Visual Exploration Techniques Research Articles

Towards air quality particulate-matter monitoring using low-cost sensor data and visual exploration techniques: case study of Kisumu, Kenya

Air quality monitoring in developing countries is in deplorable condition despite its impact on many aspects of life such as population health. Among the very fine particulate pollutants in the air, PM2.5 and PM10 are more numerous and therefore more dangerous. Higher exposure to these fine particles, which are difficult to perceive by the eye, would contribute to an increased risk of cardiovascular and respiratory diseases, as well as lung cancers. Low-cost sensor-based technologies are now emerging as an effective alternative to monitoring the concentration of particulate matter in ambient air. This technology also has the advantage of being less complex and therefore can involve more citizen participation. A concrete example on the African continent is the initiative of the Sensor.Africa project is based on the involvement and commitment of citizens to collect and share this data for free in their cities. Among more than 25 cities on the continent where the project has already been launched is the city of Kisumu in Kenya where it has been implemented since February 2019. The process used to deploy and process the data from the low-cost sensors was explained in detail for better understanding. The deployment of these sensors can open up a plethora of opportunities, but it also rises a number of challenges for effective air quality monitoring. In our case, the data obtained after sensor calibration sometimes suffer from a lack of accuracy and outliers. Therefore, data visualisation techniques are used to solve this problem before analysing and proposing perspectives for further use by different stakeholders. From a health point of view, few records showed critical values in this city for the PM2.5 and PM10 indicators.

Scale-Space Splatting: Reforming Spacetime for Cross-Scale Exploration of Integral Measures in Molecular Dynamics.

Understanding large amounts of spatiotemporal data from particle-based simulations, such as molecular dynamics, often relies on the computation and analysis of aggregate measures. These, however, by virtue of aggregation, hide structural information about the space/time localization of the studied phenomena. This leads to degenerate cases where the measures fail to capture distinct behaviour. In order to drill into these aggregate values, we propose a multi-scale visual exploration technique. Our novel representation, based on partial domain aggregation, enables the construction of a continuous scale-space for discrete datasets and the simultaneous exploration of scales in both space and time. We link these two scale-spaces in a scale-space space-time cube and model linked views as orthogonal slices through this cube, thus enabling the rapid identification of spatio-temporal patterns at multiple scales. To demonstrate the effectiveness of our approach, we showcase an advanced exploration of a protein-ligand simulation.

Loom: Complex large-scale visual insight for large hybrid IT infrastructure management

Interactive visual exploration techniques (IVET) such as those advocated by Shneiderman and extreme scale visual analytics have successfully increased our understanding of a variety of domains that produce huge amounts of complex data. In spite of their complexity, IT infrastructures have not benefited from the application of IVET techniques.Loom is inspired in IVET techniques and builds on them to tame increasing complexity in IT infrastructure management systems guaranteeing interactive response times and integrating key elements for IT management: Relationships between managed entities coming from different IT management subsystems, alerts and actions (or reconfigurations) of the IT setup. The Loom system builds on two main pillars: (1) a multiplex graph spanning data from different ITIMs; and (2) a novel visualisation arrangement: the Loom “Thread” visualisation model.We have tested this in a number of real-world applications, showing that Loom can handle million of entities without losing information, with minimum context switching, and offering better performance than other relational/graph-based systems. This ensures interactive response times (few seconds as 90th percentile). The value of the “Thread” visualisation model is shown in a qualitative analysis of users’ experiences with Loom.

ViDX: Visual Diagnostics of Assembly Line Performance in Smart Factories.

Visual analytics plays a key role in the era of connected industry (or industry 4.0, industrial internet) as modern machines and assembly lines generate large amounts of data and effective visual exploration techniques are needed for troubleshooting, process optimization, and decision making. However, developing effective visual analytics solutions for this application domain is a challenging task due to the sheer volume and the complexity of the data collected in the manufacturing processes. We report the design and implementation of a comprehensive visual analytics system, ViDX. It supports both real-time tracking of assembly line performance and historical data exploration to identify inefficiencies, locate anomalies, and form hypotheses about their causes and effects. The system is designed based on a set of requirements gathered through discussions with the managers and operators from manufacturing sites. It features interlinked views displaying data at different levels of detail. In particular, we apply and extend the Marey's graph by introducing a time-aware outlier-preserving visual aggregation technique to support effective troubleshooting in manufacturing processes. We also introduce two novel interaction techniques, namely the quantiles brush and samples brush, for the users to interactively steer the outlier detection algorithms. We evaluate the system with example use cases and an in-depth user interview, both conducted together with the managers and operators from manufacturing plants. The result demonstrates its effectiveness and reports a successful pilot application of visual analytics for manufacturing in smart factories.

Streamline Embedding for 3D Vector Field Exploration

We propose a new technique for visual exploration of streamlines in 3D vector fields. We construct a map from the space of all streamlines to points in IR(n) based on the preservation of the Hausdorff metric in streamline space. The image of a vector field under this map is a set of 2-manifolds in IR(n) with characteristic geometry and topology. Then standard clustering methods applied to the point sets in IR(n) yield a segmentation of the original vector field. Our approach provides a global analysis of 3D vector fields which incorporates the topological segmentation but yields additional information. In addition to a pure segmentation, the established map provides a natural "parametrization” visualized by the manifolds. We test our approach on a number of synthetic and real-world data sets.

Unsupervised discovery of relations for analysis of textual data

This paper addresses the problem of analysing textual data for evidence discovery. A novel framework in which to perform evidence discovery is proposed in order to reduce the quantity of data to be analysed, aid the analysts’ exploration of the data and enhance the intelligibility of the presentation of the data. The framework combines information extraction techniques with visual exploration techniques to provide a novel approach to performing evidence discovery, in the form of an evidence discovery system. By utilising unrestricted, unsupervised information extraction techniques, the investigator does not require input queries or keywords for searching, thus enabling the investigator to analyse portions of the data that may not have been identified by keyword searches. A preliminary study was performed to assess the usefulness of a text mining approach to evidence discovery from a text corpus in comparison with a traditional information retrieval approach. It was concluded that the novel approach to text analysis for evidence discovery presented in this paper is a viable and promising approach for consideration in digital forensics. The preliminary experiment showed that the results obtained from the evidence discovery system are sensible and useful.

VANTED: a system for advanced data analysis and visualization in the context of biological networks.

BackgroundRecent advances with high-throughput methods in life-science research have increased the need for automatized data analysis and visual exploration techniques. Sophisticated bioinformatics tools are essential to deduct biologically meaningful interpretations from the large amount of experimental data, and help to understand biological processes.ResultsWe present VANTED, a tool for the visualization and analysis of networks with related experimental data. Data from large-scale biochemical experiments is uploaded into the software via a Microsoft Excel-based form. Then it can be mapped on a network that is either drawn with the tool itself, downloaded from the KEGG Pathway database, or imported using standard network exchange formats. Transcript, enzyme, and metabolite data can be presented in the context of their underlying networks, e. g. metabolic pathways or classification hierarchies. Visualization and navigation methods support the visual exploration of the data-enriched networks. Statistical methods allow analysis and comparison of multiple data sets such as different developmental stages or genetically different lines. Correlation networks can be automatically generated from the data and substances can be clustered according to similar behavior over time. As examples, metabolite profiling and enzyme activity data sets have been visualized in different metabolic maps, correlation networks have been generated and similar time patterns detected. Some relationships between different metabolites were discovered which are in close accordance with the literature.ConclusionVANTED greatly helps researchers in the analysis and interpretation of biochemical data, and thus is a useful tool for modern biological research. VANTED as a Java Web Start Application including a user guide and example data sets is available free of charge at .

Connecting Segments for Visual Data Exploration and Interactive Mining of Decision Rules

Visualization has become an essential support throughout the KDD pro- cess in order to extract hidden information from huge amount of data. Visual data exploration techniques provide the user with graphic views or metaphors that repres- ent potential patterns and data relationships. However, an only image does not always convey high-dimensional data properties successfully. From such data sets, visualiza- tion techniques have to deal with the curse of dimensionality in a critical way, as the number of examples may be very small with respect to the number of attributes. In this work, we describe a visual exploration technique that automatically extracts relevant attributes and displays their ranges of interest in order to support two data mining tasks: classification and feature selection. Through different metaphors with dynamic properties, the user can re-explore meaningful intervals belonging to the most relevant attributes, building decision rules and increasing the model accuracy interactively.

Extending distortion viewing from 2D to 3D

Addresses the visual exploration of 3D information layouts. Several visual exploration techniques have been proposed for 2D information layouts. Many of these try to take advantage of humans' natural visual pattern-recognition abilities to understand global relationships while simultaneously integrating this knowledge with local details. This desire for detail-in-context views (also called fisheye, multiscale and distortion views) has fueled considerable research in the development of distortion viewing tools. Generally, these tools provide space for magnification of local detail by compressing the rest of the image. In considering a possible detail-in-context view for 3D layouts, we first examine 2D distortion techniques, bearing in mind the particular 3D problem of occlusion. Comparing 2D and 3D information layout adjustment tools leads directly to a 3D visual access tool that clears a line of sight to any region of interest. While our technique can extend to any type of 3D information display, we focus on graphs.

Falloposcopy: A microendoscopic technique for visual exploration of the human fallopian tube from the uterotubal ostium to the fimbria using a transvaginal approach

International Journal of Gynecology & ObstetricsVolume 35, Issue 3 p. 292-293 Citations from the literature endoscopy Falloposcopy: A microendoscopic technique for visual exploration of the human fallopian tube from the uterotubal ostium to the fimbria using a transvaginal approach J Kerin, J KerinSearch for more papers by this authorL Daykhovsky, L DaykhovskySearch for more papers by this authorJ Segalowitz, J SegalowitzSearch for more papers by this authorE Surrey, E SurreySearch for more papers by this authorR Anderson, R AndersonSearch for more papers by this authorA Stein, A SteinSearch for more papers by this authorM Wade, M WadeSearch for more papers by this authorW Grundfest, W GrundfestSearch for more papers by this author J Kerin, J KerinSearch for more papers by this authorL Daykhovsky, L DaykhovskySearch for more papers by this authorJ Segalowitz, J SegalowitzSearch for more papers by this authorE Surrey, E SurreySearch for more papers by this authorR Anderson, R AndersonSearch for more papers by this authorA Stein, A SteinSearch for more papers by this authorM Wade, M WadeSearch for more papers by this authorW Grundfest, W GrundfestSearch for more papers by this author First published: July 1991 https://doi.org/10.1016/0020-7292(91)90346-7AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume35, Issue3July 1991Pages 292-293 RelatedInformation

Falloposcopy: a microendoscopic technique for visual exploration of the human fallopian tube from the uterotubal ostium to the fimbria using a transvaginal approach

A transvaginal microendoscopic technique has been developed for safely exploring the human fallopian tube from the utero tubal ostium to the fimbria and adjacent peritoneal cavity. Falloposcopy was performed without complication or evidence of endotubal damage in 44 women, 38 of whom also underwent a concurrent laparoscopy. Eight women with normal tubes served as controls and 36 women with tubal damage underwent falloposcopy in an attempt to document endotubal defects. Previous salpingectomy in 13 women and ostial obstruction in 4 cases left 71 tubes available for falloposcopy. Technical failures, defined as an inability to negotiate the tubal lumen in the absence of obstructive disease occurred in 8 of 71 (11%) procedures. In 63 successful procedures, the tubal lumen was considered to be falloposcopically normal in 28 cases (44%) and contained defects ranging from partial to total obstruction secondary to intraluminal fibrosis within the intramural, isthmic, and ampullary segments in the remaining 35 tubes (56%). Falloposcopy provides a nonincisional modality for defining the normal and abnormal surface anatomy of the tubal epithelium.