S. K. Badam, N. Elmqvist, and J. Fekete, Steering the craft: Ui elements and visualizations for supporting progressive visual analytics, Comput. Graph. Forum, vol.36, issue.3, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01512256

G. E. Batista, E. J. Keogh, O. M. Tataw, and V. M. Souza, Cid: An efficient complexity-invariant distance for time series, Data Min. Knowl. Discov, vol.28, issue.3, 2014.

P. Buono and A. L. Simeone, Interactive shape specification for pattern search in time series, 2008.

A. Camerra, T. Palpanas, J. Shieh, and E. Keogh, isax 2.0: Indexing and mining one billion time series, ICDM, 2010.

P. Ciaccia and M. Patella, Pac nearest neighbor queries: Approximate and controlled search in high-dimensional and metric spaces, ICDE, 2000.

P. Ciaccia, M. Patella, and P. Zezula, A cost model for similarity queries in metric spaces, PODS, 1998.

M. Correll and M. Gleicher, The semantics of sketch: Flexibility in visual query systems for time series data, VAST, 2016.

B. Ding, S. Huang, S. Chaudhuri, K. Chakrabarti, and C. Wang, Sample + seek: Approximating aggregates with distribution precision guarantee, SIGMOD, 2016.

H. Ding, G. Trajcevski, P. Scheuermann, X. Wang, and E. Keogh, Querying and mining of time series data: Experimental comparison of representations and distance measures, Proc. VLDB Endow, vol.1, issue.2, 2008.

K. Echihabi, K. Zoumpatianos, T. Palpanas, and H. Benbrahim, The lernaean hydra of data series similarity search: An experimental evaluation of the state of the art, The VLDB Journal, vol.12, issue.2, 2018.

J. Fekete and R. Primet, Progressive analytics: A computation paradigm for exploratory data analysis, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01361430

D. Fisher, S. M. Drucker, and A. C. König, Exploratory visualization involving incremental, approximate database queries and uncertainty, IEEE CG&A, p.32, 2012.

M. Glueck, A. Khan, and D. J. Wigdor, Dive in!: Enabling progressive loading for real-time navigation of data visualizations, CHI, 2014.

A. Gogolou, T. Tsandilas, T. Palpanas, and A. Bezerianos, Comparing similarity perception in time series visualizations, IEEE TVCG, vol.25, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01845008

J. Jing, J. Dauwels, T. Rakthanmanon, E. Keogh, S. Cash et al., Rapid annotation of interictal epileptiform discharges via template matching under dynamic time warping, Journal of Neuroscience Methods, vol.274, 2016.

T. Kraska, Northstar: An interactive data science system, vol.11, pp.2150-2164, 2018.

M. Linardi and T. Palpanas, Scalable, variable-length similarity search in data series: The ULISSE approach, vol.11, 2018.

M. Mannino and A. Abouzied, Expressive time series querying with hand-drawn scale-free sketches, CHI, 2018.

D. Moritz, D. Fisher, B. Ding, and C. Wang, Trust, but verify: Optimistic visualizations of approximate queries for exploring big data, CHI, 2017.

J. Nielsen, Response times: The 3 important limits

T. Palpanas, Data series management: The road to big sequence analytics, SIGMOD Record, vol.44, issue.2, 2015.

S. Rahman, M. Aliakbarpour, H. Kong, E. Blais, K. Karahalios et al., I've seen "enough": Incrementally improving visualizations to support rapid decision making, vol.10, pp.1262-1273, 2017.

T. Rakthanmanon, B. Campana, A. Mueen, G. Batista, B. Westover et al., Searching and mining trillions of time series subsequences under dynamic time warping, KDD, 2012.

C. D. Stolper, A. Perer, and D. Gotz, Progressive visual analytics: User-driven visual exploration of in-progress analytics, IEEE TVCG, vol.20, 2014.

E. R. Tufte, The Visual Display of Quantitative Information, 1986.

C. Turkay, E. Kaya, S. Balcisoy, and H. Hauser, Designing progressive and interactive analytics processes for high-dimensional data analysis, IEEE TVCG, vol.23, issue.1, 2017.

Y. Wang, P. Wang, J. Pei, W. Wang, and S. Huang, A data-adaptive and dynamic segmentation index for whole matching on time series, Proc. VLDB Endow, vol.6, issue.10, 2013.

E. Zgraggen, A. Galakatos, A. Crotty, J. Fekete, and T. Kraska, How progressive visualizations affect exploratory analysis, IEEE TVCG, p.23, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01377896

K. Zoumpatianos, S. Idreos, and T. Palpanas, Ads: The adaptive data series index, The VLDB Journal, vol.25, issue.6, 2016.