamin Schwaller, Nathan Gauntt, Benjamin Allan und Kevin Davis. „Standardized Environment for Monitoring Heterogeneous Architectures“. In 2019 IEEE International Conference on Cluster Computing (CLUSTER). IEEE, 2019. http://dx.doi.org/10.1109/cluster.2019.8890997.

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Allan, Benjamin A., Michael Aguilar, Benjamin Schwaller und Steven Langer. „LDMS Monitoring of EDR InfiniBand Networks“. In 2020 IEEE International Conference on Cluster Computing (CLUSTER). IEEE, 2020. http://dx.doi.org/10.1109/cluster49012.2020.00066.

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Ahlgren, Ville, Stefan Andersson, Jim Brandt, Nicholas Cardo, Sudheer Chunduri, Jeremy Enos, Parks Fields et al. „Large-Scale System Monitoring Experiences and Recommendations“. In 2018 IEEE International Conference on Cluster Computing (CLUSTER). IEEE, 2018. http://dx.doi.org/10.1109/cluster.2018.00069.

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Juve, Gideon, Benjamin Tovar, Rafael Ferreira Da Silva, Dariusz Krol, Douglas Thain, Ewa Deelman, William Allcock und Miron Livny. „Practical Resource Monitoring for Robust High Throughput Computing“. In 2015 IEEE International Conference on Cluster Computing (CLUSTER). IEEE, 2015. http://dx.doi.org/10.1109/cluster.2015.115.

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Bonnie, Amanda, Mike Mason und Daniel Illescas. „Monitoring Infrastructure: The Challenges of Moving Beyond Petascale“. In 2017 IEEE International Conference on Cluster Computing (CLUSTER). IEEE, 2017. http://dx.doi.org/10.1109/cluster.2017.89.

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Berichte der Organisationen zum Thema "Cluster monitoring"

Duarte, Marco Antonio, Raymond Luke Rindels, Kody Jacob Everson, Olena Lopatina, Todd Alan Yilk und Anthony Louis Lopez. Job Profiling and Cluster Monitoring for Users. Office of Scientific and Technical Information (OSTI), August 2018. http://dx.doi.org/10.2172/1463584.

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Gentile, Ann C., James M. Brandt, M. H. Wong und Philippe Pierre Pebay. Monitoring computational clusters with OVIS. US: Sandia National Laboratories, Dezember 2006. http://dx.doi.org/10.2172/899078.

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Garcia-Lomeli, H. D., A. D. Bertsch und D. M. Fox. Continuous Security and Configuration Monitoring of HPC Clusters. Office of Scientific and Technical Information (OSTI), Mai 2015. http://dx.doi.org/10.2172/1184182.

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Warta, Katharina, Tobias Dudenbostel, María del Carmen Calatrava Moreno, Francesca Guadagno, Simon Zingerle, Sandra Skok und Harald Grill. Evaluierung des COMET-Programms. Technopolis Group - Austria, Juni 2021. http://dx.doi.org/10.22163/fteval.2022.524.

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Technopolis wurde im Mai 2020 vom Bundesministerium für Klimaschutz, Umwelt, Energie, Mobilität, Innovation und Technologie (BMK) und dem Bundesministerium für Digitalisierung und Wirtschaftsstandort (BMDW) mit der Evaluierung des COMET-Programms beauftragt. Die vorliegende Evaluierung bezieht sich auf die 25 aktuell existierenden Zentren. Der Fokus der Evaluierung liegt auf der Charakterisierung der COMET-Zentren und auf dem Monitoring- und Kennzahlensystem von COMET. Im Jahr der Corona-Pandemie wurden die Methoden angepasst, Interviews mit Stakeholdern und den Zentren wurden meist per Videocalls oder telefonisch geführt, geplante on-site Visits sowie internationale Workshops konnten nicht durchgeführt werden. Zentrale Quellen der Evaluierung sind eine Befragung der Zentren und der Unternehmens- und Wissenschaftspartner sowie die Datenbasis der FFG, die wir durch diverse Harmonisierungsschritte für neuwertige Auswertungen nutzbar machten. Dies umfasst insbesondere einen historischen Rückblick sowie eine Cluster- und Netzwerkanalyse. Die Auswartungen sind in zwei Dashboards angelegt, das Dashboard zur COMET-Befragung ist anonymisiert und daher öffentlich zugänglich.

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Nadeau, Robert Michael. Characterization and application of microearthquake clusters to problems of scaling, fault zone dynamics, and seismic monitoring at Parkfield, California. Office of Scientific and Technical Information (OSTI), Oktober 1995. http://dx.doi.org/10.2172/177321.

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Ramm-Granberg, Tynan, F. Rocchio, Catharine Copass, Rachel Brunner und Eric Nelsen. Revised vegetation classification for Mount Rainier, North Cascades, and Olympic national parks: Project summary report. National Park Service, Februar 2021. http://dx.doi.org/10.36967/nrr-2284511.

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Field crews recently collected more than 10 years of classification and mapping data in support of the North Coast and Cascades Inventory and Monitoring Network (NCCN) vegetation maps of Mount Rainier (MORA), Olympic (OLYM), and North Cascades (NOCA) National Parks. Synthesis and analysis of these 6000+ plots by Washington Natural Heritage Program (WNHP) and Institute for Natural Resources (INR) staff built on the foundation provided by the earlier classification work of Crawford et al. (2009). These analyses provided support for most of the provisional plant associations in Crawford et al. (2009), while also revealing previously undescribed vegetation types that were not represented in the United States National Vegetation Classification (USNVC). Both provisional and undescribed types have since been submitted to the USNVC by WNHP staff through a peer-reviewed process. NCCN plots were combined with statewide forest and wetland plot data from the US Forest Service (USFS) and other sources to create a comprehensive data set for Washington. Analyses incorporated Cluster Analysis, Nonmetric Multidimensional Scaling (NMS), Multi-Response Permutation Procedure (MRPP), and Indicator Species Analysis (ISA) to identify, vet, and describe USNVC group, alliance, and association distinctions. The resulting revised classification contains 321 plant associations in 99 alliances. A total of 54 upland associations were moved through the peer review process and are now part of the USNVC. Of those, 45 were provisional or preliminary types from Crawford et al. (2009), with 9 additional new associations that were originally identified by INR. WNHP also revised the concepts of 34 associations, wrote descriptions for 2 existing associations, eliminated/archived 2 associations, and created 4 new upland alliances. Finally, WNHP created 27 new wetland alliances and revised or clarified an additional 21 as part of this project (not all of those occur in the parks). This report and accompanying vegetation descriptions, keys and synoptic and environmental tables (all products available from the NPS Data Store project reference: https://irma.nps.gov/DataStore/Reference/Profile/2279907) present the fruit of these combined efforts: a comprehensive, up-to-date vegetation classification for the three major national parks of Washington State.

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Henderson, Tim, Vincent Santucci, Tim Connors und Justin Tweet. National Park Service geologic type section inventory: Klamath Inventory & Monitoring Network. National Park Service, Juli 2021. http://dx.doi.org/10.36967/nrr-2286915.

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A fundamental responsibility of the National Park Service (NPS) is to ensure that park resources are preserved, protected, and managed in consideration of the resources themselves and for the benefit and enjoyment by the public. Through the inventory, monitoring, and study of park resources, we gain a greater understanding of the scope, significance, distribution, and management issues associated with these resources and their use. This baseline of natural resource information is available to inform park managers, scientists, stakeholders, and the public about the conditions of these resources and the factors or activities which may threaten or influence their stability. There are several different categories of geologic or stratigraphic units (supergroup, group, formation, member, bed) which represent a hierarchical system of classification. The mapping of stratigraphic units involves the evaluation of lithologies, bedding properties, thickness, geographic distribution, and other factors. If a new mappable geologic unit is identified, it may be described and named through a rigorously defined process that is standardized and codified by the professional geologic community (North American Commission on Stratigraphic Nomenclature 2005). In most instances when a new geologic unit such as a formation is described and named in the scientific literature, a specific and well-exposed section of the unit is designated as the type section or type locality (see Definitions). The type section is an important reference section for a named geologic unit which presents a relatively complete and representative profile. The type or reference section is important both historically and scientifically, and should be protected and conserved for researchers to study and evaluate in the future. Therefore, this inventory of geologic type sections in NPS areas is an important effort in documenting these locations in order that NPS staff recognize and protect these areas for future studies. The documentation of all geologic type sections throughout the 423 units of the NPS is an ambitious undertaking. The strategy for this project is to select a subset of parks to begin research for the occurrence of geologic type sections within particular parks. The focus adopted for completing the baseline inventories throughout the NPS was centered on the 32 inventory and monitoring networks (I&M) established during the late 1990s. The I&M networks are clusters of parks within a defined geographic area based on the ecoregions of North America (Fenneman 1946; Bailey 1976; Omernik 1987). These networks share similar physical resources (geology, hydrology, climate), biological resources (flora, fauna), and ecological characteristics. Specialists familiar with the resources and ecological parameters of the network, and associated parks, work with park staff to support network level activities (inventory, monitoring, research, data management). Adopting a network-based approach to inventories worked well when the NPS undertook paleontological resource inventories for the 32 I&M networks. The network approach is also being applied to the inventory for the geologic type sections in the NPS. The planning team from the NPS Geologic Resources Division who proposed and designed this inventory selected the Greater Yellowstone Inventory and Monitoring Network (GRYN) as the pilot network for initiating this project. Through the research undertaken to identify the geologic type sections within the parks of the GRYN methodologies for data mining and reporting on these resources were established. Methodologies and reporting adopted for the GRYN have been used in the development of this type section inventory for the Klamath Inventory & Monitoring Network. The goal of this project is to consolidate information pertaining to geologic type sections which occur within NPS-administered areas, in order that this information is available throughout the NPS to inform park managers...

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Henderson, Tim, Mincent Santucci, Tim Connors und Justin Tweet. National Park Service geologic type section inventory: Chihuahuan Desert Inventory & Monitoring Network. National Park Service, April 2021. http://dx.doi.org/10.36967/nrr-2285306.

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A fundamental responsibility of the National Park Service is to ensure that park resources are preserved, protected, and managed in consideration of the resources themselves and for the benefit and enjoyment by the public. Through the inventory, monitoring, and study of park resources, we gain a greater understanding of the scope, significance, distribution, and management issues associated with these resources and their use. This baseline of natural resource information is available to inform park managers, scientists, stakeholders, and the public about the conditions of these resources and the factors or activities which may threaten or influence their stability. There are several different categories of geologic or stratigraphic units (supergroup, group, formation, member, bed) which represent a hierarchical system of classification. The mapping of stratigraphic units involves the evaluation of lithologies, bedding properties, thickness, geographic distribution, and other factors. If a new mappable geologic unit is identified, it may be described and named through a rigorously defined process that is standardized and codified by the professional geologic community (North American Commission on Stratigraphic Nomenclature 2005). In most instances when a new geologic unit such as a formation is described and named in the scientific literature, a specific and well-exposed section of the unit is designated as the type section or type locality (see Definitions). The type section is an important reference section for a named geologic unit which presents a relatively complete and representative profile for this unit. The type or reference section is important both historically and scientifically, and should be recorded such that other researchers may evaluate it in the future. Therefore, this inventory of geologic type sections in NPS areas is an important effort in documenting these locations in order that NPS staff recognize and protect these areas for future studies. The documentation of all geologic type sections throughout the 423 units of the NPS is an ambitious undertaking. The strategy for this project is to select a subset of parks to begin research for the occurrence of geologic type sections within particular parks. The focus adopted for completing the baseline inventories throughout the NPS was centered on the 32 inventory and monitoring networks (I&M) established during the late 1990s. The I&M networks are clusters of parks within a defined geographic area based on the ecoregions of North America (Fenneman 1946; Bailey 1976; Omernik 1987). These networks share similar physical resources (geology, hydrology, climate), biological resources (flora, fauna), and ecological characteristics. Specialists familiar with the resources and ecological parameters of the network, and associated parks, work with park staff to support network level activities (inventory, monitoring, research, data management). Adopting a network-based approach to inventories worked well when the NPS undertook paleontological resource inventories for the 32 I&M networks. The network approach is also being applied to the inventory for the geologic type sections in the NPS. The planning team from the NPS Geologic Resources Division who proposed and designed this inventory selected the Greater Yellowstone Inventory and Monitoring Network (GRYN) as the pilot network for initiating this project. Through the research undertaken to identify the geologic type sections within the parks of the GRYN, methodologies for data mining and reporting on these resources was established. Methodologies and reporting adopted for the GRYN have been used in the development of this type section inventory for the Chihuahuan Desert Inventory & Monitoring Network. The goal of this project is to consolidate information pertaining to geologic type sections which occur within NPS-administered areas, in order that this information is available throughout the NPS...

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Henderson, Tim, Vincent Santucci, Tim Connors und Justin Tweet. National Park Service geologic type section inventory: Northern Colorado Plateau Inventory & Monitoring Network. National Park Service, April 2021. http://dx.doi.org/10.36967/nrr-2285337.

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A fundamental responsibility of the National Park Service (NPS) is to ensure that park resources are preserved, protected, and managed in consideration of the resources themselves and for the benefit and enjoyment by the public. Through the inventory, monitoring, and study of park resources, we gain a greater understanding of the scope, significance, distribution, and management issues associated with these resources and their use. This baseline of natural resource information is available to inform park managers, scientists, stakeholders, and the public about the conditions of these resources and the factors or activities which may threaten or influence their stability. There are several different categories of geologic or stratigraphic units (supergroup, group, formation, member, bed) which represent a hierarchical system of classification. The mapping of stratigraphic units involves the evaluation of lithologies, bedding properties, thickness, geographic distribution, and other factors. If a new mappable geologic unit is identified, it may be described and named through a rigorously defined process that is standardized and codified by the professional geologic community (North American Commission on Stratigraphic Nomenclature 2005). In most instances when a new geologic unit such as a formation is described and named in the scientific literature, a specific and well-exposed section of the unit is designated as the type section or type locality (see Definitions). The type section is an important reference section for a named geologic unit which presents a relatively complete and representative profile. The type or reference section is important both historically and scientifically, and should be available for other researchers to evaluate in the future. Therefore, this inventory of geologic type sections in NPS areas is an important effort in documenting these locations in order that NPS staff recognize and protect these areas for future studies. The documentation of all geologic type sections throughout the 423 units of the NPS is an ambitious undertaking. The strategy for this project is to select a subset of parks to begin research for the occurrence of geologic type sections within particular parks. The focus adopted for completing the baseline inventories throughout the NPS was centered on the 32 inventory and monitoring networks (I&M) established during the late 1990s. The I&M networks are clusters of parks within a defined geographic area based on the ecoregions of North America (Fenneman 1946; Bailey 1976; Omernik 1987). These networks share similar physical resources (geology, hydrology, climate), biological resources (flora, fauna), and ecological characteristics. Specialists familiar with the resources and ecological parameters of the network, and associated parks, work with park staff to support network level activities (inventory, monitoring, research, data management). Adopting a network-based approach to inventories worked well when the NPS undertook paleontological resource inventories for the 32 I&M networks. The network approach is also being applied to the inventory for the geologic type sections in the NPS. The planning team from the NPS Geologic Resources Division who proposed and designed this inventory selected the Greater Yellowstone Inventory and Monitoring Network (GRYN) as the pilot network for initiating this project. Through the research undertaken to identify the geologic type sections within the parks of the GRYN methodologies for data mining and reporting on these resources was established. Methodologies and reporting adopted for the GRYN have been used in the development of this type section inventory for the Northern Colorado Plateau Inventory & Monitoring Network. The goal of this project is to consolidate information pertaining to geologic type sections which occur within NPS-administered areas, in order that this information is available throughout the NPS...

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Aiginger, Karl, Andreas Reinstaller, Michael Böheim, Rahel Falk, Michael Peneder, Susanne Sieber, Jürgen Janger et al. Evaluation of Government Funding in RTDI from a Systems Perspective in Austria. Synthesis Report. WIFO, Austria, August 2009. http://dx.doi.org/10.22163/fteval.2009.504.

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In the spring of 2008, WIFO, KMU Forschung Austria, Prognos AG in Germany and convelop were jointly commissioned by the Austrian Federal Ministry for Transport, Innovation and Technology and the Austrian Federal Ministry of Economy, Family and Youth to perform a systems evaluation of the country's research promotion and funding activities. Based on their findings, six recommendations were developed for a change in Austrian RTDI policy as outlined below: 1. to move from a narrow to a broader approach in RTDI policy (links to education policy, consideration of the framework for innovation such as competition, international perspectives and mobility); 2. to move from an imitation to a frontrunner strategy (striving for excellence and market leadership in niche and high-quality segments, increasing market shares in advanced sectors and technology fields, and operating in segments of relevance for society); 3. to move from a fragmented approach to public intervention to a more coordinated and consistent approach(explicit economic goals, internal and external challenges and reasoning for public intervention); 4. to move from a multiplicity of narrowly defined funding programmes to a flexible, dynamic policy that uses a broader definition of its tasks and priorities (key technology and research segments as priority-action fields, adequate financing of clusters and centres of excellence); 5. to move from an unclear to a precisely defined allocation of responsibilities between ministries and other players in the field (high-ranking steering group at government level, monitoring by a Science, Research and Innovation Council); 6. to move from red-tape-bound to a modern management of public intervention (institutional separation between ministries formulating policies and agencies executing them, e.g., by "progressive autonomy").

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