Abstract
When users interact with large data through a visualization system, its response time is crucial in keeping engagement and efficacy as high as possible, and latencies as low as 500 ms can be detrimental to the correct execution of the analysis. This can be due to several causes: (i) for large data or high query rates, database management systems (DBMS) may fail to meet the performance needs; (ii) modeling all the interactions with a visualization system is challenging due to their exploratory nature, where not all of them are equally demanding in terms of computation time; (iii) there is a lack of models for integrating optimizations in a holistic way, hampering consistent evaluation across systems. In response to these problems, we propose a conceptual interaction-driven framework that enhances the visualization pipeline by adding a new Translation layer between the Data-, Visualization- and Interaction- layers, leveraging the modeling of interactions with augmented statecharts. This new layer aims to collect information about queries and rendering computations, linking such values to interactions in the statechart. To make the Translation layer actionable, we contribute a software component to automatically model the user interactions for a generic web-based visualization system through augmented statecharts, in which each interaction is labeled with its latency threshold. We first demonstrate its generality on ten state-of-the-art visualization systems. Then we perform a user study (n = 50), collecting traces by asking users to perform already established exploratory tasks on the well-known Crossfilter interface. Finally, we replay those traces over its generated statechart, assessing the capability to model the user interactions correctly and describing violations in the latency thresholds.
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Ali, S.M., Gupta, N., Nayak, G.K., Lenka, R.K.: Big data visualization: tools and challenges. In: 2016 2nd International Conference on Contemporary Computing and Informatics (IC3I), pp. 656–660 (2016). https://doi.org/10.1109/IC3I.2016.7918044
Amar, R.A., Eagan, J., Stasko, J.T.: Low-level components of analytic activity in information visualization, pp. 111–117. IEEE (2005). https://doi.org/10.1109/INFVIS.2005.1532136
Angelini, M., Blasilli, G., Farina, L., Lenti, S., Santucci, G.: Nemesis (network medicine analysis): Towards visual exploration of network medicine data (2019)
Angelini, M., Blasilli, G., Lenti, S., Palleschi, A., Santucci, G.: Towards enhancing radviz analysis and interpretation. In: 2019 IEEE Visualization Conference (VIS), pp. 226–230 (2019). https://doi.org/10.1109/VISUAL.2019.8933775
Angelini, M., Blasilli, G., Lenti, S., Palleschi, A., Santucci, G.: CrossWidgets: enhancing complex data selections through modular multi attribute selectors (2020). https://doi.org/10.1145/3399715.3399918
Angelini, M., Blasilli, G., Lenti, S., Santucci, G.: STEIN: speeding up evaluation activities with a seamless testing environment INtegrator. In: Johansson, J., Sadlo, F., Schreck, T. (eds.) EuroVis 2018 - Short Papers. The Eurographics Association (2018). https://doi.org/10.2312/eurovisshort.20181083
Angelini, M., Blasilli, G., Lenti, S., Santucci, G.: A visual analytics conceptual framework for explorable and steerable partial dependence analysis. IEEE Trans. Visual. Comput. Graph. 1–16 (2023)
Angelini, M., Catarci, T., Santucci, G.: Ivan: an interactive Herlofson’s nomogram visualizer for local weather forecast. Computers 8(3) (2019). https://doi.org/10.3390/computers8030053
Angelini, M., Lenti, S., Santucci, G.: Crumbs: a cyber security framework browser. In: 2017 IEEE Symposium on Visualization for Cyber Security (VizSec), pp. 1–8 (2017). https://doi.org/10.1109/VIZSEC.2017.8062194
Angelini, M., Santucci, G., Schumann, H., Schulz, H.J.: A review and characterization of progressive visual analytics. Informatics 5(3) (2018). https://doi.org/10.3390/informatics5030031
Baeza-Yates, R., Ribeiro-Neto, B., et al.: Modern Information Retrieval, vol. 463. ACM Press, New York (1999)
Battle, L., et al.: Database benchmarking for supporting real-time interactive querying of large data. In: SIGMOD ’20, New York, NY, USA, pp. 1571–1587, June 2020. https://doi.org/10.1145/3318464.3389732
Benvenuti, D., Buda, E., Fraioli, F., Marrella, A., Catarci, T.: Detecting and explaining usability issues of consumer electronic products. In: Ardito, C., et al. (eds.) INTERACT 2021. LNCS, vol. 12935, pp. 298–319. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-85610-6_18
Bigelow, A., Nobre, C., Meyer, M., Lex, A.: Origraph: interactive network wrangling. In: 2019 IEEE Conference on Visual Analytics Science and Technology (VAST), pp. 81–92. IEEE (2019)
Blanch, R., Beaudouin-Lafon, M.: Programming rich interactions using the hierarchical state machine toolkit. In: AVI ’06, pp. 51–58. ACM, New York, NY, USA (2006). https://doi.org/10.1145/1133265.1133275
Bostock, M., Ogievetsky, V., Heer, J.: D\(^3\) data-driven documents. IEEE Trans. Vis. Comput. Graph. 17(12), 2301–2309 (2011). https://doi.org/10.1109/TVCG.2011.185
Brehmer, M., Munzner, T.: A multi-level typology of abstract visualization tasks. IEEE Trans. Visual Comput. Graph. 19(12), 2376–2385 (2013). https://doi.org/10.1109/TVCG.2013.124
Card, S., Mackinlay, J.: The structure of the information visualization design space. In: Proceedings of VIZ ’97: Visualization Conference, Information Visualization Symposium and Parallel Rendering Symposium, pp. 92–99 (1997). https://doi.org/10.1109/INFVIS.1997.636792
Class, B.: Summit: scaling deep learning interpretability by visualizing activation and attribution summarizations
Conner, C., Samuel, J., Garvey, M., Samuel, Y., Kretinin, A.: Conceptual frameworks for big data visualization: discussion of models, methods, and artificial intelligence for graphical representations of data. In: Handbook of Research for Big Data, pp. 197–234. Apple Academic Press (2021)
Dabrowski, J.R., Munson, E.V.: Is 100 milliseconds too fast? In: CHI’01 Extended Abstracts on Human Factors in Computing Systems, pp. 317–318 (2001)
Desolda, G., Esposito, A., Lanzilotti, R., Costabile, M.F.: Detecting emotions through machine learning for automatic UX evaluation. In: Ardito, C., et al. (eds.) INTERACT 2021. LNCS, vol. 12934, pp. 270–279. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-85613-7_19
Edwards, M., Aspinall, D.: The synthesis of digital systems using ASM design techniques. In: Computer Hardware Description Languages and their Applications, pp. 55–64 (1983)
Erraissi, A., Mouad, B., Belangour, A.: A big data visualization layer meta-model proposition. In: 2019 8th International Conference on Modeling Simulation and Applied Optimization (ICMSAO), pp. 1–5 (2019). https://doi.org/10.1109/ICMSAO.2019.8880276
Fekete, J.D., Fisher, D., Nandi, A., Sedlmair, M.: Progressive data analysis and visualization (Dagstuhl Seminar 18411). Dagstuhl Rep. 8(10), 1–40 (2019). https://doi.org/10.4230/DagRep.8.10.1, http://drops.dagstuhl.de/opus/volltexte/2019/10346
Ferrentino, A., AB, F.: State machines and their semantics in software engineering (1977)
Feyock, S.: Transition diagram-based cai/help systems. Int. J. Man Mach. Stud. 9(4), 399–413 (1977)
Galletta, A., Carnevale, L., Bramanti, A., Fazio, M.: An innovative methodology for big data visualization for telemedicine. IEEE Trans. Industr. Inf. 15(1), 490–497 (2019). https://doi.org/10.1109/TII.2018.2842234
Golfarelli, M., Rizzi, S.: A model-driven approach to automate data visualization in big data analytics. Inf. Visual. 19(1), 24–47 (2020). https://doi.org/10.1177/1473871619858933, https://doi.org/10.1177/1473871619858933
Han, F., Xu, T., Tian, C., Hou, Z.: Investigation on human visual response latency. In: 2010 International Conference On Computer Design and Applications, vol. 1, pp. V1–602. IEEE (2010)
Harel, D.: Statecharts: a visual formalism for complex systems. Sci. Comput. Program. 8(3), 231–274 (1987)
Huot, S., Dumas, C., Dragicevic, P., Fekete, J.D., Hégron, G.: The MaggLite post-WIMP toolkit: draw it, connect it and run it. In: Proceedings of ACM Symposium on User Interface Software and Technology. UIST ’04, pp. 257–266. ACM, New York, NY, USA (2004)
Jacob, R.J.: Using formal specifications in the design of a human-computer interface. Commun. ACM 26(4), 259–264 (1983)
Keim, D., Andrienko, G., Fekete, J.-D., Görg, C., Kohlhammer, J., Melançon, G.: Visual analytics: definition, process, and challenges. In: Kerren, A., Stasko, J.T., Fekete, J.-D., North, C. (eds.) Information Visualization. LNCS, vol. 4950, pp. 154–175. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-70956-5_7
Keim, D., Qu, H., Ma, K.L.: Big-data visualization. IEEE Comput. Graph. Appl. 33(4), 20–21 (2013). https://doi.org/10.1109/MCG.2013.54
Liu, Z., Heer, J.: The effects of interactive latency on exploratory visual analysis. IEEE Trans. Vis. Comput. Graph. 20(12), 2122–2131 (2014). https://doi.org/10.1109/TVCG.2014.2346452
Livny, M., et al.: Devise: integrated querying and visual exploration of large datasets. In: SIGMOD ’97, pp. 301–312. Association for Computing Machinery, New York, NY, USA (1997)
Marrella, A., Catarci, T.: Measuring the learnability of interactive systems using a petri net based approach. In: Proceedings of the 2018 Designing Interactive Systems Conference. In: DIS ’18, pp. 1309–1319 (2018). https://doi.org/10.1145/3196709.3196744
Miller, R.B.: Response time in man-computer conversational transactions. In: Proceedings of the December 9–11, 1968, Fall Joint Computer Conference, Part I, pp. 267–277 (1968)
Mohammed, L.T., AlHabshy, A.A., ElDahshan, K.A.: Big data visualization: a survey, In: 2022 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA), pp. 1–12 (2022). https://doi.org/10.1109/HORA55278.2022.9799819
Moreland, K.: A survey of visualization pipelines. IEEE Trans. Vis. Comput. Graph. 19(3), 367–378 (2013). https://doi.org/10.1109/TVCG.2012.133
Moritz, D., Howe, B., Heer, J.: Falcon: balancing interactive latency and resolution sensitivity for scalable linked visualizations. In: CHI ’19. ACM, New York, NY, USA (2019). https://doi.org/10.1145/3290605.3300924
Myers, B.A.: Separating application code from toolkits: eliminating the spaghetti of call-backs. In: Proceedings of the 4th Annual ACM Symposium on User Interface Software and Technology. UIST ’91, pp. 211–220. ACM, New York, NY, USA (1991)
Nah, F.F.H.: A study on tolerable waiting time: how long are web users willing to wait? Behav. Inf. Technol. 23(3), 153–163 (2004)
Nielsen, J.: Usability Engineering. Morgan Kaufmann, Burlington (1993)
Oney, S., Myers, B., Brandt, J.: Interstate: a language and environment for expressing interface behavior. In: Proceedings of ACM Symposium on User Interface Software and Technology. UIST ’14, pp. 263–272. ACM, New York, NY, USA (2014)
Parnas, D.L.: On the use of transition diagrams in the design of a user interface for an interactive computer system. In: Proceedings of the 1969 24th National Conference, pp. 379–385 (1969)
Qin, X., Luo, Y., Tang, N., Li, G.: Deepeye: an automatic big data visualization framework. Big Data Min. Anal. 1(1), 75–82 (2018). https://doi.org/10.26599/BDMA.2018.9020007
Raghav, R.S., Pothula, S., Vengattaraman, T., Ponnurangam, D.: A survey of data visualization tools for analyzing large volume of data in big data platform. In: 2016 International Conference on Communication and Electronics Systems (ICCES), pp. 1–6 (2016). https://doi.org/10.1109/CESYS.2016.7889976
Sacha, D., Stoffel, A., Stoffel, F., Kwon, B.C., Ellis, G., Keim, D.A.: Knowledge generation model for visual analytics. IEEE Trans. Visual Comput. Graph. 20(12), 1604–1613 (2014). https://doi.org/10.1109/TVCG.2014.2346481
Shin, M., Soen, A., Readshaw, B.T., Blackburn, S.M., Whitelaw, M., Xie, L.: Influence flowers of academic entities. In: 2019 IEEE Conference on Visual Analytics Science and Technology (VAST), pp. 1–10. IEEE (2019)
Shneiderman, B.: Response time and display rate in human performance with computers. ACM Comput. Surv. (CSUR) 16(3), 265–285 (1984)
Shneiderman, B.: The eyes have it: a task by data type taxonomy for information visualizations, pp. 364–371. Interactive Technologies (2003). https://doi.org/10.1016/B978-155860915-0/50046-9
Shneiderman, B., Plaisant, C., Cohen, M., Jacobs, S., Elmqvist, N., Diakopoulos, N.: Designing the User Interface: Strategies for Effective Human-Computer Interaction, 6th edn. Pearson, London (2016)
Strahl, J., Peltonen, J., Floréen, P.: Directing and combining multiple queries for exploratory search by visual interactive intent modeling. In: Ardito, C., et al. (eds.) INTERACT 2021. LNCS, vol. 12934, pp. 514–535. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-85613-7_34
Vredenburg, K., Mao, J.Y., Smith, P.W., Carey, T.: A survey of user-centered design practice. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. CHI ’02, pp. 471–478. Association for Computing Machinery, New York, NY, USA (2002)
Waloszek, G., Kreichgauer, U.: User-centered evaluation of the responsiveness of applications. In: Gross, T., et al. (eds.) INTERACT 2009. LNCS, vol. 5726, pp. 239–242. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-03655-2_29
Woodburn, L., Yang, Y., Marriott, K.: Interactive visualisation of hierarchical quantitative data: an evaluation. In: 2019 IEEE Visualization Conference (VIS), pp. 96–100. IEEE (2019)
Yang, J., Bäuerle, A., Moritz, D., Çağatay Demiralp: Vegaprof: Profiling vega visualizations (2022)
Yi, J.S., ah Kang, Y., Stasko, J.T., Jacko, J.A.: Toward a deeper understanding of the role of interaction in information visualization. IEEE Trans. Vis. Comput. Graph. (2007)
Zgraggen, E., Galakatos, A., Crotty, A., Fekete, J.D., Kraska, T.: How progressive visualizations affect exploratory analysis. IEEE Trans. Vis. Comput. Graph. 23(8), 1977–1987 (2017). https://doi.org/10.1109/TVCG.2016.2607714
Zhang, T., Ramakrishnan, R., Livny, M.: Birch: an efficient data clustering method for very large databases. SIGMOD ’96, pp. 103–114. Association for Computing Machinery, New York, NY, USA (1996)
Zhang, Y., Chanana, K., Dunne, C.: IDMVis: temporal event sequence visualization for type 1 diabetes treatment decision support. IEEE Trans. Visual Comput. Graph. 25(1), 512–522 (2018)
Acknowledgements
We thank Dr. Leilani Battle and Dr. Jean-Daniel Fekete for the initial discussion and precious suggestions on this work. This study was carried out within the MICS (Made in Italy - Circular and Sustainable) Extended Partnership and received funding from the European Union Next-GenerationEU (PIANO NAZIONALE DI RIPRESA E RESILIENZA (PNRR) - MISSIONE 4 COMPONENTE 2, INVESTIMENTO 1.3 - D.D. 1551.11- 10-2022, PE00000004).
The work of Dario Benvenuti is supported by the H2020 project DataCloud (Grant number: 101016835)
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Benvenuti, D., Filosa, M., Catarci, T., Angelini, M. (2023). Modeling and Assessing User Interaction in Big Data Visualization Systems. In: Abdelnour Nocera, J., Kristín Lárusdóttir, M., Petrie, H., Piccinno, A., Winckler, M. (eds) Human-Computer Interaction – INTERACT 2023. INTERACT 2023. Lecture Notes in Computer Science, vol 14143. Springer, Cham. https://doi.org/10.1007/978-3-031-42283-6_5
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