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The applied research for visualization methods in recommendation systems are demonstrated from the data analysis, results and explanation of the recommendations and human-computer interaction. First, various categories of visualization methods thoughts are described, and their characteristics, pros and cons are analyzed. Second, the typical application applying visualization methods are listed. Finally,...
Data analytics becomes increasingly important in big data applications. Adaptively subsetting large amounts of data to extract the interesting events such as the centers of hurricane or thunderstorm, statistically analyzing and visualizing the subset data, is an effective way to analyze ever-growing data. This is particularly crucial for analyzing Earth Science data, such as extreme weather. The Hadoop...
Both academia and organizations show great interest in streaming big data analytics - the process of extracting knowledge structures from continuous, high volume and high velocity continuous flow of data in a myriad of formats from a variety of real-time data sources. The challenge for organizations lies in being able to transform this deluge of data into instantaneous intelligence that can enable...
As scientific simulation applications evolve on the path towards exascale, a new model of scientific inquiry is required where concurrently with the running simulation, online analytics operate on the data it produces. By avoiding offline data storage except when absoluately necessary, it enables speeding up the scientific discovery process by providing rapid insights into the simulated science phenomena...
In this paper we proposed a scalable interactive system for fresco deterioration detection by hyper-spectral image data analysis. The system integrates data mining and visualization algorithm and process the hyper-spectral big data from fresco efficiently and conveniently. Firstly, a Geospatial Data Abstraction Library (GDAL) is adapted which provides data reading, image preview and cropping functions,...
Carsharing has emerged as an alternative to vehicle ownership and is a rapidly expanding global market. Particularly through the flexibility of free-floating models, car sharing complements public transport since customers do not need to return cars to specific stations. We present a novel data analytics approach that provides decision support to car sharing operators -- from local start-ups to global...
In risk assessment applications well informed decisions are made based on huge amounts of multi-dimensional data. In many domains not only the risk of a wrong decision, but in particular the trade-off between the costs of possible decisions are of utmost importance. In this paper we describe a framework tightly integrating interactive visual exploration with machine learning to support the decision...
Modern machine learning techniques provide robust approaches for data-driven modeling and critical information extraction, while human experts hold the advantage of possessing high-level intelligence and domain-specific expertise. We combine the power of the two for anomaly detection in GPS data by integrating them through a visualization and human-computer interaction interface. In this paper we...
Researched and developed the software system of Information Management and Decision Support System of the hidden danger troubleshooting of Coal Mine. By using the technology of collaborative software to realize fast circulation of information, dynamic tracking, rapid feedback, and closed-loop management of transaction processing for the hidden danger, and by applying the technology of data mining,...
In this paper, we used X-Means clustering algorithm, incorporated data images from a so-called Iterative Data Image Rotated Bar Graph (iDIRBrG) method (formerly referred as BC method) and used Vector Fusion Visualization to achieve better traffic data analysis results compared to our previous work by improving how data signatures are constructed from the raw data set. By doing so, we effectively identify...
During the past years the first tools for visual analysis of trajectory data appeared. Considering the growing sizes of trajectory collections, one important task is to ensure user interactivity during data analysis. In this paper we present a fast, model-based visualization approach for the analysis of location dependencies in large trajectory collections. Existing approaches are not suitable for...
Ordinary users are finding it increasingly difficult to explore the large volumes of diverse data they encounter in their everyday lives. Techniques based on data mining algorithms are useful but they tend to be too complex for casual users to work with effectively. Furthermore, these techniques don't allow the user to engage with the information using semantics meaningful to them. Semantically enriched...
The complex nature of multivariate data sets calls for high interactive performance and intuitive metaphors. A specific type of multivariate data is where the variables sum up to a constant, here defined as multicomponent data. This application paper presents an interactive application for analysis of modeled multicomponent data. The aim is to find high performance variable combinations that fulfill...
The last years witnessed a continued growth of the amount of data. The data analysis and exploration has become more and more difficult. So, it seems important to find means to visually represent this flood of data. Information visualization can help any user to get and understand information efficiently and implicate him/her in the data mining process thanks to our perception possibilities. The visualization...
The emergence of petascale computing is creating a tsunami of data from peta-scale simulations. Typically, results are analyzed by dozens of scientists who often work as teams. Obviously, it is very important to help these teams by facilitating management, analysis, sharing, and visualization of the data produced by their simulations, and by the auxiliary programs and activities used in the scientific...
The engineering experiments need to highlight the logics, steps, rules and details of involved intricate operations, but which are always neglected by the students. In this paper, we propose our workflow technology based virtual laboratory system (WFVL) for data mining. It subdivides the general data analysis model into detachable elements and encapsulates them as reusable Web services, which can...
The feedback of sonification on CAD analyzing information in addition to picture interface increases userpsilas accepting information and decreases the load of visual channel. Data overall can be observed and analyzed. According to characteristic of design data, machine simulation and data comparison analysis, data-sound mapping is analyzed. With the example of pressure angle checking about cam mechanism,...
With the dramatic increases in simulation complexity and resolution comes an equally dramatic challenge for resources, both computational and storage, needed to facilitate analysis and understanding of the results. Traditionally these needs have been met by powerful workstations equipped with sophisticated analysis tools and special purpose visualization hardware. More and more these personal computing...
The speed of data retrieval qualitatively affects how analysts visually explore and analyze their data. To ensure smooth interactions in massive time series datasets, one needs to address the challenges of computing adhoc queries, distributing query load, and hiding system latency. In this paper, we present ATLAS, a visualization tool for temporal data that addresses these issues using a combination...
We present techniques for discovering and exploiting regularity in large curvilinear data sets. The data can be based on a single mesh or a mesh composed of multiple submeshes (also known as zones). Multi-zone data are typical in Computational Fluid Dynamics (CFD) simulations. Regularities include axis-aligned rectilinear and cylindrical meshes as well as cases where one zone is equivalent to a rigid...
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