A. Abouzied, D. Angluin, C. H. Papadimitriou, J. M. Hellerstein, and A. Silberschatz, Learning and verifying quantified boolean queries by example, Symposium on Principles of Database Systems (PODS), 2013.
DOI : 10.1145/2463664.2465220
URL : http://db.cs.berkeley.edu/papers/pods13-learning.pdf

A. Abouzied, J. M. Hellerstein, and A. Silberschatz, Playful query specification with dataplay, Proc. VLDB Endow, vol.5, issue.12, pp.1938-1941, 2012.
DOI : 10.14778/2367502.2367542
URL : http://vldb.org/pvldb/vol5/p1938_azzaabouzied_vldb2012.pdf

C. B. Barber, D. P. Dobkin, and H. Huhdanpaa, The quickhull algorithm for convex hulls, ACM Trans. Math. Softw, vol.22, issue.4, pp.469-483, 1996.
DOI : 10.1145/235815.235821
URL : http://www.cise.ufl.edu/~ungor/courses/fall06/papers/QuickHull.pdf

K. Bellare, S. Iyengar, A. Parameswaran, and V. Rastogi, Active sampling for entity matching with guarantees, ACM Trans. Knowl. Discov. Data, vol.7, issue.3, 2013.
DOI : 10.1145/2513092.2500490

A. Bordes, S. Ertekin, J. Weston, and L. Bottou, Fast kernel classifiers with online and active learning, J. Mach. Learn. Res, vol.6, pp.1579-1619, 2005.
URL : https://hal.archives-ouvertes.fr/hal-00752361

L. Breiman, Random forests. Machine learning, vol.45, pp.5-32, 2001.

C. Campbell, N. Cristianini, and A. J. Smola, Query learning with large margin classifiers, Proceedings of the Seventeenth International Conference on Machine Learning, ICML '00, pp.111-118, 2000.

G. Casella and R. L. Berger, Statistical Inference. Cengage Learning, 2001.

A. Cheung and A. Solar-lezama, Computer-assisted query formulation, Found. Trends Program. Lang, vol.3, issue.1, pp.1-94, 2016.
DOI : 10.1561/2500000018

A. Cheung, A. Solar-lezama, and S. Madden, Using program synthesis for social recommendations, Proceedings of the 21st ACM International Conference on Information and Knowledge Management, CIKM '12, pp.1732-1736, 2012.
DOI : 10.1145/2396761.2398507
URL : http://db.csail.mit.edu/pubs/lifejoin.pdf

D. Dash, J. Rao, N. Megiddo, A. Ailamaki, and G. Lohman, Dynamic faceted search for discovery-driven analysis, CIKM, 2008.
DOI : 10.1145/1458082.1458087
URL : http://www.db.cs.cmu.edu/db-site/Pubs/Lib/cikm2008/55-Dynamic%20Faceted%20Search%20For%20Discovery-driven%20Analysis.pdf

Y. Diao, K. Dimitriadou, Z. Li, W. Liu, O. Papaemmanouil et al., AIDE: an automatic user navigation system for interactive data exploration, vol.8, pp.1964-1967, 2015.

K. Dimitriadou, O. Papaemmanouil, and Y. Diao, Explore-by-example: an automatic query steering framework for interactive data exploration, SIGMOD Conference, pp.517-528, 2014.
DOI : 10.1109/icdew.2014.6818348

K. Dimitriadou, O. Papaemmanouil, and Y. Diao, AIDE: an active learning-based approach for interactive data exploration, IEEE Transactions on Knowledge and Data Engineering, 2016.
DOI : 10.1109/tkde.2016.2599168

R. El-yaniv and Y. Wiener, Active learning via perfect selective classification, J. Mach. Learn. Res, vol.13, issue.1, pp.255-279, 2012.

S. Ertekin, J. Huang, L. Bottou, and L. Giles, Learning on the border: Active learning in imbalanced data classification, Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management, CIKM '07, pp.127-136, 2007.

R. Garnett, Y. Krishnamurthy, X. Xiong, J. G. Schneider, and R. P. Mann, Bayesian optimal active search and surveying, Proceedings of the 29th International Conference on Machine Learning, ICML 2012, 2012.

B. Grünbaum, Convex polytopes, Convex Polytopes, 2003.

S. Hanneke, Rates of convergence in active learning, Ann. Statist, vol.39, issue.1, pp.333-361, 2011.
DOI : 10.1214/10-aos843
URL : https://doi.org/10.1214/10-aos843

S. Hanneke, Theory of disagreement-based active learning, Found. Trends Mach. Learn, vol.7, issue.2-3, pp.131-309, 2014.
DOI : 10.1561/2200000037

S. Hanneke, Refined error bounds for several learning algorithms, J. Mach. Learn. Res, vol.17, issue.1, pp.4667-4721, 2016.

T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning, 2001.

E. Huang, L. Peng, L. D. Palma, A. Abdelkafi, A. Liu et al., Optimization for active learning-based interactive database exploration, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01870560

B. E. Jacobs and C. A. Walczak, A Generalized Query-by-Example Data Manipulation Language Based on Database Logic, IEEE Transactions on Software Engineering, vol.9, issue.1, pp.40-57, 1983.
DOI : 10.1109/tse.1983.236169

M. Kahng, S. B. Navathe, J. T. Stasko, and D. H. Chau, Interactive browsing and navigation in relational databases, Proc. VLDB Endow, vol.9, issue.12, pp.1017-1028, 2016.
DOI : 10.14778/2994509.2994520
URL : http://arxiv.org/pdf/1603.02371

A. Kalinin, U. Cetintemel, and S. Zdonik, Interactive data exploration using semantic windows, Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data, SIGMOD '14, pp.505-516, 2014.
DOI : 10.1145/2588555.2593666
URL : http://cs.brown.edu/~akalinin/papers/sem-windows.pdf

N. Kamat, P. Jayachandran, K. Tunga, and A. Nandi, Distributed Interactive Cube Exploration, ICDE, 2014.
DOI : 10.1109/icde.2014.6816674
URL : http://arnab.org/files/dice.nandi_.pdf

S. R. Lay, Convex Sets and Their Applications, 2007.

H. Li, C. Chan, and D. Maier, Query from examples: An iterative, data-driven approach to query construction, Proc. VLDB Endow, vol.8, issue.13, pp.2158-2169, 2015.

W. Liu, Y. Diao, and A. Liu, An analysis of query-agnostic sampling for interactive data exploration, 2016.
DOI : 10.1080/03610926.2017.1363231

Y. Ma, R. Garnett, and J. G. Schneider, ?-optimality for active learning on gaussian random fields, Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems, pp.2751-2759, 2013.

D. Mottin, M. Lissandrini, Y. Velegrakis, and T. Palpanas, Exemplar queries: Give me an example of what you need, Proc. VLDB Endow, vol.7, pp.365-376, 2014.
DOI : 10.14778/2732269.2732273

R. Neamtu, R. Ahsan, C. Lovering, C. Nguyen, E. A. Rundensteiner et al., Interactive time series analytics powered by ONEX, Proceedings of the 2017 ACM International Conference on Management of Data, SIGMOD Conference, pp.1595-1598, 2017.
DOI : 10.1145/3035918.3058729
URL : http://real.mtak.hu/74287/1/p1595_neamtu_u.pdf

G. Ozsoyoglu and H. Wang, Example-Based Graphical Database Query Languages, Computer, vol.26, issue.5, pp.25-38, 1993.
DOI : 10.1109/2.211893

S. B. Roy, H. Wang, G. Das, U. Nambiar, and M. Mohania, Minimum-effort driven dynamic faceted search in structured databases, Proceedings of the 17th ACM Conference on Information and Knowledge Management (CIKM), 2008.

S. B. Roy, H. Wang, U. Nambiar, G. Das, and M. Mohania, Dynacet: Building dynamic faceted search systems over databases, International Conference on Data Engineering, 2009.
DOI : 10.1109/icde.2009.117
URL : http://dbxlab.uta.edu/dbxlab/ICDE.pdf

B. Settles, Active Learning. Synthesis Lectures on Artificial Intelligence and Machine Learning, 2016.

Y. Shen, K. Chakrabarti, S. Chaudhuri, B. Ding, and L. Novik, Discovering queries based on example tuples, Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data, SIGMOD '14, pp.493-504, 2014.
DOI : 10.1145/2588555.2593664

J. Shlens, A tutorial on principal component analysis, 2014.

, Dr8 sample sql queries

A. S. Szalay, P. Z. Kunszt, A. Thakar, J. Gray, D. R. Slutz et al., Designing and mining multi-terabyte astronomy archives: The sloan digital sky survey, SIGMOD Conference, pp.451-462, 2000.

B. Tang, K. Mouratidis, and M. L. Yiu, Determining the impact regions of competing options in preference space, Proceedings of the 2017 ACM International Conference on Management of Data, SIGMOD '17, pp.805-820, 2017.

S. Tong and D. Koller, Support vector machine active learning with applications to text classification, J. Mach. Learn. Res, vol.2, pp.45-66, 2002.

Q. T. Tran, C. Chan, and S. Parthasarathy, Query by output, Proceedings of the 2009 ACM SIGMOD International Conference on Management of Data, SIGMOD '09, pp.535-548, 2009.

H. P. Vanchinathan, A. Marfurt, C. Robelin, D. Kossmann, and A. Krause, Discovering valuable items from massive data, Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.1195-1204, 2015.

Z. Zhao, L. D. Stefani, E. Zgraggen, C. Binnig, E. Upfal et al., ACM. (a) Accuracy for Q1 (0.32%) (b) Accuracy for Q2 (0.267%) (c) Accuracy for Q3 (0.267%) (d) Accuracy for Q4 (0.249%) (e) Accuracy for Q5 (0.231%) (f) Accuracy for Q6 (0.338%) (g) Accuracy for Q7 (0.818%) (h) Accuracy for Q8 (0.32%) (i) Accuracy for Q9 (0.302%) (j) Accuracy for Q10 (0.356%) (k) Accuracy for Q11 (0.285%) (l) Accuracy for Q12 (0.196%) (m) Accuracy for Q13 (0.285%) (n) Accuracy for Q14 (0.231%) (o) Accuracy for, Proceedings of the 2017 ACM International Conference on Management of Data, SIGMOD '17, pp.527-540, 2017.

, Accuracy for 18 queries in the Cars User Study( No noise), Figure, vol.11