Principles of Big Data Analytics Visualization
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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.
Principles of Big Data Analytics Visualization
- 1. PRINCIPLESOF BIG DATA ANALYTICS VISUALIZATION Matej Novotný
- 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
- 14. DATA FLOWHAS ALWAYSBEEN INCREASING IN SIZE
- 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
- 22. ANALYZINGRELEASED NYCVALUE-ADDED DATA, GARY RUBINSTEIN
- 25. REALITY TOUCH THEATRE UNIVERSITY OF GRONINGEN
- 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
- 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
- 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
- 54. BINNING ← EACH DIMENSION ND COMPLEXITY
- 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
- 63. PRINCIPLESOF LARGE DATA VISUALIZATION Matej Novotný