Temporal Data Analytics

The project aims at developing solutions for analyzing temporal data. Temporal data in this context is not only referred to point timestamped data, for instance time series, but also interval timestamped data, i.e. series where the time measure is an interval.

This project is partially funded by the research grants VCTP - IN2059 and ISMarD - WW200N of Free University of the Bozen-Bolzano.



Visor screenshot VISOR helps the user to explore data and their summary structures by visualizing the relationships between the size k of a data summary and the induced error. Given an ordered dataset, VISOR allows to vary the size k of a data summary and to immediately see the effect on the induced error, by visualizing the error and its dependency on k in an ε-graph and Δ-graph, respectively. The user can easily explore different values of k and determine the best value for the summary size. VISOR allows also to compare different summarization methods, such as piecewise constant approximation, piecewise aggregation approximation or V-optimal histograms.

Download VISOR source code.


HotPeriods screenshot With the ever increasing amount and complexity of data, visual analysis becomes a fundamental tool to spot correlations and other relationships in data. Most techniques (e.g., scatter plots or heatmaps) focus on point data, i.e., data with point measures, such as prices or volumes. We focus instead on data with interval measures, that is data where measures consist of an interval or range of values, such as price ranges or time intervals. HOTPERIODS allows to visualize correlations between two interval measures in the two-dimensional space, where the two measures represent a rectangle. To visualize such data, we first perform a rectangle aggregation. The result of this aggregation is a density matrix, where each cell stores the number of rectangles that cover the corresponding points in space. For the visualization of the density matrix, color-coding is used to represent different density values similar to heatmaps.

An online version of the prototype can be found at http://archimedes.inf.unibz.it:8080/hotperiods


  • Johann Gamper
  • Anton Dignös
  • Necati Duran
  • Giovanni Mahlknecht


Necati DuranGiovanni Mahlknecht, Anton Dignös, Johann Gamper:
HOTPERIODS: Visual Correlation Analysis of Interval Data. SSTD 2019: 178-181.
Giovanni Mahlknecht, Michael H. Böhlen, Anton Dignös, Johann Gamper:
VISOR: Visualizing Summaries of Ordered Data. SSDBM 2017: 40:1-40:5.

Free University of Bozen-Bolzano
Faculty of Computer Science
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Design by Riccardo Di Curti as an intern of our group.
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