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The VLDB Journal
Erschienen in: The VLDB Journal 1/2020

19.11.2019 | Special Issue Paper

Making data visualization more efficient and effective: a survey

verfasst von: Xuedi Qin, Yuyu Luo, Nan Tang, Guoliang Li

Erschienen in: The VLDB Journal | Ausgabe 1/2020

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Abstract

Data visualization is crucial in today’s data-driven business world, which has been widely used for helping decision making that is closely related to major revenues of many industrial companies. However, due to the high demand of data processing w.r.t. the volume, velocity, and veracity of data, there is an emerging need for database experts to help for efficient and effective data visualization. In response to this demand, this article surveys techniques that make data visualization more efficient and effective. (1) Visualization specifications define how the users can specify their requirements for generating visualizations. (2) Efficient approaches for data visualization process the data and a given visualization specification, which then produce visualizations with the primary target to be efficient and scalable at an interactive speed. (3) Data visualization recommendation is to auto-complete an incomplete specification, or to discover more interesting visualizations based on a reference visualization.
Vorheriger Artikel The core decomposition of networks: theory, algorithms and applications
Nächster Artikel Evaluating interactive data systems

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Fußnoten
1
Note that, our pipeline and terminologies used in this paper are slightly different than those used in the visualization community. Please refer to https://​infovis-wiki.​net/​wiki/​Visualization_​Pipeline for more details.
 
Literatur
1.
Michael, B., Vadim, O., Jeffrey, H.: D3: Data-driven documents. TVCG 17(12), 2301–9 (2011)
2.
Satyanarayan, A., Moritz, D., Wongsuphasawat, K., Heer, J.: Vega-lite: a grammar of interactive graphics. TVCG 23(1), 341–350 (2016)
3.
Hanrahan, P.: Vizql: a language for query, analysis and visualization. In: SIGMOD, p. 721 (2006)
4.
5.
6.
7.
Neumann, T., Mühlbauer, T., Kemper, A.: Fast serializable multi-version concurrency control for main-memory database systems. In: SIGMOD, pp. 677–689 (2015)
8.
Neumann, T.: Efficiently compiling efficient query plans for modern hardware. PVLDB 4(9), 539–550 (2011)
9.
10.
11.
12.
13.
14.
15.
Wickham, H.: ggplot2–elegant graphics for data analysis. J Comput. Graph. Stat. 19(1), 3–28 (2009)MathSciNetMATH
16.
Li, D., Mei, H., Shen, Y., Su, S., Zhang, W., Wang, J., Zu, M., Chen, W.: ECharts: A declarative framework for rapid construction of web-based visualization. Vis. Inform. 2, 136–146 (2018)
17.
Luo, Y., Qin, X., Tang, N., Li, G.: DeepEye: towards automatic data visualization. In: ICDE, pp. 101–112 (2018)
18.
Siddiqui, T., Lee, J., Kim, A., Xue, E., Yu, X., Zou, S., Guo, L., Liu, C., Wang, C., Karahalios, K., Parameswaran, A.G.: Fast-forwarding to desired visualizations with zenvisage. In: CIDR (2017)
19.
Kalinin, A., Cetintemel, U., Zdonik, S.: Interactive data exploration using semantic windows. In: SIGMOD, pp. 505–516 (2014)
20.
Stolte, C., Hanrahan, P.: Polaris: a system for query, analysis and visualization of multi-dimensional relational databases. In: INFOVIS, pp. 5–14 (2000)
21.
Qin, X., Luo, Y., Tang, N., Li, G.: DeepEye: an automatic big data visualization framework. Big Data Min. Anal. 1(1), 75–82 (2018)
22.
Vartak, M., Madden, S., Parameswaran, A., Polyzotis, N.: Seedb: automatically generating query visualizations. PVLDB 7(13), 1581–1584 (2014)
23.
Vartak, M., Rahman, S., Madden, S., Parameswaran, A.G., Polyzotis, N.: SeeDB: efficient data-driven visualization recommendations to support visual analytics. PVLDB 8(13), 2182–2193 (2015)
24.
Ding, B., Huang, S., Chaudhuri, S., Chakrabarti, K., Wang, C.: Sample + seek: approximating aggregates with distribution precision guarantee. In: SIGMOD, pp. 679–694 (2016)
25.
Moritz, D., Fisher, D., Ding, B., Wang, C.: Trust, but verify: optimistic visualizations of approximate queries for exploring big data. In: CHI, pp. 2904–2915 (2017)
26.
Kim, A., Blais, E., Parameswaran, A.G., Indyk, P., Madden, S., Rubinfeld, R.: Rapid sampling for visualizations with ordering guarantees. PVLDB 8(5), 521–532 (2015)
27.
Fisher, D., Popov, I., Drucker, S., Schraefel, M.: Trust me, i’m partially right: incremental visualization lets analysts explore large datasets faster. In: CHI, pp. 1673–1682 (2012)
28.
Rahman, S., Aliakbarpour, M., Kong, H.K., Blais, E., Karahalios, K., Parameswaran, A., Rubinfield, R., Rahman, S., Aliakbarpour, M., Kong, H.K.: I’ve seen “enough”: incrementally improving visualizations to support rapid decision making. PVLDB 10(11), 1262–1273 (2017)
29.
Wesley, R.M.G., Eldridge, M., Terlecki, P.: An analytic data engine for visualization in tableau. In: SIGMOD, pp. 1185–1194 (2011)
30.
Wang, Z., Ferreira, N., Wei, Y., Bhaskar, A.S., Scheidegger, C.: Gaussian cubes: real-time modeling for visual exploration of large multidimensional datasets. TVCG 23(1), 681–690 (2016)
31.
Liu, Z., Jiang, B., Heer, J.: imMens: real-time visual querying of big data. In: Eurographics Conference on Visualization, pp. 421–430 (2013)
32.
Luo, Y., Qin, X., Tang, N., Li, G., Wang, X.: DeepEye: creating good data visualizations by keyword search. In: SIGMOD, pp. 1733–1736 (2018)
33.
Wu, E., Psallidas, F., Miao, Z., Zhang, H., Rettig, L.: Combining design and performance in a data visualization management system. In: CIDR (2017)
34.
Doshi, P.R., Rundensteiner, E.A., Ward, M.O.: Prefetching for visual data exploration. In: DASFAA, pp. 195–202 (2003)
35.
Moritz, D., Wang, C., Nelson, G.L., Lin, H., Smith, A.M., Howe, B., Heer, J.: Formalizing visualization design knowledge as constraints: actionable and extensible models in draco. TVCG 25(1), 438–448 (2019)
36.
Siddiqui, T., Kim, A., Lee, J., Karahalios, K., Parameswaran, A.G.: Effortless data exploration with zenvisage: an expressive and interactive visual analytics system. PVLDB 10(4), 457–468 (2016)
37.
Qin, X., Luo, Y., Tang, N., Li, G.: DeepEye: visualizing your data by keyword search. In: EDBT Vision (2018)
38.
Seo, J., Shneiderman, B.: A rank-by-feature framework for interactive exploration of multidimensional data. IV 4(2), 96–113 (2005)
39.
Mackinlay, J.D., Hanrahan, P., Stolte, C.: Show me: automatic presentation for visual analysis. TVCG 13(6), 1137–1144 (2007)
40.
Wang, Y., Han, F., Zhu, L., Deussen, O., Chen, B.: Line graph or scatter plot? Automatic selection of methods for visualizing trends in time series. TVCG 24(2), 1141–1154 (2018)
41.
Wongsuphasawat, K., Moritz, D., Anand, A., Mackinlay, J.D., Howe, B., Heer, J.: Voyager: exploratory analysis via faceted browsing of visualization recommendations. TVCG 22(1), 649–658 (2016)
42.
Kandel, S., Paepcke, A., Hellerstein, J., Heer, J.: Wrangler: interactive visual specification of data transformation scripts. In: CHI, pp. 3363–3372 (2011)
43.
Von Landesberger, T., Kuijper, A., Schreck, T., Kohlhammer, J., van Wijk, J.J., Fekete, J.-D., Fellner, D.W.: Visual analysis of large graphs: state-of-the-art and future research challenges. Comput. Graph. Forum 30, 1719–1749 (2011)
44.
Herman, I., Melançon, G., Marshall, M.S.: Graph visualization and navigation in information visualization: a survey. TVCG 6(1), 24–43 (2000)
45.
Beck, F., Burch, M., Diehl, S., Weiskopf, D.: A taxonomy and survey of dynamic graph visualization. Comput. Graph. Forum 36(1), 133–159 (2017)
46.
Bikakis, N., Sellis, T.: Exploration and visualization in the web of big linked data: a survey of the state of the art. arXiv preprint arXiv:​1601.​08059 (2016)
47.
Marie, N., Gandon, F.: Survey of linked data based exploration systems. In: IESD (2014)
48.
Dadzie, A.-S., Pietriga, E.: Visualisation of linked data-reprise. Semant. Web 8(1), 1–21 (2017)
49.
Katifori, A., Halatsis, C., Lepouras, G., Vassilakis, C., Giannopoulou, E.: Ontology visualization methods’a survey. ACM Comput. Surv. (CSUR) 39(4), 10 (2007)
50.
Liu, S., Maljovec, D., Wang, B., Bremer, P.-T., Pascucci, V.: Visualizing high-dimensional data: advances in the past decade. TVCG 3, 1249–1268 (2017)
51.
Wohlfart, E., Aigner, W., Bertone, A., Miksch, S.: Comparing information visualization tools focusing on the temporal dimensions. In: IV, pp. 69–74 (2008)
52.
Mei, H., Ma, Y., Wei, Y., Chen, W.: The design space of construction tools for information visualization: A survey. J. Vis. Lang. Comput. 44, 120–132 (2018)
53.
Diamond, M., Mattia, A.: Data visualization: an exploratory study into the software tools used by businesses. J. Instr. Pedag. 17, 1–7 (2017)
54.
Ghosh, A., Nashaat, M., Miller, J., Quader, S., Marston, C.: A comprehensive review of tools for exploratory analysis of tabular industrial datasets. Vis. Inform. 2(4), 235–253 (2018)
55.
Keim, D.A., Lee, J.P., Thuraisinghaman, B., Wittenbrink, C.: Database issues for data visualization: supporting interactive database exploration. In: Workshop on Database Issues for Data Visualization, pp. 12–25 (1995)
56.
Idreos, S., Papaemmanouil, O., Chaudhuri, S.: Overview of data exploration techniques. In: SIGMOD, pp. 277–281 (2015)
57.
58.
Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. TKDE 6, 734–749 (2005)
59.
Burke, R.: Hybrid recommender systems: survey and experiments. User Model. User-Adapted Interact. 12(4), 331–370 (2002)MATH
60.
Sharma, L., Gera, A.: A survey of recommendation system: research challenges. IJETT 4(5), 1989–1992 (2013)
61.
Bobadilla, J., Ortega, F., Hernando, A., Gutiérrez, A.: Recommender systems survey. Knowl. Based Syst. 46, 109–132 (2013)
62.
Christi, J.R., Premkumar, K.: Survey on recommendation and visualization techniques for QoS-aware web services. In: ICICES, pp. 1–6 (2014)
63.
Guy, I., Zwerdling, N., Carmel, D., Ronen, I., Uziel, E., Yogev, S., Ofek-Koifman, S.: Personalized recommendation of social software items based on social relations. In: RecSys, pp. 53–60 (2009)
64.
Wei, K., Huang, J., Fu, S.: A survey of e-commerce recommender systems. In: ICSSSM, pp. 1–5 (2007)
65.
Heer, J., Card, S.K., Landay, J.A.: prefuse: a toolkit for interactive information visualization. In: CHI, pp. 421–430 (2005)
66.
67.
Bostock, M., Heer, J.: Protovis: a graphical toolkit for visualization. TVCG 15(6), 1121–8 (2009)
68.
Satyanarayan, A., Russell, R., Hoffswell, J., Heer, J.: Reactive vega: a streaming dataflow architecture for declarative interactive visualization. TVCG 22(1), 659–668 (2015)
69.
Khan, M., Khan, S.S.: Data and information visualization methods, and interactive mechanisms: a survey. Int. J. Comput. Appl. 34(1), 1–14 (2011)
70.
Wilkinson, L.: The Grammar of Graphics. Springer, Berlin (2005)MATH
71.
Wickham, H.: A layered grammar of graphics. J. Comput. Graph. Stat. 19(1), 3–28 (2010)MathSciNet
72.
VanderPlas, J., Granger, B.E., Heer, J., Moritz, D., Wongsuphasawat, K., Satyanarayan, A., Lees, E., Timofeev, I., Welsh, B., Sievert, S.: Altair: interactive statistical visualizations for python. https://​altair-viz.​github.​io. Accessed 31 Dec 2018
73.
74.
Shneiderman, B.: Direct manipulation: a step beyond programming languages. IEEE Comput. 16(8), 57–69 (1983)
75.
76.
Gonzalez, H., Halevy, A.Y., Jensen, C.S., Langen, A., Madhavan, J., Shapley, R., Shen, W., Goldberg-Kidon, J.: Google fusion tables: web-centered data management and collaboration. In: SIGMOD, pp. 1061–1066 (2010)
77.
Ren, D., Höllerer, T., Yuan, X.: iVisDesigner: expressive interactive design of information visualizations. TVCG 20(12), 2092–2101 (2014)
78.
79.
Yalçın, M.A., Elmqvist, N., Bederson, B.B.: Keshif: Rapid and expressive tabular data exploration for novices. TVCG 24(8), 2339–2352 (2018)
80.
81.
Liu, Z., Thompson, J., Wilson, A., Dontcheva, M., Delorey, J., Grigg, S., Kerr, B., Stasko, J.T.: Data illustrator: Augmenting vector design tools with lazy data binding for expressive visualization authoring. In: CHI, p. 123 (2018)
82.
Warren, L.: The visual display of quantitative information. Yale J. Biol. Med. 44(4), 400–400 (1986)
83.
Wongsuphasawat, K., Qu, Z., Moritz, D., Chang, R., Ouk, F., Anand, A., Mackinlay, J.D., Howe, B., Heer, J.: Voyager 2: augmenting visual analysis with partial view specifications. In: CHI, pp. 2648–2659 (2017)
84.
Key, A., Howe, B., Perry, D., Aragon, C.R.: Vizdeck: self-organizing dashboards for visual analytics. In: SIGMOD, pp. 681–684 (2012)
85.
Kandel, S., Parikh, R., Paepcke, A., Hellerstein, J.M., Heer, J.: Profiler: integrated statistical analysis and visualization for data quality assessment. In: AVI, pp. 547–554 (2012)
86.
Elzen, S.V.D., van Wijk, J.J.: Small multiples, large singles: a new approach for visual data exploration. Comput. Graph. Forum 32(3pt2), 191–200 (2013)
87.
Wilkinson, L., Anand, A., Grossman, R.: Graph-theoretic scagnostics. In: IEEE Symposium on Information Visualization, 2005. IEEE, Minneapolis, MN, USA (2005)
88.
Mackinlay, J.: Automating the design of graphical presentations of relational information. ACM Trans. Graph. 5(2), 110–141 (1986)
89.
Setlur, V., Battersby, S.E., Tory, M., Gossweiler, R., Chang, A.X.: Eviza: A natural language interface for visual analysis. In: UIST, pp. 365–377 (2016)
90.
Hoque, E., Setlur, V., Tory, M., Dykeman, I.: Applying pragmatics principles for interaction with visual analytics. TVCG 24(1), 309–318 (2017)
91.
Wu, E., Battle, L., Madden, S.R.: The case for data visualization management systems: vision paper. PVLDB 7(10), 903–906 (2014)
92.
Wu, E., Psallidas, F., Miao, Z., Zhang, H., Rettig, L., Wu, Y., Sellam, T.: Combining design and performance in a data visualization management system. In: CIDR (2017)
93.
Lins, L., Klosowski, J.T., Scheidegger, C.: Nanocubes for real-time exploration of spatiotemporal datasets. TVCG 19(12), 2456–2465 (2013)
94.
Pang, Z., Wu, S., Chen, G., Chen, K., Shou, L.: FlashView: an interactive visual explorer for raw data. PVLDB 10(12), 1869–1872 (2017)
95.
Zoumpatianos, K., Idreos, S., Palpanas, T.: Indexing for interactive exploration of big data series. In: SIGMOD, pp. 1555–1566 (2014)
96.
Piringer, H., Tominski, C., Muigg, P., Berger, W.: A multi-threading architecture to support interactive visual exploration. TVCG 15(6), 1113–1120 (2009)
97.
Chan, S.-M., Xiao, L., Gerth, J., Hanrahan. P.: Maintaining interactivity while exploring massive time series. In: VAST, pp. 59–66 (2008)
98.
Battle, L., Chang, R., Stonebraker, M.: Dynamic prefetching of data tiles for interactive visualization. In: SIGMOD, pp. 1363–1375 (2016)
99.
Alabi, D., Wu, E.: PFunk-H: approximate query processing using perceptual models. In: HILDA@SIGMOD, pp. 10–16 (2016)
100.
Bikakis, N., Papastefanatos, G., Skourla, M., Sellis, T.: A hierarchical aggregation framework for efficient multilevel visual exploration and analysis. Semant. Web 8(1), 139–179 (2017)
101.
Elmqvist, N., Fekete, J.D.: Hierarchical aggregation for information visualization: overview, techniques, and design guidelines. TVCG 16(3), 439–454 (2010)
102.
Pahins, C.A., Stephens, S.A., Scheidegger, C., Comba, J.L.: Hashedcubes: simple, low memory, real-time visual exploration of big data. TVCG 23(1), 671–680 (2016)
103.
Moritz, D., Howe, B., Heer, J.: Falcon: balancing interactive latency and resolution sensitivity for scalable linked visualizations. In: CHI, p. 694 (2019)
104.
Tauheed, F., Heinis, T., Shrmann, F., Markram, H., Ailamaki, A.: SCOUT: prefetching for latent feature following queries. PVLDB 5(11), 1531–1542 (2012)
105.
Yesilmurat, S.: Retrospective adaptive prefetching for interactive web gis applications. Geoinformatica 16(3), 435–466 (2012)
106.
Dong, H.L., Kim, J.S., Kim, S.D., Kim, K.C., Yoosung, K., Park, J.: Adaptation of a neighbor selection markov chain for prefetching tiled web GIS data. In: ADVIS, pp. 213–222 (2002)
107.
Fua, Y.H., Ward, M.O., Rundensteiner, E.A.: Structure-based brushes: a mechanism for navigating hierarchically organized data and information spaces. TVCG 6(2), 150–159 (2000)
108.
Tao, W., Liu, X., Demiralp, Ç., Chang, R., Stonebraker, M.: Kyrix: Interactive visual data exploration at scale. In: CIDR (2019)
109.
Broy, M., Denert, E., Bayer, R., McCreight, E.: Organization and maintenance of large ordered indexes. In: Broy, M., Denert, E. (eds.) Software Pioneers. Springer, Berlin, Heidelberg (2002)
110.
Wongsuphasawat, K., Moritz, D., Anand, A., Mackinlay, J.D., Howe, B., Heer, J.: Towards a general-purpose query language for visualization recommendation. In: HILDA@SIGMOD, pp. 4–9 (2016)
111.
Roth, S.F., Kolojejchick, J., Mattis, J., Goldstein, J.: Interactive graphic design using automatic presentation knowledge. In: CHI, p. 207 (1994)
112.
Casner, S.M.: Task-analytic approach to the automated design of graphic presentations. ACM Trans. Graph. 10(2), 111–151 (1991)
113.
114.
Cleveland, W.S., McGill, R.: Graphical perception: theory, experimentation, and application to the development of graphical methods. ASA 79(387), 531–554 (1984)
115.
Shepard, R.N.: Toward a universal law of generalization for psychological science. Science 242(4880), 1317–1323 (1988)MathSciNetMATH
116.
Lewandowsky, Stephan, Spence, Ian: Discriminating strata in scatterplots. ASA 84(407), 682–688 (1989)
117.
Hu, K.Z., Orghian, D., Hidalgo, C.A.: DIVE: a mixed-initiative system supporting integrated data exploration workflows. In: HILDA@SIGMOD, pp. 5:1–5:7 (2018)
118.
Cleveland, W.S.: Robust locally weighted regression and smoothing scatterplots. ASA 74(368), 829–836 (1979)MathSciNetMATH
119.
120.
Dibia, V., Demiralp, Ç.: Data2Vis: Automatic generation of data visualizations using sequence to sequence recurrent neural networks. CoRR, abs/1804.03126 (2018)
121.
Burges, C., Shaked, T., Renshaw, E., Lazier, A., Deeds, M., Hamilton, N., Hullender, G.: Learning to rank using gradient descent. In: Proceedings of the 22nd International Conference on Machine Learning, pp. 89–96 (2005)
122.
Herbrich, R., Graepel, T., Obermayer, K.: Support vector learning for ordinal regression. In: ICANN, vol. 1, pp. 97–102 (2002)
123.
Kim, Y., Heer, J.: Assessing effects of task and data distribution on the effectiveness of visual encodings. Comput. Graph. Forum 37(3), 157–167 (2018)
124.
Saket, B., Endert, A., Demiralp, C.: Task-based effectiveness of basic visualizations. TVCG PP(99), 1–1 (2017)
125.
Quinlan, J.R.: Induction of decision trees. Mach. Learn. 1(1), 81–106 (1986)
126.
Wu, Q., Burges, C.J., Svore, K.M., Gao, J.: Ranking, boosting, and model adaptation. Technical report, Microsoft Research (2008)
127.
128.
Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. NIPS 4, 3104–3112 (2014)
129.
Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. Comput. Sci. arXiv preprint arXiv:​1409.​0473 (2014)
130.
Sundermeyer, M., Schlüter, R., Ney, H.: LSTM neural networks for language modeling. In: Interspeech, pp. 601–608 (2012)
131.
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
132.
Poco, J., Heer, J.: Reverse-engineering visualizations: recovering visual encodings from chart images. Comput Graph Forum 36(3), 353–363
133.
Sakoe, H., Chiba, S.: Dynamic programming algorithm optimization for spoken word recognition. IEEE Trans. Acoust. Speech Signal Process. 26(1), 159–165 (1990)MATH
134.
Gotz, D., Wen, Z.: Behavior-driven visualization recommendation. In: IUI, pp. 315–324 (2009)
135.
Schafer, J.B., Konstan, J., Riedl, J.: Recommender systems in e-commerce. In: World Automation Congress, pp. 158–166 (1999)
136.
Liu, R.R., Jia, C.X., Zhou, T., Sun, D., Wang, B.H.: Personal recommendation via modified collaborative filtering. Physica A Stat. Mech. Appl. 388(4), 462–468 (2012)
137.
Soboroff, I., Nicholas, C.: Combining content and collaboration in text filtering. In: IJCAI, pp. 86–91 (1999)
138.
Mutlu, B., Veas, E., Trattner, C.: VizRec: recommending personalized visualizations. TIIS 6(4), 31 (2016)
139.
Wu, E., Madden, S.R.: Scorpion: explaining away outliers in aggregate queries. In: PVLDB, pp. 553–564 (2013)
140.
Song, H., Szafir, D.A.: Where’s my data? Evaluating visualizations with missing data. IEEE Trans. Vis. Comput. Graph. 25(1), 914–924 (2019)
141.
Battle, L., Angelini, M., Binnig, C., Catarci, T., Eichmann, P., Fekete, J., Santucci, G., Sedlmair, M., Willett, W.: Evaluating visual data analysis systems: a discussion report. In: HILDA@SIGMOD, pp. 4:1–4:6 (2018)
142.
Battle, L., Chang, R., Heer, J., Stonebraker, M.: Position statement: the case for a visualization performance benchmark. In: DSIA, pp. 1–5 (2017)
143.
Jiang, L., Rahman, P., Nandi, A.: Evaluating interactive data systems: workloads, metrics, and guidelines. In: SIGMOD, pp. 1637–1644 (2018)
144.
Hu, K.Z., Gaikwad, S.N.S., Hulsebos, M., Bakker, M.A., Zgraggen, E., Hidalgo, C.A., Kraska, T., Li, G., Satyanarayan, A., Demiralp, Ç.: Viznet: Towards A large-scale visualization learning and benchmarking repository. In: CHI, pp. 662 (2019)
145.
Valizadegan, H., Jin, R., Zhang, R., Mao, J.: Learning to rank by optimizing NDCG measure. In: NIPS, pp. 1883–1891 (2009)
146.
Rezig, E.K., Cao, L., Stonebraker, M., Simonini, G., Tao, W., Madden, S., Ouzzani, M., Tang, N., Elmagarmid, A.K.: Data civilizer 2.0: a holistic framework for data preparation and analytics. PVLDB 12(12), 1954–1957 (2019)
147.
Rezig, E.K., Cao, L., Simonini, G., Schoemans, M., Madden, S., Ouzzani, M., Tang, N., Stonebraker, M.: Dagger: a data (not code) debugger. In: CIDR (2020)
Metadaten
Titel
Making data visualization more efficient and effective: a survey
verfasst von
Xuedi Qin
Yuyu Luo
Nan Tang
Guoliang Li
Publikationsdatum
19.11.2019
Verlag
Springer Berlin Heidelberg
Erschienen in
The VLDB Journal / Ausgabe 1/2020
Print ISSN: 1066-8888
Elektronische ISSN: 0949-877X
DOI
https://doi.org/10.1007/s00778-019-00588-3

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