The constant growth in data generation, driven by technological advancement, highlights the need to organize information to extract relevant knowledge. In this context, visual representations emerge as effective tools to simplify this complex task. The automation of this process can be achieved through visualization recommendation systems. This work aims to improve the understanding of data visualization recommendations by synthesizing current literature to identify research gaps and outline initial requirements for developing prototypes and tools in this area. To achieve this, we conducted a systematic literature mapping followed by forward snowballing, covering the period from 2017 to 2025, through which we carefully selected and analyzed 89 papers on data visualization recommendations. We provide an overview of visualization recommendation systems, identifying employed techniques and categorizing studies based on different recommendation approaches. We also guide the selection of algorithms and methods for developing automatic and semiautomatic recommendation systems and present lessons learned and future research possibilities.
BerinatoS.Good charts: the HBR guide to making smarter, more persuasive data visualizations. Harvard Business Review Press, 2016.
3.
KanchanaWADMadushankaGDLMadurangaHP, et al. Semi-automated recommendation platform for data visualization: Roopana. In: Proceedings of the 2017 Moratuwa engineering research conference (MERCon), 2017, pp. 117–122. DOI:10.1109/MERCon.2017.7980467.
4.
LiHWangYZhangS, et al. Kg4vis: a knowledge graph-based approach for visualization recommendation. IEEE Trans Vis Comput Graph2022; 28(1): 195–205.
5.
DevMKumarAKumarG, et al. Data science related to big data, data-driven decision making and its application. In: Proceedings of the 2022 2nd international conference on technological advancements in computational sciences (ICTACS), 2022, pp. 221–227. DOI: 10.1109/ICTACS56270.2022.9988578.
6.
BrynjolfssonEMcElheranK.The rapid adoption of data-driven decision-making. Am Econ Rev2016; 106(5): 133–139.
7.
KandelSPaepckeAHellersteinJM, et al. Enterprise data analysis and visualization: an interview study. IEEE Trans Vis Comput Graph2012; 18(12): 2917–2926.
8.
MunznerT.Visualization analysis and design. A K Peters Visualization Series. CRC Press, 2015.
9.
WardMOGrinsteinGKeimD.Interactive data visualization: foundations, techniques, and applications. A K Peters/CRC Press, 2015.
10.
CrisanAFisherSEGardyJL, et al. Gevitrec: data reconnaissance through recommendation using a domain-specific visualization prevalence design space. IEEE Trans Vis Comput Graph2022; 28(12): 4855–4872.
11.
ShenLShenETaiZ, et al. Visual data analysis with task-based recommendations. Data Sci Eng2022; 7(4): 354–369.
12.
HarrisCRossiRAMalikS, et al. Insight-centric visualization recommendation. ArXiv 2021; abs/2103.11297: 1–10.
13.
MackinlayJ.Automating the design of graphical presentations of relational information. ACM Trans Graph1986; 5(2): 110–141.
14.
MackinlayJHanrahanPStolteC.Show me: automatic presentation for visual analysis. IEEE Trans Vis Comput Graph2007; 13(6): 1137–1144.
15.
VartakMRahmanSMaddenS, et al. Seedb: efficient data-driven visualization recommendations to support visual analytics. Proc VLDB Endow2015; 8(13): 2182–2193.
16.
PandeyAL’YiSWangQ, et al. Genorec: a recommendation system for interactive genomics data visualization. IEEE Trans Vis Comput Graph2023; 29(1): 570–580.
17.
ZengZMohPDuF, et al. An evaluation-focused framework for visualization recommendation algorithms. IEEE Trans Vis Comput Graph2022; 28(1): 346–356.
18.
CoelhoDGhaiBKrishnaA, et al. Taskfinder: a semantics-based methodology for visualization task recommendation. Analytics2024; 3(3): 255–275.
19.
HuKBakkerMALiS, et al. Vizml: a machine learning approach to visualization recommendation. In: Proceedings of the 2019 CHI conference on human factors in computing systems. CHI ’19, 2019, pp. 1–12. New York, NY: Association for Computing Machinery. DOI: 10.1145/3290605.3300358.
20.
DibiaVDemiralpC.Data2vis: automatic generation of data visualizations using sequence-to-sequence recurrent neural networks. IEEE Comput Graph Appl2019; 39(5): 33–46.
21.
WangLZhangSWangY, et al. Llm4vis: Explainable visualization recommendation using chatgpt. In: Proceedings of the 2023 conference on empirical methods in natural language processing: industry track, 2023, pp. 675–692. DOI: 10.18653/v1/2023.emnlp-industry.64.
22.
VartakMHuangSSiddiquiT, et al. Towards visualization recommendation systems. ACM SIGMOD Rec2017; 45(4): 34–39.
23.
TuAMehtaPWuA, et al. Addressing bias in visualization recommenders by identifying trends in training data: improving vizml through a statistical analysis of the plotly community feed. ArXiv 2022; abs/2203.04937: 1–11.
24.
WangGTanBWangZ, et al. A machine learning approach to visual insight discovery in multidimensional hierarchical data. In: Proceedings of the 2022 5th international conference on pattern recognition and artificial intelligence (PRAI), 2022, pp. 1304–1310. DOI: 10.1109/PRAI55851.2022.9904175.
25.
KeyAHoweBPerryD, et al. Vizdeck: self-organizing dashboards for visual analytics. In: Proceedings of the 2012 ACM SIGMOD international conference on management of data. SIGMOD ’12, 2012, pp. 681–684. New York, NY: Association for Computing Machinery. DOI: 10.1145/2213836.2213931.
MoritzDWangCNelsonGL, et al. Formalizing visualization design knowledge as constraints: actionable and extensible models in draco. IEEE Trans Vis Comput Graph2018; 25(1): 438–448.
28.
LinHMoritzDHeerJ.Dziban: balancing agency & automation in visualization design via anchored recommendations. In: Proceedings of the 2020 CHI conference on human factors in computing systems. CHI ’20, 2020, pp. 1–12. New York, NY: Association for Computing Machinery. DOI: 10.1145/3313831.3376880.
29.
LeeDJSetlurVToryM, et al. Deconstructing categorization in visualization recommendation: a taxonomy and comparative study. IEEE Trans Vis Comput Graph2022; 28(12): 4225–4239.
30.
PoetzschTGermanakosPHuesteggeL.Toward a taxonomy for adaptive data visualization in analytics applications. Front Artif Intell2020; 3(9): 9.
31.
BaoCSLiSFloresSG, et al. Recommendations for visualization recommendations: Exploring preferences and priorities in public health. In: Proceedings of the 2022 CHI conference on human factors in computing systems. Number 411 in CHI ’22, 2022, pp. 1–17. New York, NY: Association for Computing Machinery. DOI: 10.1145/3491102.3501891.
32.
ZhuSSunGJiangQ, et al. A survey on automatic infographics and visualization recommendations. Vis Informatics2020; 4(3): 24–40.
33.
ZengZBattleL.A review and collation of graphical perception knowledge for visualization recommendation. In: Proceedings of the 2023 CHI conference on human factors in computing systems. Number 820 in CHI ’23, 2023, pp. 1–16. New York, NY: Association for Computing Machinery. DOI: 10.1145/3544548.3581349.
34.
KaurPOwonibiM.A review on visualization recommendation strategies. In Proceedings of the 12th international joint conference on computer vision, imaging and computer graphics theory and applications (VISIGRAPP 2017), 2017, Vol. 3, pp. 266–272. DOI: 10.5220/0006175002660273.
35.
KitchenhamBAChartersS.Guidelines for performing systematic literature reviews in software engineering. Technical Report EBSE 2007-001, Keele University and Durham University Joint Report, New York, NY, USA, 2007.
36.
WohlinC.Guidelines for snowballing in systematic literature studies and a replication in software engineering. In: Proceedings of the 18th international conference on evaluation and assessment in software engineering. Number 38 in EASE ’14, 2014, pp. 1–10. New York, NY: Association for Computing Machinery. DOI: 10.1145/2601248.2601268.
37.
MourãoEKalinowskiMMurtaL, et al. Investigating the use of a hybrid search strategy for systematic reviews. In: Proceedings of the 2017 ACM/IEEE international symposium on empirical software engineering and measurement (ESEM), 2017, pp. 193–198. DOI: 10.1109/ESEM.2017.30.
38.
FabbriSSilvaCHernandesE, et al. Improvements in the start tool to better support the systematic review process. In: Proceedings of the 20th international conference on evaluation and assessment in software engineering. Number 21 in EASE ’16, 2016, pp. 1–5. New York, NY: Association for Computing Machinery. DOI: 10.1145/2915970.2916013.
39.
ZhangHNingSZhengQ, et al. Towards visualizing electronic medical records via natural language queries, 2025. DOI: 10.48550/arXiv.2506.12837.
40.
MoensPVolckaertBVan HoeckeS.VisCARS: knowledge graph-based context-aware recommender system for time-series data visualization and monitoring dashboards. IEEE Trans Vis Comput Graph2025; 31(9): 4728–4745.
41.
GuMZhuJWangQ, et al. Intellicircos: a data-driven and AI-powered authoring tool for circos plots. Comput Graph Forum2025; 44(3): e70118.
42.
StahlbomEMolinJLundstromC, et al. Visualization for diagnostic review of copy number variants in complex dna sequencing data. IEEE Trans Vis Comput Graph2025; 31(4): 2211–2222.
43.
LiXLiaoJLiuW, et al. Cold-start visualization recommendation driven by large language models for ocean data analysis. Front Mar Sci2025; 12: 1–11. DOI: 10.3389/fmars.2025.1554241.
44.
DengDWuAQuH, et al. Dashbot: insight-driven dashboard generation based on deep reinforcement learning. IEEE Trans Vis Comput Graph2023; 29: 690–700.
45.
ZhangQ.A system of visualizing business chart using text mining techniques. In: 2019 4th IEEE international conference on cybernetics (Cybconf), 2019, pp. 1–6. DOI: 10.1109/Cybconf47073.2019.9436613.
46.
LiYQiYShiY, et al. Diverse interaction recommendation for public users exploring multi-view visualization using deep learning. IEEE Trans Vis Comput Graph2023; 29(1): 95–105.
47.
SarikayaACorrellMBartramL, et al. What do we talk about when we talk about dashboards?IEEE Trans Vis Comput Graph2018; 25(1): 682–692.
48.
ChenHJiangSYuX, et al. Geovis: a data-driven geographic visualization recommendation system via latent space encoding. J Vis2024; 27(4): 603–622.
49.
YiJSKangYAStaskoJ., et al. Toward a deeper understanding of the role of interaction in information visualization. IEEE Trans Vis Comput Graph2007; 13(6): 1224–1231.
50.
CockburnAKarlsonABedersonBB.A review of overview+detail, zooming, and focus+context interfaces. ACM Comput Surv2009; 41(1): 1–31.
LahmarHBHerschelM.Provenance-based recommendations for visual data exploration. In: Proceedings of the 9th USENIX conference on theory and practice of provenance. TaPP’17, 2017, pp. 686–689. USENIX Association. DOI: 10.5441/002/edbt.2018.85.
53.
AnanthanarayananRLohiaPKBedathurS. Datavizard: recommending visual presentations for structured data. In: Proceedings of the 21st international workshop on the web and databases. Number 3 in WebDB’18, 2018, pp. 1–6. New York, NY: Association for Computing Machinery. DOI: 10.1145/3201463.3201465.
54.
KubernátováPFriedjungováMVan DuijnM.Knowledge at first glance: a model for a data visualization recommender system suited for non-expert users. In: Proceedings of the 7th international conference on data science, technology and applications, 2018, pp. 208–219. DOI: 10.5220/0006851302080219.
55.
De StefaniLSpiegelbergLFUpfalE, et al. Vizcertify: a framework for secure visual data exploration. In: Proceedings of the 2019 IEEE international conference on data science and advanced analytics (DSAA), 2019, pp. 241–251. DOI: 10.1109/DSAA.2019.00039.
56.
ShiDShiYXuX, et al. Task-oriented optimal sequencing of visualization charts. In: Proceedings of the 2019 IEEE visualization in data science (VDS), 2019, pp. 58–66. DOI: 10.1109/VDS48975.2019.8973383.
57.
KubernátováPFriedjungováMVan DuijnM.Constructing a data visualization recommender system. Volume 862. Springer International Publishing, 2019, pp. 1–25. DOI: 10.1007/978-3-030-26636-3_1.
58.
L’YiSChangYShinD, et al. Toward understanding representation methods in visualization recommendations through scatterplot construction tasks. Comput Graph Forum2019; 38(3): 201–211.
59.
CuiWZhangXWangY, et al. Text-to-viz: automatic generation of infographics from proportion-related natural language statements. IEEE Trans Vis Comput Graph2020; 26: 906–916.
60.
NieSHealeyCGChirkovaRY, et al. Rapid sequence matching for visualization recommender systems. In: Proceedings of graphics interface 2019. GI 2019, 2019. Canadian Information Processing Society. DOI: 10.20380/GI2019.05.
61.
CuiZBadamSKYalçinMA, et al. Datasite: proactive visual data exploration with computation of insight-based recommendations. Inf Vis2019; 18(2): 251–267.
62.
ZhangXGeXChrysanthisPK.Evaluating query strategies for different feedback types in interactive view recommendation. In: Proceedings of the 2020 24th international conference information visualisation (IV), 2020, pp. 397–402. DOI: 10.1109/IV51561.2020.00070.
63.
MafrurRSharafMAZucconG. Quality matters: understanding the impact of incomplete data on visualization recommendation. In: Proceedings of the database and expert systems applications: 31st international conference, DEXA 2020, Proceedings, Part I, Bratislava, Slovakia, 14–17 September 2020, pp. 122–138. Berlin, Heidelberg: Springer International Publishing. DOI: 10.1007/978-3-030-59003-1_8.
64.
QianXRossiRADuF, et al. Ml-based visualization recommendation: learning to recommend visualizations from data. ArXiv 2020; abs/2009.12316: 1–17.
65.
Vázquez-IngelmoAGarcía-PeñalvoFJTherónR, et al. Connecting domain-specific features to source code: towards the automatization of dashboard generation. Cluster Comput2020; 23(3): 1803–1816.
66.
WangYSunZZhangH, et al. Datashot: automatic generation of fact sheets from tabular data. IEEE Trans Vis Comput Graph2020; 26(1): 895–905.
67.
QianXRossiRADuF, et al. Learning to recommend visualizations from data. In: Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data mining. KDD ’21, 2021, pp. 1359–1369, New York, NY: Association for Computing Machinery. DOI: 10.1145/3447548.3467224.
68.
Ben LahmarHHerschelM.Collaborative filtering over evolution provenance data for interactive visual data exploration. Inf Syst2021; 95: 1–16.
69.
ZhangXGeXChrysanthisPK, et al. Viewseeker: an interactive view recommendation framework. Big Data Res2021; 25(C): 100238.
70.
NarechaniaASrinivasanAStaskoJ.Nl4dv: a toolkit for generating analytic specifications for data visualization from natural language queries. IEEE Trans Vis Comput Graph2021; 27(2): 369–379.
71.
OppermannMKincaidRMunznerT.Vizcommender: computing text-based similarity in visualization repositories for content-based recommendations. IEEE Trans Vis Comput Graph2021; 27(2): 495–505.
72.
ZehrungRSinghalACorrellM, et al. Vis ex Machina: an analysis of trust in human versus algorithmically generated visualization recommendations. In: Proceedings of the 2021 CHI conference on human factors in computing systems. CHI ’21, 2021, pp. 1–12. New York, NY: Association for Computing Machinery. DOI: 10.1145/3411764.3445195.
73.
ZhouMLiQHeX, et al. Table2charts: recommending charts by learning shared table representations. In: Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data mining. KDD ’21, 2021, pp. 2389–2399. New York, NY: Association for Computing Machinery. DOI: 10.1145/3447548.3467279.
74.
NguyenNVPhamVDangT.Hmaviz: human-machine analytics for visual recommendation. In Proceedings of the 12th international conference on advances in information technology. Number 32 in IAIT2021, 2021, pp. 1–9. New York, NY: Association for Computing Machinery. DOI: 10.1145/3468784.3471601.
75.
SharafMAEhsanH.Efficient query refinement for view recommendation in visual data exploration. IEEE Access2021; 9: 76461–76478.
76.
ShiDXuXSunF, et al. Calliope: automatic visual data story generation from a spreadsheet. IEEE Trans Vis Comput Graph2021; 27(2): 453–463.
77.
WuAWangYZhouM, et al. Multivision: designing analytical dashboards with deep learning based recommendation. IEEE Trans Vis Comput Graph2022; 28(1): 162–172.
78.
QianXRossiRADuF, et al. Personalized visualization recommendation. ACM Trans Web2022; 16(3): 1–47.
79.
OjoFRossiRAHoffswellJ, et al. Visgnn: personalized visualization recommendation via graph neural networks. In: Proceedings of the ACM web conference 2022. WWW ’22, 2022, pp. 2810–2818. New York, NY: Association for Computing Machinery. DOI: 10.1145/3485447.3512001.
80.
LakPBozantaAKavakliogluC, et al. A replication study on implicit feedback recommender systems with application to the data visualization recommendation. Expert Syst2022; 39(4): 1–21. DOI: 10.1111/exsy.12871
81.
de CarvalhoBAMayr Lima MartinsDde Lima NetoFB. Mapview: exploring datasets via unsupervised view recommendation. In: Proceedings of the 2021 IEEE Latin American conference on computational intelligence (LA-CCI), 2021, pp. 1–6. DOI: 10.1109/LA-CCI48322.2021.9769785.
82.
LeeDJLTangDAgarwalK, et al. Lux: always-on visualization recommendations for exploratory dataframe workflows. Proc VLDB Endow2022; 15(3): 727–738.
83.
PandeyASrinivasanASetlurV.MEDLEY: intent-based recommendations to support dashboard composition. IEEE Trans Vis Comput Graph2023; 29: 1135–1145.
84.
LinYLiHWuA, et al. Dminer: dashboard design mining and recommendation. IEEE Trans Vis Comput Graph2024; 30: 4108–4121.
85.
LiLLiTHeX, et al. Asta: learning analytical semantics over tables for intelligent data analysis and visualization, 2022. DOI: 10.48550/arXiv.2208.01043.
86.
EppersonWJung-Lin LeeDWangL, et al. Leveraging analysis history for improved in situ visualization recommendation. Comput Graph Forum2022; 41(3): 145–155.
87.
MarutaAKatoMP.Intent-aware data visualization recommendation. Data Sci Eng2022; 7(4): 301–315.
88.
VijRRajRSinghalM, et al. Vizai: selecting accurate visualizations of numerical data. In: Proceedings of the 5th joint international conference on data science & management of data (9th ACM IKDD CODS and 27th COMAD). CODS-COMAD 2022, 2022, pp. 28–36, New York, NY: Association for Computing Machinery. DOI: 10.1145/3493700.3493717.
89.
ZhaoJFanMFengM.Chartseer: interactive steering exploratory visual analysis with machine intelligence. IEEE Trans Vis Comput Graph2022; 28(3): 1500–1513.
90.
CaoYRLiXHPanJY, et al. Visguide: user-oriented recommendations for data event extraction. In: Proceedings of the 2022 CHI conference on human factors in computing systems. Number 412 in CHI ’22, 2022, pp. 1–13. New York, NY: Association for Computing Machinery. DOI: 10.1145/3491102.3517648.
91.
LuoYQinXChaiC, et al. Steerable self-driving data visualization. IEEE Trans Knowl Data Eng2022; 34(1): 475–490.
92.
ChakrabartiAAhmadFJarkeM, et al. Monitoring large scale production processes using a rule-based visualization recommendation system. SN Comput Sci2022; 4(1): 1–24. DOI: 10.1007/s42979-022-01419-z.
93.
LiQLinHTangCF, et al. Exploring the “double-edged sword” effect of auto-insight recommendation in exploratory data analysis. In: Proceedings of the ACM IUI 2021 workshops, 2021. College Station, TX: CEUR Workshop Proceedings.
94.
RaghunandanDCuiZKrishnanK, et al. Lodestar: supporting rapid prototyping of data science workflows through data-driven analysis recommendations. Inf Vis2024; 23(1): 21–39.
95.
WuZChenWMaY, et al. Explainable data transformation recommendation for automatic visualization. Front Inf Technol Electron Eng2023; 24(7): 1007–1027.
96.
ZhangSLiHQuH, et al. Adavis: adaptive and explainable visualization recommendation for tabular data. IEEE Trans Vis Comput Graph2023; 30(9): 5923–5938. DOI: 10.1109/TVCG.2023.3316469.
97.
JiDLuoHBaoZ.Visualization recommendation through visual relation learning and visual preference learning. In: Proceedings of the 2023 IEEE 39th international conference on data engineering (ICDE), 2023, pp. 1860–1873. DOI: 10.1109/ICDE55515.2023.00145.
98.
SchmidtJPointnerBMikschS.Visual analytics for understanding Draco’s knowledge base. IEEE Trans Vis Comput Graph2023; 30(1): 1–11.
99.
ChangYL’YiSKimYT, et al. Pharos: a transparent and steerable visualization recommendation system. J Comput Sci Eng2023; 17(1): 1–12.
100.
SharafMAMafrurRZucconG.Efficient diversification for recommending aggregate data visualizations. IEEE Access2023; 11: 62261–62280.
101.
LiuJWanGDengP, et al. Dpvis: automatic visual encoding based on deep learning. IEEE Access2023; 11: 118078–118087.
102.
ShiYChenBChenY, et al. Supporting guided exploratory visual analysis on time series data with reinforcement learning. IEEE Trans Vis Comput Graph2024; 30: 1172–1182.
103.
GabaASetlurVSrinivasanA, et al. Comparison conundrum and the chamber of visualizations: an exploration of how language influences visual design. IEEE Trans Vis Comput Graph2023; 29(1): 1211–1221.
104.
LiXZhouJChenW, et al. Visualization recommendation with prompt-based reprogramming of large language models. 2024, pp. 13250–13262. DOI: 10.18653/v1/2024.acl-long.716.
105.
XieYLuoYLiG, et al. Haichart: human and AI paired visualization system. Proc VLDB Endow2024; 17(11): 3178–3191.
106.
ZhuJWuMZhouY, et al. Dowsing: a task-driven approach for multiple-view visualizations dynamic recommendation. J Vis2024; 27(4): 695–712.
107.
ZhangSWangLLiTJ, et al. Chartifytext: automated chart generation from data-involved texts via LLM. arXiv preprint arXiv:2410.14331, 2024; abs-2410-14331v2: 1–11. DOI: 10.48550/ARXIV.2410.14331.
108.
LiSChenXSongY, et al. prompt4vis: prompting large language models with example mining for tabular data visualization. VLDB J2025; 34: 38.
109.
PodoLAngeliniMVelardiP.V-recs, a low-cost llm4vis recommender with explanations, captioning and suggestions. ArXiv 2024; abs/2406.15259: 1–11.
110.
EliseevaUHeißSNazemiK.Query-to-vis: conceptualization of a broad-coverage automated visualization pipeline. In: 2024 28th international conference information visualisation (IV), 2024, pp. 174–179. DOI: 10.1109/IV64223.2024.00038.
111.
SharafMAHelalHZakiN, et al. Automated recommendation of aggregate visualizations for crowdfunding data. Algorithms2024; 17(6): 244.
112.
HuSRossiRAYuT, et al. Interactive visualization recommendation with HIER-SUCB. In: Proceedings of the ACM on web conference 2025. WWW ’25, 2025, pp. 313–321. New York, NY: Association for Computing Machinery. DOI: 10.1145/3696410.3714697.
113.
WangHWGordonMBattleL, et al. Dracogpt: extracting visualization design preferences from large language models. IEEE Trans Vis Comput Graph2025; 31(1): 710–720.
114.
TianYCuiWDengD, et al. Chartgpt: leveraging LLMs to generate charts from abstract natural language. IEEE Trans Vis Comput Graph2025; 31(3): 1731–1745.
115.
TianYDengDYangS, et al. Noteflow: recommending charts as sight glasses for tracing data flow in computational notebooks. ArXiv 2025; abs/2502.02326: 1–20.
116.
HuYWangJRahmanS.Lakevisage: towards scalable, flexible and interactive visualization recommendation for data discovery over data lakes. Proc VLDB Endow2025; 18(10): 3545–3558.
117.
ShawM.Writing good software engineering research papers. In: 25th international conference on software engineering, 2003, pp. 726–736. DOI: 10.1109/ICSE.2003.1201262.
118.
LamHBertiniEIsenbergP, et al. Empirical studies in information visualization: seven scenarios. IEEE Trans Vis Comput Graph2012; 18(9): 1520–1536.
119.
BellmanR.A Markovian decision process. J Math Mech1957; 6(5): 679–684.
120.
ChaSH.Comprehensive survey on distance/similarity measures between probability density functions. Int J Math Models Methods Appl Sci2007; 1(4): 300–307.
121.
VapnikVNChervonenkisAY.On the uniform convergence of relative frequencies of events to their probabilities. Springer International Publishing, 2015, pp. 11–30. DOI: 10.1007/978-3-319-21852-6_3.
122.
BastHMajumdarDSchenkelR, et al. Io-top-k: index-access optimized top-k query processing. In: Proceedings of the 32nd international conference on very large data bases. VLDB ’06, VLDB endowment, 2006, pp. 475–486.
123.
SchulzCZeyfangAGarderenM, et al. Simultaneous visual analysis of multiple software hierarchies. In: 2018 IEEE working conference on software visualization (VISSOFT), 2018, pp. 87–95. DOI: 10.1109/VISSOFT.2018.00017.
124.
GrassmannWKTaksarMIHeymanDP.Regenerative analysis and steady state distributions for markov chains. Oper Res1985; 33(5): 1107–1116.
125.
GrangerCWJ.Investigating causal relations by econometric models and cross-spectral methods. Econometr Soc1969; 37(3): 424–438.
126.
AichSVianneyJMUIslamMA, et al. Bidirectional attention network for monocular depth estimation. In: Proceedings of the 2021 IEEE international conference on robotics and automation (ICRA), 2021, pp. 11746–11752. DOI: 10.1109/ICRA48506.2021.9560885.
127.
AmarREaganJStaskoJ.Low-level components of analytic activity in information visualization. In: IEEE symposium on information visualization, 2005. INFOVIS 2005, 2005, pp. 111–117. DOI: 10.1109/INFVIS.2005.1532136.
128.
RussellDM.Simple is good: observations of visualization use amongst the big data digerati. In: Proceedings of the international working conference on advanced visual interfaces. AVI ’16, 2016, pp. 7–12. New York, NY: Association for Computing Machinery. DOI: 10.1145/2909132.2933287.
129.
KnaflicCN.Storytelling with data: a data visualization guide for business professionals. 1st ed.Wiley, 2015.
