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RWTH Aachen
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Dynamic Visual Analytics Pipeline Processing With WebAssembly

Thesis type
  • Bachelor
Status Open
Supervisor(s)
Advisor(s)

Within software development, analytical approaches are employed at various stages. For instance, self-reflection on developed artifacts plays an important role within open source communities on GitHub. Also, DevOps teams visualize their gathered analytics data in various ways, for example to identify usage patterns.

In a previous master thesis, we introduced the SWEVA app for Social Web-based Visual Analytics. It features an analytics pipeline editor, where data sources can be graphically linked to data processing nodes in a near real-time collaborative way. The pipeline is then instantiated in a collaborative visual analytics view, where diagrams are populated with the resulting processed data. Following the ideas of visual analytics, it allows tuning parameters. Data processing tasks, like normalization, standardizing, or outlier detection, can run on the client device or on a server. However, the security implications and threats are high, as potentially harmful user-defined code is executed within the browsers of collaboration peers. For this reason, we introduced SWEVAscript, a (syntactically) reduced variant of JavaScript, that can be instantiated in browser and Node.js environments. As we do not allow for instance array access over square brackets, usability is low, however.

The WebAssembly specification introduces an intermediary code representation format, that can be universally instantiated on various architectures and processors. Additionally, it is sandboxed by default, making it the ideal candidate for data processing tasks. Thus, in this thesis, we want to replace SWEVAscript with a new WebAssembly-based processing framework. To this end, SWEVA should be extended with a collaborative code editor, for instance with AssemblyScript, to create processing nodes. The processing nodes should be dynamically instantiated on client and server environments. For the evaluation, we want to compare data on execution parameters depending on execution location.

SWEVA Visual Analytics

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