An Incremental Approach for Real-Time Big Data Visual Analytics

García, Ignacio; Casado, Rubén; Bouchachia, Abdelhamid

doi:10.1109/w-ficloud.2016.46

Cited by 3 publications

(4 citation statements)

References 21 publications

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“…• Indefinite computations: These are computations having no defined end by design, such as visualizations for monitoring transient data streams [29][30][31].…”

Section: Reasons For Using Pvamentioning

confidence: 99%

A Review and Characterization of Progressive Visual Analytics

et al. 2018

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Progressive Visual Analytics (PVA) has gained increasing attention over the past years. It brings the user into the loop during otherwise long-running and non-transparent computations by producing intermediate partial results. These partial results can be shown to the user for early and continuous interaction with the emerging end result even while it is still being computed. Yet as clear-cut as this fundamental idea seems, the existing body of literature puts forth various interpretations and instantiations that have created a research domain of competing terms, various definitions, as well as long lists of practical requirements and design guidelines spread across different scientific communities. This makes it more and more difficult to get a succinct understanding of PVA's principal concepts, let alone an overview of this increasingly diverging field. The review and discussion of PVA presented in this paper address these issues and provide (1) a literature collection on this topic, (2) a conceptual characterization of PVA, as well as (3) a consolidated set of practical recommendations for implementing and using PVA-based visual analytics solutions.Keywords: visual analytics; progressive visualization; incremental visualization; online algorithms MotivationWith data growing in size and complexity, and analysis methods getting more sophisticated and computationally intensive, the idea of Progressive Visual Analytics (PVA) [1,2] becomes increasingly appealing. A PVA approach can either subdivide the data to process each data chunk individually, or it can subdivide the analytic process into computational steps that iteratively refine analytic results [3]. By doing so, PVA yields partial results of increasing completeness or approximative results of increasing correctness, respectively. This is useful in a wide range of visual analytics scenarios:• to realize responsive client-server visualizations using incremental data transmissions Because of its versatility, the progressive approach to data analysis and visualization is alternatively seen as a paradigm for computation, for interaction, for data transmission, or for visual presentation. It is thus not surprising that PVA-related research is distributed over multiple disciplines, motivated by various underlying problems, described in different, sometimes overloaded terms at different levels of detail for different audiences.

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“…• Indefinite computations: These are computations having no defined end by design, such as visualizations for monitoring transient data streams [29][30][31].…”

Section: Reasons For Using Pvamentioning

confidence: 99%

A Review and Characterization of Progressive Visual Analytics

et al. 2018

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“…In some cases, an additional V could be added to form the 4th V, value. The value refers to the quality of data and its impact (García, Casado, & Bouchachia, 2016). Moreover, a very important characteristic of Big Data is its repeated patterns and trends over time and space (Simpao et al, 2014).…”

Section: Ehrs and Big Datamentioning

confidence: 99%

“…Moreover, the system presents a single evolution pathway and does not support complex ones (Perer & Sun, 2012). Another interesting approach was described by García et al (2016) for real-time interactive visualization. This approach seeks a high efficiency in response time and a better performance compared to existing solutions by incrementally visualizing data.…”

Section: Medical Visualization Tools and Related Studiesmentioning

confidence: 99%

“…In other words, data will be processed gradually not all at once which allow users to get approximate results directly rather than waiting for the whole data to be processed to see an output (García et al, 2016). However, this approach provides an approximate result that varies over time with any data change.…”

Section: Medical Visualization Tools and Related Studiesmentioning

confidence: 99%

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Unsupervised Feature-Based Visualization Tool Applied on Medical Data. (c2020)

Moufarrej¹

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In a time when data is experiencing a remarkable growth in different fields, extracting and understanding the correlations between different data features is becoming increasingly important in many application areas, ranging over business, demographics, politics, and medicine, among others. The proper exploitation of such data offers great challenges in terms of data analysis and visualization, in order to allow effective and efficient decision-making. The problem is further aggravated on the Web where the data is often loosely structured and multi-featured. In this context, interactive data visualization has been recently suggested as a promising solution to facilitate data analysis and help in unveiling patterns, trends, and anomalies in the data. In this research, we present a new unsupervised feature-based tool for data visualization titled “mirrored dendrograms”. Our tool accepts as input semi-structured and multi-featured data, and allows the user to select the target features to be visualized and mapped against each other, as well as their relative impacts (weights) on the visualization process. It then invokes a hierarchical clustering process to cluster the data following the user-chosen features, and produces a dendrogram structure for each combination of target features. The dendrograms are then mirrored against each other by mapping the internal nodes of one dendrogram against the other dendrogram’s nodes, in order to highlight their structure correlation. Different from existing solutions like parallel coordinates and heatmap dendrograms, our work offers three main contributions: (i) connecting the dendrograms through their internal nodes (instead of connecting their leaf nodes like with heatmap dendrograms and tangelgrams) providing a visual representation of the relationships between the data structures, (ii) allowing to zoom-in and out the data to show their relationships at different granularity levels (compared with existing static solutions which do not allow varying granularities), and (iii) identifying the best zooming level between two dendrograms highlighting the maximum correlation (similarity) with the minimal amount of details (granularity) presented to the user (based on the intuition that users wish to acquire the most value out of the data, while viewing the least amount of data, i.e., with the least amount of effort). We have conducted a preliminary evaluation of our solution, using a sample dataset of 197 Electronic Health Records (EHRs) obtained from a private medical clinic, where all EHRs were vetted by a medical doctor from LAU Rizk hospital. The study was focused on the migraine headache disorder, where multiple patient data samples were mapped and visualized against each other. A number of 19 testers volunteered to participate in an online survey to help review and evaluate the data visualizations produced by our new tool, compared with existing solutions. Initial results are promising and highlight the potential our tool.

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BiVis: Interactive and Progressive Visualization of Billions (and Counting) Items

Blanch

2023

Lecture Notes in Computer Science

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An Incremental Approach for Real-Time Big Data Visual Analytics

Cited by 3 publications

References 21 publications

A Review and Characterization of Progressive Visual Analytics

A Review and Characterization of Progressive Visual Analytics

Unsupervised Feature-Based Visualization Tool Applied on Medical Data. (c2020)

BiVis: Interactive and Progressive Visualization of Billions (and Counting) Items

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