54 Conclusions
does not give any particular suggestion regarding the actions that the user could take about his/her behavior. Future work could include an action recommen-dation system integrated with the visualization interfaces.
There is a significant challenge in understanding the ever-growing amount of digital information1,2. The conjunct usage of data mining and visualization techniques can be used to extract invaluable insights from such large amount of data, from financial trends to business intelligence [K+02]. The proposed visualizations could be generalized in order to be applied to several other fields:
• visualization of movement has application in personal movement tracking, animal migration, warehouse management
• visualization of periodic patterns has promising applications in business intelligence, event analysis and climate studies
• network visualization has application in social network analysis, network communication and graph analysis
8.2 Contributions 55
helped users to quantify several aspects of their behaviour, and that there is an interest in such personal informatics systems.
As part of the work on this thesis I have written two research papers together with my supervisors. One paper has been accepted for theCHI 2013 workshop and another paper is currently under review for theMobile HCI 2013conference.
The two papers are included in Appendix AandB.
56 Conclusions
Appendix A
A Mobile Personal Informatics System with Interactive Visualizations of Mobility and Social Interactions
Currently under review for theMobile HCI 2013 conference.
Authors: Cuttone, A., Lehmann, S. and Larsen, J.,
Abstract: In this paper we describe a personal informatics system for Android smartphones that provides feedback on personal mobility and social interactions through interactive visualization interfaces. The mobile app has been made available to N=136 first year university students as part of a study of social network interactions in a university campus setting. We describe the design of the interactive visualization interfaces enabling the participants to gain insights into own behaviours. We report initial findings based on device logging of par-ticipant interactions with the interactive visualization app on the smartphone and from a survey on usage with response from 45 (33%) of the participants.
58
A Mobile Personal Informatics System with Interactive Visualizations of Mobility and Social Interactions
Appendix B
QS Spiral: Visualizing Periodic Quantified Self Data
Accepted for the CHI 2013 workshop.
Authors: Larsen, J., Cuttone, A. and Lehmann, S.
Abstract: In this paper we propose an interactive visualization techniqueQS Spiralthat aims to capture the periodic properties of quantified self data and let the user explore those recurring patterns. The approach is based on time-series data visualized as a spiral structure. The interactivity includes the possibil-ity of varying the time span and the time frame shown, allowing for different levels of detail and the discoverability of repetitive patterns in the data on mul-tiple scales. We illustrate the capabilities of the visualization technique using two quantified self data sets involving self-tracking of geolocation and physical activity respectively.
QS Spiral: Visualizing Periodic Quantified Self Data
Jakob Eg Larsen Tech. University of Denmark Dept. of Applied Mathematics and Computer Science Matematiktorvet, Bld 303B 2800 Kgs. Lyngby, Denmark jel@imm.dtu.dk
Andrea Cuttone Tech. University of Denmark Dept. of Applied Mathematics and Computer Science Matematiktorvet, Bld 303B 2800 Kgs. Lyngby, Denmark andreacuttone@gmail.com
Sune Lehmann Tech. University of Denmark Dept. of Applied Mathematics and Computer Science Matematiktorvet, Bld 303B 2800 Kgs. Lyngby, Denmark slj@imm.dtu.dk
Copyright is held by the author/owner(s).
CHI’13, Apr 27–May 2, 2013, Paris, France.
Abstract
In this paper we propose an interactive visualization techniqueQS Spiralthat aims to capture the periodic properties of quantified self data and let the user explore those recurring patterns. The approach is based on time-series data visualized as a spiral structure. The interactivity includes the possibility of varying the time span and the time frame shown, allowing for different levels of detail and the discoverability of repetitive patterns in the data on multiple scales. We illustrate the capabilities of the visualization technique using two quantified self data sets involving self-tracking of geolocation and physical activity respectively.
Author Keywords
Quantified Self, Personal Informatics, Time-Series Data, Data Visualization, Periodic Events
ACM Classification Keywords
H.5.m [Information interfaces and presentation (e.g., HCI)]: Miscellaneous.
Introduction
Recently self-tracking has gained increased attention and uptake with the availability of smartphones and low cost
wearable devices, such as, FitBit1, Basis2, and Zeo3. These devices are gradually lowering the barrier for people to engage in self-tracking and lifelogging. As suggested by Li et al.[4], self-reflection is an essential part of personal informatics. Self-reflection can be facilitated through data visualization enabling the user to interact with the collected data to receive feedback on self-tracking and to be guided in understanding of behavioral patterns.
Different approaches to visualization of quantified self data has been proposed, ranging from traditional statistical charts to abstract art4.
Figure 1:The QS Spiral interactive visualization with color coded data points shown on a time spiral in a 7-day view.
Figure 2:In this configuration the time scale of the QS Spiral is shorter and more details are shown in a 24-hour view.
An inherent property of lifelog self-tracking data is the periodic properties of the data with patterns on different temporal scales including days, weeks, months, and years.
Moreover self-tracking data may potentially be characterized as multi-dimensional depending on the number of signals being tracked. Our motivation is to create an interactive visualization that captures these specific properties of quantified self data. Thus in this paper we propose the data visualization approachQS Spiralthat aims to capture both the continuity and periodic properties of quantified self data by introducing visualization of time-series data on a spiral, enabling the user to interact with and interpret the data through simple touch-based gestures.
Related Work
In visualization of lifelogging and quantified self time series data traditional charts have been the norm, but several other approaches have been proposed to enable easier and faster perception and exploration. For instance physical
1http://www.fitbit.com 2http://www.mybasis.com 3http://www.myzeo.com
4The work of Laurie Frick is a good example of art based on self-tracking data sets http://www.lauriefrick.com/
activity indicated by the growth of a flower on the FitBit wearable device, but also abstract visualizations or avatars have been proposed. While visualization of time-series data in a spiral structure has been proposed in [3], [5], and [6] visualization of quantified self data is novel. Although Spark[2] is an example where abstract representations of physical activity data include configurations with a spiral structure and concentric circles of data points, this visualization is fairly abstract and aimed as ambient art, and does not communicate details nor periodic events.
QS Spiral
Our proposed visualization technique is shown in Figure1 and2and builds on top of a clock-dial metaphor with a circle corresponding to a time span (hour, day, week, or year). The continuity of the time series is captured by the continuous spiral timeline, with the present in the outer ring, and the past towards the center. In the present configurations one full circle represents 24 hours or 7 days as shown, and the spiral is logarithmic with the distance between each turn decreasing in geometric progression.
This way the outer arcs are thicker, and become progressively thinner as they approach the center, so that most space is allocated for information in the outer ring, representing the most recent time frame. The start- and end time of each event are converted to angles and the corresponding arc is drawn on the spiral. Data points can be shown using different color coding of the timeline as well as symbols or even animations. Depending on the amount of information that needs to be embedded per time frame, the thickness of the timeline can be varied by zooming in or out. Thin lines optimizes the visualization for overview and discovery of patterns on longer time frames whereas thick lines optimizes the visualization for details on a shorter time frame. For navigational support an overlay with time of the day or day of the week is
added on top of the spiral.
Prototype
We have built a prototype of theQS Spiralvisualization for Android smartphones enabling us to experiment with touch-based interfaces as the basis for the interactive visualization. The single tap gesture allows the user to highlight specific data elements. A pinch gesture allow the user to zoom into a particular part of the data and swipe gestures allow to pan, resulting in a zoomable map metaphor. The double-tap gesture switches between different time views. The combination of these gestures allow to focus into the details of a specific period, or provide overview of a larger time frame.
Case Studies
Figure 3:A week view showing the geolocation data captured over a 4 month duration. A tag cloud of color-coded geolocations are shown at the top and a timeline of map segments at the bottom. Tapping the map segments will highlight those geolocations in the spiral.
To demonstrate the capabilities of the interactive visualization we have experimented with different types of quantified self data sets. Two data sets are used to illustrate the capabilities of visualizing and interacting with quantified self data, and in particular to discover periodic trends inherent in the data.
Geolocation Self-Tracking Data
In the first example we use geolocation data captured with sensing software on an Android smartphone[1]. The software continuously acquire and log data from the embedded location sensors (GPS and WiFi). From the location data, static locations are extracted according to movement speed and grouped into logical groups using the DBSCAN clustering algorithm. The data was captured over a duration of 4 months. A screenshot from the interactive visualization is shown in Figure3. In this interactive visualization the display is separated in three parts with theQS Spiralin the center. On the top is a tag cloud showing the most frequently visited places in
colors corresponding to the colors shown in the spiral.
The bottom of the display contains time series of places with map segments showing the places visited. When the user taps a visited place the corresponding places will be highlighted in the spiral.
Physical Activity Self-Tracking Data
The second example data set contains physical activity data captured using the FitBit step counter for a duration of 12 months. A screenshot from the interactive visualization is shown in Figure4. In this interactive visualization the display is separated in two parts with the spiral in the center showing the physical activity level measured in number of steps color coded with color intensity corresponding to the number of steps taken in that time frame, as indicated by the legend shown at the top.
Discussion
The interactive visualization facilitates exploratory data analysis and helps identify periodic patterns. In both cases we found that that periodic events and the daily/weekly rhythms were discoverable using theQS Spiral visualization, but also that the repetitive nature of the data makes deviations stand out clearly. The spiral visualization represents events with similar period aligned along similar angles of the arcs, resulting in aligned sections of the spiral. Moreover, the color coding of events highlights the relation between groups of events.
The combination of angle and color allows one to explore patterns of events ”at a glance”. As examples using the interactive visualization of geolocation data we discovered two distinct periodic patterns during (9am to 3pm and 7pm to 10pm). In addition patterns on Mondays and Wednesdays, and a pattern on Tuesdays and Fridays were found. Events with the same color belonging to the same
region but with different duration signal an irregularity in duration. Events not in the same region as others with the same color signal an irregularity in a periodic occurrence, and isolated events are easily discovered too.
Also the choice of the scale of period changes the perception of periodicity, where a period of 24 hours highlights patterns for events happening in the same time of the day, while a period of 7 days highlights events happening in the same day of the week. In the interactive visualization of physical activity repetitive patterns were found indicating lower activity levels during weekends compared to week days. Also higher activity levels were discovered in the last quarter of the year.
Figure 4:A four week view showing physical activity data captured over a 12 month duration, with the color density corresponding to the number of steps taken daily.
In the present data sets, the 00:00am–06:00am hour time period is almost empty meaning that a substantial part of the visualization area is unutilized. Integration of multiple data sets including geolocation, physical activity, sleep, calendar data and other personal information data sources with temporal properties could utilize the full space and allow the user to discover correlations between different quantified self data streams. To make quantified self data useful a large volume of data needs to be easily accessible, so that rhythms and progress can be monitored. An interactive visualization is essential to engage users by providing feedback at a glance and theQS Spiralis considered a compact representation that fits on a small accessible display such as a smartphone display or even a wearable/wrist-worn (self-tracking) device. Future work include experiments with additional self-tracking data sets (with different resolution) and variations of interface configurations (such as distortions and animations) and to further explore the design language to map touch gestures to navigation of the interactive visualization.
Conclusions
We have presentedQS Spiralas an approach to visualize and explore quantified self data with the aim for users to discover patterns and the periodic properties of the data.
The visualization has been demonstrated with geolocation and physical activity self-tracking data sets.
Acknowledgments
This work funded in part by the SensibleDTU project (The Villum Foundation).
References
[1] Aharony, N., Pan, W., Ip, C., Khayal, I., and Pentland, A. Social fmri: Investigating and shaping social mechanisms in the real world.Pervasive and Mobile Computing(2011).
[2] Fan, C., Forlizzi, J., and Dey, A. A spark of activity:
Exploring informative art as visualization for physical activity. InProc of the CHI2012 Workshop on Personal Informatics(2012).
[3] Hewagamage, K., Hirakawa, M., and Ichikawa, T.
Interactive visualization of spatiotemporal patterns using spirals on a geographical map. InVisual Languages, IEEE (1999), 296–303.
[4] Li, I., Dey, A., and Forlizzi, J. A stage-based model of personal informatics systems. InProc of the 28th int conf on Human factors in computing systems, ACM (2010), 557–566.
[5] Suntinger, M., Schiefer, J., Obweger, H., and Groller, M. The event tunnel: Interactive visualization of complex event streams for business process pattern analysis. InVisualization Symposium, IEEE (2008), 111–118.
[6] Weber, M., Alexa, M., and M¨uller, W. Visualizing time-series on spirals. InProceedings of the IEEE Symposium on Information Visualization(2001), 7.
64 QS Spiral: Visualizing Periodic Quantified Self Data
Bibliography
[AAG03] Natalia Andrienko, Gennady Andrienko, and Peter Gatalsky.
Exploratory spatio-temporal visualization: an analytical review.
Journal of Visual Languages & Computing, 14(6):503–541, 2003.
[ABK+07] Luis Otavio Alvares, Vania Bogorny, Bart Kuijpers, Jose MACEDO, Bart Moelans, and Alejandro Vaisman. A model for en-riching trajectories with semantic geographical information. 2007.
[ABL10] Yong-Yeol Ahn, James P Bagrow, and Sune Lehmann. Link communities reveal multiscale complexity in networks. Nature, 466(7307):761–764, 2010.
[APF+06] Balázs Adamcsek, Gergely Palla, Illés J Farkas, Imre Derényi, and Tamás Vicsek. Cfinder: locating cliques and overlapping modules in biological networks. Bioinformatics, 22(8):1021–1023, 2006.
[API+11] N. Aharony, W. Pan, C. Ip, I. Khayal, and A. Pentland. Social fmri:
Investigating and shaping social mechanisms in the real world. Per-vasive and Mobile Computing, 2011.
[ASC+09] Harith Alani, Martin Szomszor, Ciro Cattuto, Wouter Van den Broeck, Gianluca Correndo, and Alain Barrat. Live social seman-tics. The Semantic Web-ISWC 2009, pages 698–714, 2009.
[Beh97] John T Behrens. Principles and procedures of exploratory data analysis. Psychological Methods; Psychological Methods, 2(2):131, 1997.
66 BIBLIOGRAPHY
[BGLL08] Vincent D Blondel, Jean-Loup Guillaume, Renaud Lambiotte, and Etienne Lefebvre. Fast unfolding of communities in large net-works. Journal of Statistical Mechanics: Theory and Experiment, 2008(10):P10008, 2008.
[BMP+12] Daniel Boston, Steve Mardenfeld, Juan Susan Pan, Quentin Jones, Adriana Iamnitchi, and Cristian Borcea. Leveraging bluetooth co-location traces in group discovery algorithms. Pervasive and Mo-bile Computing, 2012.
[BT07] David Borland and Russell M Taylor. Rainbow color map (still) considered harmful. Computer Graphics and Applications, IEEE, 27(2):14–17, 2007.
[BZA12] Andrey Boytsov, Arkady Zaslavsky, and Zahraa Abdallah. Where have you been? using location clustering and context awareness to understand places of interest. Internet of Things, Smart Spaces, and Next Generation Networking, pages 51–62, 2012.
[CCJ10] Xin Cao, Gao Cong, and Christian S Jensen. Mining significant semantic locations from gps data. Proceedings of the VLDB En-dowment, 3(1-2):1009–1020, 2010.
[DH02] Pierre Dragicevic and Stéphane Huot. Spiraclock: a continuous and non-intrusive display for upcoming events. InCHI’02 extended abstracts on Human factors in computing systems, pages 604–605.
ACM, 2002.
[DP73] David H Douglas and Thomas K Peucker. Algorithms for the reduction of the number of points required to represent a digitized line or its caricature. Cartographica: The International Journal for Geographic Information and Geovisualization, 10(2):112–122, 1973.
[EKSX96] Martin Ester, Hans-Peter Kriegel, Jörg Sander, and Xiaowei Xu.
A density-based algorithm for discovering clusters in large spatial databases with noise. InProceedings of the 2nd International Con-ference on Knowledge Discovery and Data mining, volume 1996, pages 226–231. AAAI Press, 1996.
[EP06] Nathan Eagle and Alex Pentland. Reality mining: sensing complex social systems. Personal and Ubiquitous Computing, 10(4):255–
268, 2006.
[FFD12] Chloe Fan, Jodi Forlizzi, and Anind Dey. A spark of activity: Ex-ploring informative art as visualization for physical activity. 2012.
BIBLIOGRAPHY 67
[Fre04] Linton C Freeman. The development of social network analysis:
A study in the sociology of science, volume 1. Empirical Press Vancouver, 2004.
[Fry07] Ben Fry. Visualizing Data: Exploring and Explaining Data with the Processing Environment. O’Reilly Media, Incorporated, 2007.
[GFC05] Mohammad Ghoniem, Jean-Daniel Fekete, and Philippe Castagli-ola. On the readability of graphs using node-link and matrix-based representations: a controlled experiment and statistical analysis.
Information Visualization, 4(2):114–135, 2005.
[HB03] Mark Harrower and Cynthia Brewer. Colorbrewer. org: an online tool for selecting colour schemes for maps. Cartographic Journal, The, 40(1):27–37, 2003.
[HB05] Jeffrey Heer and Danah Boyd. Vizster: Visualizing online social networks. In Information Visualization, 2005. INFOVIS 2005.
IEEE Symposium on, pages 32–39. IEEE, 2005.
[HFM07] Nathalie Henry, J-D Fekete, and Michael J McGuffin. Nodetrix:
a hybrid visualization of social networks. Visualization and Com-puter Graphics, IEEE Transactions on, 13(6):1302–1309, 2007.
[HHI99] K.P. Hewagamage, M. Hirakawa, and T. Ichikawa. Interactive visu-alization of spatiotemporal patterns using spirals on a geographical map. InVisual Languages, pages 296–303. IEEE, 1999.
[HSL] Theus Hossmann, Thrasyvoulos Spyropoulos, and Franck Legen-dre. From wireless contacts to community structures.
[IdKM+06] Wijnand IJsselsteijn, Yvonne de Kort, Cees Midden, Berry Eggen, and Elise van den Hoven. Persuasive technology for human well-being: setting the scene. Persuasive technology, pages 1–5, 2006.
[ISB+11] Lorenzo Isella, Juliette Stehlé, Alain Barrat, Ciro Cattuto, Jean-François Pinton, and Wouter Van den Broeck. What’s in a crowd?
analysis of face-to-face behavioral networks. Journal of theoretical biology, 271(1):166–180, 2011.
[JGO06] Jungwook Jun, Randall Guensler, and Jennifer H Ogle. Smoothing methods to minimize impact of global positioning system random error on travel distance, speed, and acceleration profile estimates.
Transportation Research Record: Journal of the Transportation Re-search Board, 1972(-1):141–150, 2006.
68 BIBLIOGRAPHY
[JLJ+10] Bjørn Sand Jensen, Jakob Eg Larsen, Kristian Jensen, Jan Larsen, and Lars Kai Hansen. Estimating human predictability from mobile sensor data. In Machine Learning for Signal Processing (MLSP), 2010 IEEE International Workshop on, pages 196–201.
IEEE, 2010.
[K+02] Daniel A Keim et al. Information visualization and visual data min-ing. IEEE transactions on Visualization and Computer Graphics, 8(1):1–8, 2002.
[LDF10] Ian Li, Anind Dey, and Jodi Forlizzi. A stage-based model of personal informatics systems. InProceedings of the 28th interna-tional conference on Human factors in computing systems, pages 557–566. ACM, 2010.
[LDF11] Ian Li, Anind K Dey, and Jodi Forlizzi. Understanding my data, myself: supporting self-reflection with ubicomp technologies. In Proceedings of the 13th international conference on Ubiquitous computing, pages 405–414. ACM, 2011.
[LF09] Andrea Lancichinetti and Santo Fortunato. Community detec-tion algorithms: a comparative analysis. Physical Review E, 80(5):056117, 2009.
[LML+06] James Lin, Lena Mamykina, Silvia Lindtner, Gregory Delajoux, and Henry Strub. Fish’n’steps: Encouraging physical activity with an interactive computer game. UbiComp 2006: Ubiquitous Com-puting, pages 261–278, 2006.
[LN08] Elizabeth A Leicht and Mark EJ Newman. Community structure in directed networks. Physical Review Letters, 100(11):118703, 2008.
[LPDR06] Jamie Lawrence, Terry R Payne, and David De Roure. Co-presence communities: Using pervasive computing to support weak social networks. In Enabling Technologies: Infrastructure for Collabo-rative Enterprises, 2006. WETICE’06. 15th IEEE International Workshops on, pages 149–156. IEEE, 2006.
[MCLP10] Anmol Madan, Manuel Cebrian, David Lazer, and Alex Pentland.
Social sensing for epidemiological behavior change. InProceedings of the 12th ACM international conference on Ubiquitous comput-ing, pages 291–300. ACM, 2010.
[MFGPP11] Anmol Madan, Katayoun Farrahi, Daniel Gatica-Perez, and Alex Pentland. Pervasive sensing to model political opinions in face-to-face networks. Pervasive Computing, pages 214–231, 2011.
BIBLIOGRAPHY 69
[New04] Mark EJ Newman. Fast algorithm for detecting community struc-ture in networks. Physical Review E, 69(6):066133, 2004.
[Nor06] Chris North. Toward measuring visualization insight. Computer Graphics and Applications, IEEE, 26(3):6–9, 2006.
[OGB+02] Jennifer Ogle, Randall Guensler, William Bachman, Maxim Kout-sak, and Jean Wolf. Accuracy of global positioning system for de-termining driver performance parameters.Transportation Research Record: Journal of the Transportation Research Board, 1818(-1):12–24, 2002.
[PBKA08] Andrey Tietbohl Palma, Vania Bogorny, Bart Kuijpers, and Luis Otavio Alvares. A clustering-based approach for discovering interesting places in trajectories. InProceedings of the 2008 ACM symposium on Applied computing, pages 863–868. ACM, 2008.
[Pla04] Catherine Plaisant. The challenge of information visualization eval-uation. In Proceedings of the working conference on Advanced vi-sual interfaces, pages 109–116. ACM, 2004.
[PS06] Adam Perer and Ben Shneiderman. Balancing systematic and flex-ible exploration of social networks. Visualization and Computer Graphics, IEEE Transactions on, 12(5):693–700, 2006.
[PSM07] Zachary Pousman, John T Stasko, and Michael Mateas. Ca-sual information viCa-sualization: Depictions of data in everyday life. Visualization and Computer Graphics, IEEE Transactions on, 13(6):1145–1152, 2007.
[PTH+06] Jukka Perkio, Ville Tuulos, Marion Hermersdorf, Heli Nyholm, Jukka Salminen, and Henry Tirri. Utilizing rich bluetooth envi-ronments for identity prediction and exploring social networks as techniques for ubiquitous computing. In Web Intelligence, 2006.
WI 2006. IEEE/WIC/ACM International Conference on, pages 137–144. IEEE, 2006.
[PXYH05] Doantam Phan, Ling Xiao, Ron Yeh, and Pat Hanrahan. Flow map layout. In Information Visualization, 2005. INFOVIS 2005.
IEEE Symposium on, pages 219–224. IEEE, 2005.
[Rae09] Alasdair Rae. From spatial interaction data to spatial interaction information? geovisualisation and spatial structures of migration from the 2001 uk census. Computers, Environment and Urban Systems, 33(3):161–178, 2009.
70 BIBLIOGRAPHY
[RFF+08] George Robertson, Roland Fernandez, Danyel Fisher, Bongshin Lee, and John Stasko. Effectiveness of animation in trend visual-ization.Visualization and Computer Graphics, IEEE Transactions on, 14(6):1325–1332, 2008.
[Rog06] Yvonne Rogers. Moving on from weiser’s vision of calm comput-ing: Engaging ubicomp experiences. UbiComp 2006: Ubiquitous Computing, pages 404–421, 2006.
[RSH00] Johan Redstrom, Tobias Skog, and Lars Hallnas. Informative art:
using amplified artworks as information displays. In Designing Augmented Reality Environments: Proceedings of DARE 2000 on Designing augmented reality environments: Elsinore, Denmark, volume 2000, pages 103–114, 2000.
[SA09] Nadine Schuessler and Kay W Axhausen. Processing raw data from global positioning systems without additional information. Trans-portation Research Record: Journal of the TransTrans-portation Research Board, 2105(-1):28–36, 2009.
[Shn96] Ben Shneiderman. The eyes have it: A task by data type tax-onomy for information visualizations. InVisual Languages, 1996.
Proceedings., IEEE Symposium on, pages 336–343. IEEE, 1996.
[Shn01] Ben Shneiderman. Inventing discovery tools: Combining informa-tion visualizainforma-tion with data mining. In Discovery Science, pages 17–28. Springer, 2001.
[SQBB10] Chaoming Song, Zehui Qu, Nicholas Blumm, and Albert-László Barabási. Limits of predictability in human mobility. Science, 327(5968):1018–1021, 2010.
[SSOG08] M. Suntinger, J. Schiefer, H. Obweger, and M.E. Groller. The event tunnel: Interactive visualization of complex event streams for business process pattern analysis. InVisualization Symposium, pages 111–118. IEEE, 2008.
[Tid10] Jenifer Tidwell. Designing interfaces. O’Reilly Media, Incorpo-rated, 2010.
[WAM01] M. Weber, M. Alexa, and W. Müller. Visualizing time-series on spirals. In Proceedings of the IEEE Symposium on Information Visualization, page 7, 2001.
[WDSC07] Jo Wood, Jason Dykes, Aidan Slingsby, and Keith Clarke. Inter-active visual exploration of a large spatio-temporal dataset: reflec-tions on a geovisualization mashup. Visualization and Computer Graphics, IEEE Transactions on, 13(6):1176–1183, 2007.