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Multi-parameter instrument array and control system (MPIACS): A software interface implementation of real-time data acquisition and visualization for environmental monitoring

Multi-parameter instrument array and control system (MPIACS)...

引用

Celebrating the Past... Teaming Toward the Future

作者： Ojo, T. Sterling Jr., M.C. Bonner, J.S. Fuller, C.B. Kelly, F. Page, C.A. Civil Engineering Department Texas A and M University College Station TX 77843-3136 United States The Conrad Blucher Institute Texas A and M Univ. - Corpus Christi Corpus Christi TX 78412 United States

Recent developments in sensor technology over the past decade have shifted the paradigm in environmental as well as oceanographic sampling and monitoring. The environmental and oceanography scientific communities continue to deploy more and more in-situ sensors either as single-unit deployments for generation of time-series and vertical profiles or as a towed-array for mapping environmental variables in surface waters in multi-dimensional space. Emergency response activities also are enhanced by way of real-time geo-referenced data acquisition using mobile in-situ sensor arrays. Against this backdrop, we developed a software interface for real-time data acquisition, analysis and visualization from a towed-array of sensors. This is the Multi-parameter Instrument Array and Control System (MPIACS) described in this paper and was used in the fall of 2002 during a mock oil-spill exercise in Corpus Christi Bay, TX under the auspices of the Texas General Land Office (TGLO). The interface is designed for correlative visualization along the transect between as many as six state variables or environmental indicators allowing for the implementation of adaptive sampling schemes. The software can also be used for scheduled sampling of water quality parameters in surface waters.

关键词： Oceanographic instruments

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Improving Production Monitoring and Surveillance by Using An Integrated Production data Management

Improving Production Monitoring and Surveillance by Using An...

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Eighth Latin American and Caribbean Petroleum Engineering conference

作者： Aprilian, Salis Maria, Regina Pertamina DOH SBS GeoQuest-Schlumberger

Pertamina Upstream's strategy is that "First Quality, Then Growth". As a part of Pertamina's drive to improve quality (productivity and efficiency), Pertamina DOH SBS which is the biggest upstream business unit of Pertamina located in South Sumatra initiated the steps, towards the end of the year 2000, to improve the overall data quality and information management process related to production monitoring and surveillance. With the vision to fully automate the production information system, Pertamina DOH SBS reviewed the availably commercial software offered in the area of production information management. The solution is intended to provide a structured data capturing system as well as overall framework towards an integrated Production data Management System (PDMS) for Pertamina DOH SBS. The system implementation was successfully completed in the first quarter of 2002. It is used everyday to capture daily field production data, production allocation, and it provides "Daily Morning Production Report" posted at the Pertamina DOH SBS website (intranet) by 8am every morning. This field data capture system replaced the MS Excel™ spreadsheet files that were used previously to capture and store daily production data. It also provides the user quality control tools like graphical visualization of historical data, which has significantly contributed towards increasing the confidence in the quality of data. This Production data Management System provides the engineers with reliable data, thus minimize time spend for data collecting. Instead, more time will be spent for engineering analysis to increase field productivity. Besides savings in the time, the improved quality and timely availability of data will considerably improve the success rate by allowing users to make well informed decisions.

关键词： Petroleum engineering

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Improving Production Monitoring and Surveillance by Using an Integrated Production data Management

Improving Production Monitoring and Surveillance by Using an...

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SPE Asia Pacific Oil and Gas conference and Exhibition

作者： Aprilian, Salis Maria, Regina SPE Pertamina DOH SBS GeoQuest-Schlumberger

Pertamina Upstream's strategy is "First Quality, Then Growth". As a part of Pertamina's drive to improve quality (productivity and efficiency), Pertamina DOH SBS which is the biggest upstream business unit of Pertamina located in South Sumatra initiated the steps, towards the end of the year 2000, to improve the overall data quality and information management process related to production monitoring and surveillance. With the vision to fully automate the production information system, Pertamina DOH SBS reviewed the availably commercial software offered in the area of production information management. The solution is intended to provide a structured data capturing system as well as overall framework towards an integrated Production data Management System (PDMS) for Pertamina DOH SBS. The system implementation was successfully completed in the first quarter of 2002. It is used everyday to capture daily field production data, production allocation, and it provides "Daily Morning Production Report" posted at the Pertamina DOH SBS website (intranet) by 8am every morning. This field data capture system replaced the MS Excel™ spreadsheet files that were used previously to capture and store daily production data. It also provides the user quality control tools like graphical visualization of historical data, which has significantly contributed towards increasing the confidence in the quality of data. This Production data Management System provides the engineers with reliable data, thus minimize time spend for data collecting. Instead, more time will be spent for engineering analysis to increase field productivity. Besides savings in the time, the improved quality and timely availability of data will considerably improve the success rate by allowing users to make well informed decisions.

关键词： Petroleum industry

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Using bitmap index for interactive exploration of large datasets 15

Using bitmap index for interactive exploration of large data...

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15th International conference on Scientific and Statistical database Management

作者： Wu, KS Koegler, W Chen, J Shoshani, A Univ Calif Berkeley Lawrence Berkeley Lab Berkeley CA 94720 USA

ISBN: (纸本)0769519644

Many scientific applications generate large spatio-temporal datasets. A common way of exploring these datasets is to identify and track regions of interest. Usually these regions are defined as contiguous sets of points whose attributes satisfy some user defined conditions, e.g. high temperature regions in a combustion simulation. At each time step, the regions of interest may be identified by first searching for all points that satisfy the conditions and then grouping the points into connected regions. To speed up this process, the searching step may use a tree-based indexing scheme, such as a KD-tree or an Octree. However, these indices are efficient only if the searches are limited to one or a small number of selected attributes. Scientific datasets often contain hundreds of attributes and scientists frequently study these attributes in complex combinations, e.g. finding regions of high temperature and low pressure. Bitmap indexing is an efficient method for searching on multiple criteria simultaneously. We apply a bitmap compression scheme to reduce the size of the indices. In addition, we show that the compressed bitmaps can be used efficiently to perform the region growing and the region tracking operations. Analyses show that our approach scales well and our tests on two datasets from simulation of the autoignition process show impressive performance.

关键词： Analytical models Combustion data visualization Energy management Indexing Laboratories Performance analysis Spatiotemporal phenomena Temperature Testing

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5th International conference on High Performance Computing for Computational Science – VECPAR 2002

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5th International conference on High Performance Computing for Computational Science, VECPAR 2002

ISBN: (纸本)3540008527

The proceedings contain 49 papers. The special focus in this conference is on Fluids, structures and data mining. The topics include: Fluid-structure interaction simulations using parallel computers;investigation of turbulent flame kernels using DNS on clusters;A parallel, state-of-the-art, least-squares spectral element solver for incompressible flow problems;edge-based interface elements for solution of three-dimensional geomechanical problems;finite element simulation of seismic elastic two dimensional wave propagation;parallel implementation for probabilistic analysis of 3D discrete cracking in concrete;an a posteriori error estimator for adaptive mesh refinement using parallel in-element particle tracking methods;high performance data mining;data mining for data classification based on the KNN-fuzzy method supported by genetic algorithm;a major challenge for computational chemistry;high performance computing in electron microscope tomography of complex biological structures;visualization of RNA pseudoknot structures;the cactus framework and toolkit;performance of message-passing MATLAB toolboxes;evaluating the performance of space plasma simulations using FPGA’s;remote parallel model reduction of linear time-invariant systems made easy;an approach to teaching computer arithmetic;fast sparse matrix-vector multiplication for TeraFlop/s computers;performance evaluation of parallel gram-Schmidt re-orthogonalization methods;toward memory-efficient linear solvers;a parallel Newton-GMRES algorithm for solving large scale nonlinear systems;preconditioning for an iterative elliptic solver on a vector processor;a large scale electron scattering simulation dominated by the multiplication of dynamically changing matrices;a parallel implementation of the Atkinson algorithm for solving a fredholm equation.

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High performance data mining

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5th International conference on High Performance Computing for Computational Science, VECPAR 2002

作者： Kumar, Vipin Joshi, Mahesh V. Han, Eui-Hong Sam Tan, Pang-Ning Steinbach, Michael University of Minnesota 4-192 EE/CSci Building 200 Union Street SE MinneapolisMN55455 United States

ISBN: (纸本)3540008527

Recent times have seen an explosive growth in the availability of various kinds of data. It has resulted in an unprecedented opportunity to develop automated data-driven techniques of extracting useful knowledge. data mining, an important step in this process of knowledge discovery, consists of methods that discover interesting, non-trivial, and useful patterns hidden in the data[SAD+93, CHY96]. The field of data mining builds upon the ideas from diverse fields such as machine learning, pattern recognition, statistics, database systems, and data visualization. But, techniques developed in these traditional disciplines are often unsuitable due to some unique characteristics of today’s data-sets, such as their enormous sizes, high-dimensionality, and heterogeneity. There is a necessity to develop effective parallel algorithms for various data mining techniques. However, designing such algorithms is challenging, and the main focus of the paper is a description of the parallel formulations of two important data mining algorithms: discovery of association rules, and induction of decision trees for classification. We also briefly discuss an application of data mining to the analysis of large data sets collected by Earth observing satellites that need to be processed to better understand global scale changes in biosphere processes and patterns. © Springer-Verlag Berlin Heidelberg 2003.

关键词： data mining

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data exploration with paired hierarchical visualizations: initial designs of PairTrees 03

Data exploration with paired hierarchical visualizations: in...

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Proceedings of the 2003 annual national conference on Digital government research

作者： Bill Kules Ben Shneiderman Catherine Plaisant University of Maryland at College Park College Park MD

Paired hierarchical visualizations (PairTrees) integrate treemaps, node-link diagrams, choropleth maps and other information visualization techniques to support exploration of hierarchical data sets at multiple levels of abstraction (Kules, Shneiderman et al., in press). Coordinated visualizations are an effective way to support exploratory data analysis of multidimensional data sets. Hierarchies are often used to reduce complexity, show structure, and support reasoning at multiple levels of abstraction or aggregation. Our work seeks to integrate hierarchical and coordinated visualizations, taking advantage of the semantics embedded in aggregate, sub-class, containment, and other forms of *** demonstration shows two novel applications of PairTrees in the econometric and health statistics domains. The first application demonstrates PairTrees used to enhance the immediate comprehension and usability of a treemap of disease mortality data from the National Center for Health Statistics. Treemaps (Johnson and Shneiderman, 1991) are an effective way to visualize hierarchies of quantitative data, however their structure is often not immediately apparent to users because of the visual complexity of the display. To improve the immediate usability of this information-rich display and reduce learning time, the treemap is coupled with a SpaceTree interactive node-link diagram (Grosjean, Plaisant et al., 2002) to show an alternate view of the same hierarchy. Users can quickly find a node in the treemap by brushing it in the node-link diagram, and vice-versa. Zooming is also *** second application demonstrates simultaneous multi-level comparisons of aggregate data from separate hierarchies, using data from the US Census Bureau's Economic Census, specifically revenue data for the "Professional, Scientific and Technical Services" sector of the North American Industry Classification System (NAICS). Two treemaps are displayed, each showing a different aggregation

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Announcement: conference on visualization and data analysis (VDA 2003)

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Computers & Graphics 2002年第6期26卷 982-982页

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Announcement: conference on visualization and data analysis (VDA 2003)

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Computers & Graphics 2002年第5期26卷 827-827页

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Automated geodata analysis and metadata generation

Automated geodata analysis and metadata generation

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conference on visualization and data analysis 2002

作者： Balfanz, D Zentrum Graph Datenverarbeitung Ctr Comp Graph D-64283 Darmstadt Germany

ISBN: (纸本)0819444057

With the shift from production to information society, a parallel development has taken place in processing geo information. Today, the focus is often more on intelligent and complex use and analysis of existing data than on data acquisition. The tasks of users now are to find appropriate data, as well as appropriate analysis or mining methods, for their specific exploration goals. This paper first presents an integrated approach that uses metadata technology to guide users through data and method selection. Important prerequisites in the decision process are the user's correct understanding of geodata qualities and, to this end, the availability of metadata. Therefore, the core of the presented approach is then described in detail, i.e. metadata visualization and generation. The visualization part aims to make the user aware of the goal-related geodata qualities. It consists of an automated semantic level-of-detail method, using abstraction hierarchies and linked visualization functions. The underlying metadata is provided via a repository-based generator, which creates descriptive metadata by analysis and interpretation of the original geodata. Finally, an outlook over the next steps in automated support for geodata mining is given.

关键词： metadata geodata semantic LoD metadata visualization metadata generation

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