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Related Concept Videos

Development of Analytical Methods01:21

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An analytical methodology can be divided into four sequential steps: technique, method, procedure, and protocol. A technique is a scientific principle that rationalizes a specific phenomenon through chemical measurements. Adapting a technique for analyzing a sample of interest is termed a method. The procedure outlines the directions for performing the analysis via an analytical method. The protocol is the detailed guidelines on the procedure, which should be strictly followed to obtain the...
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Carl Jung, a Swiss psychiatrist and former follower of Freud, eventually broke away from Freud's ideas to create his framework, analytical psychology. This approach emphasizes achieving a balance between the conscious and unconscious aspects of the mind and reconciling various experiences within an individual's personality. Jung believed that this process, which typically unfolds in the latter part of life, involves an ongoing journey of recognizing and incorporating unconscious...
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Functional groups are groups of atoms with specific chemical properties that occur within organic molecules and are sometimes denoted as “R”. Functional groups can “functionalize” a compound by enabling it to adopt different physical and chemical properties.
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In an underdamped second-order system, where the damping ratio ζ is between 0 and 1, a unit-step input results in a transfer function that, when transformed using the inverse Laplace method, reveals the output response. The output exhibits a damped sinusoidal oscillation, and the difference between the input and output is termed the error signal. This error signal also demonstrates damped oscillatory behavior. Eventually, as the system reaches a steady state, the error diminishes to zero.
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Facilitating the Analysis of Immunological Data with Visual Analytic Techniques
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Commercial Visual Analytics Systems-Advances in the Big Data Analytics Field.

Michael Behrisch, Dirk Streeb, Florian Stoffel

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    |July 31, 2018
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    Summary

    Commercial Visual Analytics (VA) systems have matured, but innovation is declining as vendors prioritize user needs over research. This reevaluation assesses current VA tools, highlighting areas for future development in Big Data Analytics.

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    Area of Science:

    • Information Visualization
    • Visual Analytics
    • Data Science

    Background:

    • A significant gap exists between academic research and commercial offerings in Visual Analytics (VA).
    • The previous 2012 survey highlighted key trends in the VA field, influencing subsequent development.
    • The commercial VA landscape has evolved considerably over the past five years.

    Purpose of the Study:

    • To reevaluate the state of commercial Big Data Analytics systems five years after the initial survey.
    • To assess the maturity of the VA field and identify trade-offs between innovation and user-group satisfaction.
    • To provide a comprehensive overview of the commercial VA landscape, including new products and features.

    Main Methods:

    • Evaluation of new and existing commercial VA products using established criteria (features, performance, usability).
    • Introduction of novel evaluation metrics, including suitability for specific user groups and handling of complex data types.
    • Conducting a new case study to showcase innovative features within commercial VA systems.

    Main Results:

    • While the commercial VA field has matured, there's a noticeable decline in research-driven innovation.
    • Current systems often prioritize broad user group needs over specialized, cutting-edge capabilities.
    • New products and features are emerging, but their impact on addressing core VA challenges requires further investigation.

    Conclusions:

    • Commercial VA systems face a critical juncture, balancing widespread adoption with the need for continued innovation.
    • Future development roadmaps should prioritize advanced features, complex data handling, and tailored user group solutions.
    • Further research is needed to bridge the gap between academic advancements and commercial VA system capabilities.