Simple Web-Based Visual Analytics with SWeVA


Simple Web-Based Visual Analytics (SWeVa) is an extensible Web-based visual analytics platform that can be used for analyzing aspects of software development and usage inside a community [1], as well as to support learning analytics [2]. Supporting the Visual Analytics Mantra “Analyze First - Show the Important - Zoom, Filter, and Analyse Further Details on Demand” [3], SWeVA provides a browser-based interface for the graph-based composition of interactive data visualizations. Visualizations are generated following the cutting-edge Web component specifications and thus support simple and comfortable re-use in arbitrary Web page contexts. Another specialty of SWeVA is its support for real-time collaborative data visualization authoring, using the Yjs framework.


SWeVA Visual Analytics Work Flow

Figure 1 - SWeVA Visual Analytics Work Flow

In general, SWeVA follows the standard visual analytics work flow [4], depicted in Fig. 1. In order to guarantee an appropriate technical separation of concerns, SWeVA is divided into two major parts: a core framework and a collaborative visualization tool. The Core Framework follows the instructions in a data processing model to compute visualizable results for the Collaborative Visualization Tool. It works with a service-oriented architecture, where specialized remote services are orchestrated to request and process data. Additionally, it supports local computations that can be used for simple operations and to make the data compatible among the services. The orchestrated services generally do not know anything about each other, so the APIs might not be directly compatible regarding the used data structures.

SWeVA Collaborative Visualization Tool

Figure 2 - SWeVA Collaborative Visualization Tool - Authoring Canvas & Visualization

The Collaborative Visualization Tool is responsible for visualization, interaction and knowledge gain in the visual analytics process. Fig. 2 shows the user interface of the collaborative visualization tool. It supports the creation and customization of arbitrary visualization techniques. It therefore follows a modularized approach, where visualization techniques are not predefined, but loaded on demand. Many of the responsibilities relating the visualizations are held by visualization modules. They control how exactly the data is represented and rendered, how hypotheses are included in it and even what interactivity options users are offered. The process of rendering gives the foundation for the ability to export visualizations, which is a part of knowledge sharing. An important key concept of visual analytics is showing an initial overview and allowing the display of further details on demand. For the collaboration on visualization creation and use, SWeVA thus supports the following principles:

SWeVA Interactive Module Visualization

Figure 3 - SWeVA Interactive Module Visualization

Developers and Contributors


  1. L. Corral, A. Sillitti, G. Succi, J. Vlasenko, and A. I. Wasserman, Eds., in Open Source Software: Mobile Open Source Technologies, Berlin Heidelberg, 2014, vol. 427, pp. 11–20.
  2. M. Derntl, N. Günnemann, and R. Klamma, “A Dynamic Topic Model of Learning Analytics Research,” in LAK (Data Challenge), 2013.
  3. D. A. Keim, F. Mansmann, J. Schneidewind, J. Thomas, and H. Ziegler, “Visual Analytics: Scope and Challenges,” in Visual Data Mining, vol. 4404, S. J. Simoff, M. H. Böhlen, and A. Mazeika, Eds. Berlin and New York: Springer, 2008, pp. 76–90.
  4. D. A. Keim, J. Kohlhammer, G. Ellis, F. Mansmann, and D. Keim, Mastering the Information Age: Solving Problems with Visual Analytics. Goslar: Florian Mansmann and Eurographics Association, 2010.