Static grids are commonly an ensemble of millions of reservoir property values. Analyzing this vast dataset is a challenging task and is usually performed via simple statistical parameters (e.g arithmetic mean and sta...
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ISBN:
(纸本)9781613998724
Static grids are commonly an ensemble of millions of reservoir property values. Analyzing this vast dataset is a challenging task and is usually performed via simple statistical parameters (e.g arithmetic mean and standard deviation). The objective of this paper is to present a quick and efficient way of analyzing static model realization results in a more visual, interactive and efficient manner allowing a multidisciplinary team to absorb information and key ideas quickly. The proposed methodology starts with a set of properties from a given static grid realization. The data are prepared with all necessary labeling and categorization to be analyzed (e.g. reservoir and segment indicators, well regions etc.). All kinds of data variations available in the static model are gathered and mobilized into dynamic data dashboards for further analysis. The interactive data visualization templates provide flexibility to filter information either by categories or data ranges. The filtering schemes are immediately propagated to all available plots in the dashboard highlighting common patterns from all the data relationships in the static model. The principle output from this methodology is an interactive data visualization panel that displays all necessary infographics related to reservoir data-trends. Starting from simple rock quality pie-charts, which can be mapped showing field-wide trends, porosity-permeability plots, to more detailed infographics of property ranges per well/reservoir indicator as well as summary tables of average properties per reservoirs/layers/segments etc. The ability to have all plots linked in a single display provides a simple but powerful platform to evaluate and interrogate data from the static grid using different views. data can be aggregated in different ways and summarized accordingly. With this continuous process of data filtering, it is possible to quickly identify outliers and collect evidence of data inconsistency within the static grid. The
data stories - this buzzword links together two different disciplines: computer science and journalism. The new relationship is called data-driven journalism. The emerging product of this relationship: data-based visu...
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A large amount of data has been accumulated in the field of film capacitors, and these data resources need to be utilized to release the hidden value and provide a reference paradigm for subsequent researchers. data v...
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At present, we are in the era of big data. With the continuous development of the Internet, we are generating huge data that we cannot observe at any time. Traditional data analysis has been unable to meet the current...
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Using technology techniques like data visualization and GIS analysis, the design and construction of a model-driven decision support system assist local leaders to analyze the utilization of land in the determination ...
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Making sense of large data sets can be challenging without visual aids. The purpose of this project was to use freely available, web-based tools to rapidly visualize and enable the exploration of relationships within ...
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In recent years, China's "Golden Courses" research has had a profound impact on the high-quality development of higher education. However, there are few articles that comprehensively analyze the relevant...
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Background: When medical data have been successfully recorded or exchanged between systems there appear a need to present the data consistently to ensure that it is clearly understood and interpreted. A standard based...
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Background: When medical data have been successfully recorded or exchanged between systems there appear a need to present the data consistently to ensure that it is clearly understood and interpreted. A standard based user interface can provide interoperability on the visual level. Objectives: The goal of this research was to develop, implement and evaluate an information model for building user interfaces for archetype based medical data. Methods: The following types of knowledge were identified as important elements and were included in the information model: medical content related attributes, data type related attributes, user-related attributes, device-related attributes. In order to support flexible and efficient user interfaces an approach that represents different types of knowledge with different models separating the medical concept from a visual concept and interface realization was chosen. We evaluated the developed approach using Guideline for Good Evaluation Practice in Health Informatics (GEP-HI). Results: We developed a higher level information model to complement the ISO 13606 archetype model. This enabled the specification of the presentation properties at the moment of the archetypes' definition. The model allows realizing different users' perspectives on the data. The approach was implemented and evaluated within a functioning EHR system. The evaluation involved 30 patients of different age and IT experience and 5 doctors. One month of testing showed that the time required reading electronic health records decreased for both doctors (from average 310 to 220 s) and patients (from average 95 to 39 s). Users reported a high level of satisfaction and motivation to use the presented data visualization approach especially in comparison with their previous experience. Conclusion: The introduced information model allows separating medical knowledge and presentation knowledge. The additional presentation layer will enrich the graphical user interface's fl
visualization is an essential tool for observing and analyzing origin-destination (OD) data, which encodes flows between geographic locations, e.g., in applications concerning commuting, migration, and transport of go...
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visualization is an essential tool for observing and analyzing origin-destination (OD) data, which encodes flows between geographic locations, e.g., in applications concerning commuting, migration, and transport of goods. However, depicting OD data often encounter issues of cluttering and occlusion. To address these issues, many visual designs feature data abstraction and visual abstraction, such as node aggregation and edge bundling, resulting in information loss. The recent theoretical and empirical developments in visualization have substantiated the merits of such abstraction, while confirming that viewers' knowledge can alleviate the negative impact due to information loss. It is thus desirable to map out different ways of losing and adding information in origin-destination data visualization (ODDV). We therefore formulate a new design space of ODDV based on the categorization of informative operations on OD data in data abstraction and visual abstraction. We apply this design space to existing ODDV methods, outline strategies for exploring the design space, and suggest ideas for further exploration.
In this study, the power of 12 of the most widely used meteorological drought indices was compared. For this purpose, the datasets of 12 stations (from 1967 to 2021) with different climatic conditions in Iran were use...
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In this study, the power of 12 of the most widely used meteorological drought indices was compared. For this purpose, the datasets of 12 stations (from 1967 to 2021) with different climatic conditions in Iran were used. For statistical analysis, multiple linear regression based on the relative importance metric introduced by the Lindeman, Merenda, Gold (MLR-LMG) and data visualization (DV) models were used. In the temporal assessment, the relative importance metrics (RIM) between the drought severity based on the different drought indices and the annual yield of rain-fed winter wheat (AYW) based on the fitted MLR-LMG model was investigated at the annual timescale in the chosen stations. In the spatial evaluation, the RIM between the drought severity based on the different drought indices and the AYW were investigated each year (1967,., 2021). The results showed that in temporal assessment, the modified standardized precipitation evapotranspiration index (MSPEI) was the most suitable (58.33% of selected stations). Also, in spatial evaluation, the MSPEI and Z-score were the most efficient drought indices (65.45% and 27.27% of the years, respectively). The validation results of the fitted MLR-LMG models showed that the models were trustworthy in all stations and all years.
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