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Facilitating the development of Analytical Dashboards on the Web

  • Author(s): Zarifis, Konstantinos;
  • Advisor(s): Papakonstantinou, Yannis;
  • Griswold, William G
  • et al.

Developing custom, reactive dashboards that deliver continuously updated visualizations,

has been a traditionally laborious task, due to the advanced technical expertise they require in

web development. MVVM frameworks have attempted to simplify this process, by offering a

template language that enables the declarative specification of dashboards, and by automatically

maintaining the displayed visualizations when the visualized data change. These frameworks,

however, still exhibit significant drawbacks. When mutations occur in underlying data sources,

developers have to observe and manually propagate (using imperative code) said mutations to

the framework, which still requires advanced skill-set and experience in web development. Additionally,

even though these frameworks automatically mutate the dependent visualizations (thus

absolving developers from manually performing this task), once mutations have been propagated

to the framework, their incremental rendering algorithms incur significant performance penalties,

especially when visualizing big data.

To address these issues, we present FORWARD, a framework that simplifies the development

of custom live dashboards. FORWARD offers a declarative template language

that simplifies the integration of (a) database and web service systems (such as Postgres and

GraphQL), (b) visualization libraries (such as Google Maps and HighCharts), and (c) data

processing functions (performing aggregations, ML computations, and more), thus enabling a

truly declarative specification of dashboards. This significantly lowers the technical expertise

needed for dashboard development, thus allowing programmers with limited experience in web

development (such as data analysts), to produce custom, information-dense, highly-reactive

dashboards. FORWARD templates describe dashboards as semi-structured views. As a result,

when mutations occur in the base-data of such views, FORWARD employs novel incremental

view maintenance techniques, that automatically propagate changes from data sources all the

way to the visual layer more efficiently than existing frameworks.

In this Thesis, we illustrate FORWARD’s template language and incremental rendering

algorithms, and show their superior algorithmic complexity compared to the state of the art.

Experimental results validate the complexity results and show that FORWARD’s incremental

rendering can be orders of magnitude more efficient than existing approaches. Line-of-code and

development time experiments show that the performance gains are accompanied by productivity

gains for developers that use FORWARD to build information-dense dashboards. Lastly, we

present ViDeTTe, a system that integrates FORWARD with Jupyter notebooks, and enables data

analysts to build Jupyter Notebooks with reactive visualizations. Notebook readers can then

directly interact with these visualizations to further explore the underlying data, a functionality

that is currently not supported in Jupyter Notebooks.

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