This document discusses principles of big data visualization. It explains why visualization is important for exploring trends, clusters, anomalies and patterns in large datasets. Techniques discussed for visualizing large amounts of data include aggregation methods like sampling, binning and clustering to reduce data size, as well as pixel-based techniques and focus+context approaches to maximize display capacity. The document also covers challenges of visualizing large data like occlusion, slow response times and maintaining clarity and truthfulness.

1. PRINCIPLESOF BIG DATA ANALYTICS
VISUALIZATION
Matej Novotný

3. WHY VISUALIZE?

4. Predmet Priemer bodov
Poľský jazyk I 57
Poľský jazyk II 60
Dejepis 54
Náuka o spoločnosti 54
Matematika I 60
Matematika II 50
Biológia 48

7. DAVID McCANDLESS, INFORMATION IS BEAUTIFUL

8. EXPLORATION
CONFIRMATION
PRESENTATION

9. TRENDS CLUSTERS ANOMALIES

10. VISUALIZATIONTO THE RESCUE

11. PATTERNS
INTUITION
EXPERTISE

12. POWER
MEMORY
PRECISION

14. DATA FLOWHAS ALWAYSBEEN
INCREASING IN SIZE

15. WHAT’S
“BIG DATA”
AND WHAT’S
“NOT-SO-BIGDATA”

16. RECURSIVE PATTERN: A TECHNIQUE FORVISUALIZING
VERY LARGE AMOUNTS OF DATA
KEIM et al. , IEEE VISUALIZATION 1995

17. PRINCIPLESOF BIG DATA ANALYTICS
VISUALIZATION
Matej Novotný

18. PRINCIPLESOF LARGE DATA
VISUALIZATION
Matej Novotný

19. WHAT IS “LARGE”?
- IN DATABASES
- IN DATA TRANSFER
- IN DATA VISUALIZATION

20. 250.000 FILMOV × 900.000 HERCOV

21. AGGREGATION
OCCLUSION
SLOW RESPONSE
CLARITY
TRUTHFULNESS
INTERACTIVITY
VS.

22. ANALYZINGRELEASED NYCVALUE-ADDED DATA,
GARY RUBINSTEIN

23. SOLUTIONS:
INCREASE CAPACITY,
REDUCE DATA,
MODIFY DRAWING

24. INCREASE
DISPLAYCAPACITY

25. REALITY TOUCH THEATRE
UNIVERSITY OF GRONINGEN

26. PIXEL-BASED
TECHNIQUES

27. VISUAL EXPLORATION AND
DISCOVERY OF ATYPICAL
BEHAVIOR IN
FINANCIAL TIME SERIES DATA
USING TWO-DIMENSIONAL
COLORMAPS,
ZIEGLER ET AL., IV’07

28. INTERACTIVE INFORMATION VISUALIZATION
OF AMILLIONITEMS,
FEKETE & PLAISANT, IEEE INFOVIS 2002

29. OPACITY,
PIXEL DEPTH

30. 16.000 ITEMS

31. 100.000 ITEMS

32. REVEALING STRUCTURE WITHIN CLUSTERED PARALLEL COORDINATES DISPLAYS
JOHANSSON ET AL., IEEE INFOVIS 2005

33. FOCUS+CONTEXT

34. FISHEYETREE VIEWS AND LENSES FOR
GRAPH VISUALIZATION
TOMINSKI ET AL., IV’06

35. REPRESENTATION-INDEPENDENT IN-PLACE
MAGNIFICATION WITH SIGMA LENSE,
PIETRIGA ET AL., IEEE TVCG VOL. 16(3)

36. VISUALIZING HIERARCHICAL TIME SERIES WITH A FOCUS+CONTEXT APPROACH
CUENCA ET AL., IEEE VIS 2017

37. EDGE BUNDLING

38. HIERARCHICAL EDGE BUNDLES: VISUALIZATION OF ADJACENCY RELATIONS IN HIERARCHICAL DATA
DANNY HOLTEN, TVCGVOL. 12(5)

40. SPATIAL VISUALIZATION OF GRAPH STRUCTURES
ĽUBOMÍRLÁBAJ

41. DATA REDUCTION:
SAMPLING

43. SAMPLING ERRORS
SAMPLING BIAS,
OUTLIERS

44. DATA AGGREGATION:
CLUSTERING

47. HIERARCHICAL PARALLELCOORDINATES FOREXPLORATION OF LARGE DATASETS
FUA, WARD, RUNDENSTEINER, VISUALIZATION'99

48. TED DATA ANALYSIS
ROUNAK BANIK
LEVEL-OF-DETAIL VISUALIZATION OF CLUSTERED GRAPH LAYOUTS
BALZER &DEUSSEN, APVIS 2007

49. CLUSTERING ← DISTANCE
CURSE OF DIMENSIONALITY

50. DATA AGGREGATION:
BINNING

52. HTTPS://WWW.CHRISSTUCCHIO.COM/BLOG/2012/DONT_USE_SCATTERPLOTS.HTML

53. HTTPS://WWW.CHRISSTUCCHIO.COM/BLOG/2012/DONT_USE_SCATTERPLOTS.HTML

54. BINNING ← EACH DIMENSION
ND COMPLEXITY

55. OUTLIERS IN AGGREGATION

56. WORKFLOW

57. WORKFLOW

58. OUTLIER-PRESERVING FOCUS+CONTEXT VISUALIZATION IN PARALLEL COORDINATES
NOVOTNY &HAUSER, IEEE VIS 2006

59. INTERACTIVE DISPLAYOF 3.000.000 RECORDS, 16 DIMENSIONS

60. LARGE DATA VISUALIZATION:
DATA + DISPLAY= SOLVED

61. FUTURE VIS CHALLENGES:
MACHINE LEARNING,
DATA & VISUALIZATIONSKILLS,
MULTI-MODAL DATA,
RELIABILITY VISUALIZATION

62. Matej Novotný
QUESTIONS?

63. PRINCIPLESOF LARGE DATA
VISUALIZATION
Matej Novotný