Visual mining of time series using a tubular visualization

Bouali, Fatma; Devaux, Sébastien; Venturini, Gilles

doi:10.1007/s00371-014-1052-0

Cited by 6 publications

(4 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In our recent work [4], we also showed that simple navigation modes can be proposed to the users, thus avoiding the complex problem of 3D navigation. We also underlined that this 3D representation has no occlusion thanks to the tubular shape: it thus resolve another problem in 3D visualizations.…”

Section: B Tubular Representations For Time-dependent Datamentioning

confidence: 96%

“…Recently, we extended DataTube to DataTube2, with various visualization modes and interactions that are devoted to sequence mining [4]. However, DataTube2 is not adapted to the mining of software logs, because it does not represent important information such as the messages or the synchronization between threads or other software objects.…”

Section: Introductionmentioning

confidence: 98%

“…So this paper focuses on the visual and interactive methods that can be used to mine timedependent data [1], and more precisely, on the methods that can represent log data that were generated by multi-threaded software [2] [3]. In our work on the visual exploration of time series [4], we are studying and extending a visual approach for temporal data mining that is called DataTube [5]. This initial visualization has received little attention up to now and seems to be promising to address our issues.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

DataTube4log: A Visual Tool for Mining Multi-threaded Software Logs

Devaux

Bouali

Venturini

2014

2014 18th International Conference on Information Visualisation

View full text Add to dashboard Cite

In this paper we study a 3D tubular visualization of software activity log data, with the aim of supporting multithreaded software development and debugging. We consider an existing visualization called Datatube2 that has already been used to help various domain experts in the analysis of large amounts of time-dependent data. Since software logs are also time series, DataTube2 has been enhanced to support the specific data and tasks that are currently found in debugging, yielding to a specific visualization that we called DataTube4log. In this visualization, each line in the tube is devoted to the activity of a thread. Dependencies between threads are materialized with arrows. Synchronization objects are also represented. Using a real dataset, we show how a domain expert has solved a debugging problem. In this experiment, we found that DataTube4log can be easily learned and adopted, and that the ability of the tube to efficiently render an overview of large time series was beneficial to the software engineer.

show abstract

Section: B Tubular Representations For Time-dependent Datamentioning

confidence: 96%

Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

DataTube4log: A Visual Tool for Mining Multi-threaded Software Logs

Devaux

Bouali

Venturini

2014

2014 18th International Conference on Information Visualisation

View full text Add to dashboard Cite

show abstract

“…Alternative spatial layouts can reveal periodic structure [39] and accentuate interesting data regions [15]. Visualizations have been applied to the result of pattern discovery algorithms, including wavelet analysis [18], cluster analysis across multiple time scales [40], motif discovery with augmented suffix trees [24], tabular views [6] and frequently occurring patterns using k-means clustering [16]. We find no work on the interactive classification of time series data.…”

Section: Visual Pattern Discovery In Time Seriesmentioning

confidence: 99%

TimeClassifier: a visual analytic system for the classification of multi-dimensional time series data

et al. 2015

View full text Add to dashboard Cite

Biologists studying animals in their natural environment are increasingly using sensors such as accelerometers in animal-attached 'smart' tags because it is widely acknowledged that this approach can enhance the understanding of ecological and behavioural processes. The potential of such tags is tempered by the difficulty of extracting animal behaviour from the sensors which is currently primarily dependent on the manual inspection of multiple time Electronic supplementary material The online version of this article (series graphs. This is time consuming and error-prone for the domain expert and is now the limiting factor for realising the value of tags in this area. We introduce TimeClassifier, a visual analytic system for the classification of time series data for movement ecologists. We deploy our system with biologists and report two real-world case studies of its use.

show abstract