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ModViz : A Modular and Extensible Architecture for Drill-Down and Visualization of Complex Data

Part of the Communications in Computer and Information Science book series (CCIS,volume 1598)

Abstract

Analysis of data sets that may be changing often or in real-time, consists of at least three important synchronized components: i) figuring out what to infer (objectives), ii) analysis or computation of those objectives, and iii) understanding of the results which may require drill-down and/or visualization. There is considerable research on the first two of the above components whereas understanding actionable inferences through visualization has not been addressed properly. Visualization is an important step towards both understanding (especially by non-experts) and inferring the actions that need to be taken. As an example, for Covid-19, knowing regions (say, at the county or state level) that have seen a spike or are prone to a spike in the near future may warrant additional actions with respect to gatherings, business opening hours, etc. This paper focuses on a modular and extensible architecture for visualization of base as well as analyzed data.

This paper proposes a modular architecture of a dashboard for user interaction, visualization management, and support for complex analysis of base data. The contributions of this paper are: i) extensibility of the architecture providing flexibility to add additional analysis, visualizations, and user interactions without changing the workflow, ii) decoupling of the functional modules to ease and speed up development by different groups, and iii) supporting concurrent users and addressing efficiency issues for display response time. This paper uses Multilayer Networks (or MLNs) for analysis.

To showcase the above, we present the architecture of a visualization dashboard, termed CoWiz++ (for Covid Wizard), and elaborate on how web-based user interaction and display components are interfaced seamlessly with the back-end modules.

Keywords

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Notes

  1. 1.

    We focus on the USA as we have more accurate data for that although the pandemic is worldwide! Any country can be analyzed by swapping the data sets and with minor changes, such as prefectures in Japan instead of states.

  2. 2.

    A Multilayer Network is a set of networks (each network termed a layer) where nodes within a layer are connected by intra-layer edges and nodes between two layers can be optionally connected using inter-layer edges.

  3. 3.

    Dashboard [24]: https://itlab.uta.edu/cowiz/, Youtube Videos: https://youtu.be/4vJ56FYBSCg, https://youtu.be/V_w0QeyIB5s. Readers are encouraged to play with the dashboard and watch the videos.

  4. 4.

    Currently, an in-memory hash table is used for quick lookup. If this hash table size exceeds available memory, this can be changed to a disk-based alternative (extendible hash or B+ tree) without affecting any other module. In this case, disk-based, pre-fetching and/or other buffer management strategies can be used to improve response time. Separate hash tables are used for different visualizations for scalability. Also, hash tables are written as binary objects and reloaded avoiding re-construction time.

  5. 5.

    Any analysis approach and associated model can be used. We are using the Multilayer Network (MLN) model proposed in [25, 28] for this dashboard. This also validates our assertion of the applicability of MLN model for complex analysis of real-world applications.

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Acknowledgements

This work has been partly supported by NSF Grant CCF-1955798 and CNS-2120393.

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Correspondence to Sharma Chakravarthy .

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Rademacher, D., Valdez, J., Memeti, E., Samant, K., Santra, A., Chakravarthy, S. (2022). ModViz : A Modular and Extensible Architecture for Drill-Down and Visualization of Complex Data . In: Ivanovic, M., Kirikova, M., Niedrite, L. (eds) Digital Business and Intelligent Systems. Baltic DB&IS 2022. Communications in Computer and Information Science, vol 1598. Springer, Cham. https://doi.org/10.1007/978-3-031-09850-5_16

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