Introduction of Information Visualization and Visual Analytics. Chapter 2. Introduction and Motivation

Transcription

1 Introduction of Information Visualization and Visual Analytics Chapter 2 Introduction and Motivation

2 Overview! 2 Overview and Motivation! Information Visualization (InfoVis)! InfoVis Application Areas! Visual Analytics! Visual Analytics Tools! Visual Analytics Application Areas 1

3 Acknowledgements!! Some contents in this chapter are taken from the Information Visualization for UbiComp Data Seminar by Prof. Dr. Aaron J. Quigley, University of St. Andrews, UK [ Examples and images are taken from many past research papers and online resources! 2

4 Information Visualization: Need! IBM hard drive in 1956! Weighted over 1 ton! 5 Mb capacity 3

5 Information Visualization: Need! Data collected and stored at enormous speeds! Data collection examples:! remote sensors on a satellite! telescopes scanning the skies! microarrays generating gene expression data! scientific simulations generating terabytes of data!. 4

6 Information Visualization: Need! Data collection is no longer a problem! however, extracting value from data stores into useful information has become increasingly difficult! Visualization links the human eye and computer, helping people to identify patterns and to extract insights from large amounts of data or information! Visualization techniques show promise for increasing the value in the large-scale collection of data and the generation of information and hence knowledge! 5

7 Information Visualization: Visualization defined! Previously visualization was defined as: constructing a visual image in the mind (Oxford English Dictionary, 1972)! An internal construct of the mind (tacit)! In the mind s eye! idiom! Now often taken to mean, a graphical representation of data or concepts 6

8 Information Visualization: Visualization defined! Previously visualization was defined as: constructing a visual image in the mind (Oxford English Dictionary, 1972)! An internal construct of the mind (tacit)! In the mind s eye! idiom! Now often taken to mean, a graphical representation of data or concepts! Now it s an external construct (explicit)! An external visual artifact supporting decision making! One purpose of visualization is insight, not pictures 7

9 Visualization: A broad field! Visualization or Data Visualization Scientific Visualization Information Visualization Computer Graphics 8

10 Scientific Visualization! Primarily relates to and represents something physical or geometric (e.g., physical phenomena)! Often 3-D! Examples! Architectural models! Air flow over a wing! Stresses on a girder! Torrents inside a tornado! Organs in the human body! Molecular bonding! etc. 9

11 Scientific Visualization: Examples 10

12 Information Visualization! What is information?! Items, entities, things which do not have a direct physical correspondence! Notion of abstractness of the entities is important too! Examples:! Baseball statistics! Stock trends! Connections between criminals! Relations between components in a software! Query results! Text of books! etc. 11

13 Information Visualization! In this context, what is visualization?! The use of computer-supported, interactive visual representations of data to amplify cognition. [Card, Mackinlay, Shneiderman 98]! Cognition! It is the process by which the sensory input is transformed, reduced, elaborated, stored, recovered, and used! In science, cognition is the mental processing that includes the attention of working memory, comprehending and producing language, calculating, reasoning, problem solving, and decision making. 12

14 Information Visualization Components:! Take items without a direct physical correspondence and map them to a 2-D or 3-D physical space (geometry)! Give information a visual representation that is useful for analysis, decision-making, gaining insight... and more 13

15 Information Visualization: Example! Consider one type of information, a graph! A graph G = (V, E) is composed of! a set V of vertices, also referred to as nodes! a set E of edges, that is, unordered pairs (a, b) of vertices! Edges are also referred to as links, arcs or connections! A graph has no geometry 14

16 Information Visualization: Example Geometry! A, B are nodes! A is connected to B by an edge! How do we draw this in 2D?! All of these drawings are equally valid! A A B A B B B A 15

17 Information Visualization: Example! The input is a graph with no geometry! The output is a drawing of the graph; the drawing should be nice B Relational Information A D C A-B A-C A-D B-A B-C B-D C-A C-B C-D C-E D-A D-B D-C D-E E-C E-D Graph Drawing Algorithm A B E C E D 16

18 Example Graph Drawing Force Directed Layout Algorithm 17

19 Example Graph Drawing Force Directed Layout Algorithm! A graph drawing through a number of iterations of a force directed layout algorithm 18

20 Example Graph Drawing Force Directed Layout Algorithm 19

21 Graph Drawing Example: Social Network 20

22 Graph Drawing Example: Software Visualization 21

23 Graph Drawing Example: Software Visualization Graphviz - Graph Visualization Software [ 22

24 InfoVis Application Areas! Information Visualization is applied in a number of areas, such as:! Journalism! Art! Entertainment! Story Telling! Exploration! Insight! etc. 23

25 Journalism The Evacuation Zones Around the Fukushima Daiichi Nuclear Plant: The New York Times, 25 March

26 Journalism 25

27 Art: The first movement, opens with a wildly swarming mass of around 1,500 particles, emanating from the center of the screen and then careening outwards, bouncing off walls and reacting to the behavior of the mouse. Each particle represents a single feeling, posted by a single individual. The color of each particle corresponds to the tone of the feeling inside happy positive feelings are bright yellow, sad negative feelings are dark blue, angry feelings are bright red, calm feelings are pale green, and so on. The size of each particle represents the length of the sentence contained within. Circular particles are sentences. Rectangular particles contain pictures. 26

28 Story Telling: health care cost rise ( ) 27

29 Story Telling: Napoleon s march map 28

30 Story Telling: Napoleon s march map 29

31 Story Telling: Napoleon s march map 30

32 Exploration paired parallel coordinates: an individual s social network extracted from Facebook 31

33 Insight! ESSAVis Tool [ AlTarawneh et al. : Enhancing Understanding of Safety Aspects in Embedded Systems through an Interactive Visual Tool. IUI Companion '14. ACM, 9-12, 2014.] 32

34 Visual Analytics! Visual Analytics is the science of analytical reasoning supported by interactive visual interfaces [Thomas, J., Cook, K.: Illuminating the Path: Research and Development Agenda for Visual Analytics. IEEE-Press (2005)]! Data is produced at an incredible rate! Challenge:! The ability to collect and store the data vs.! The ability to analyze it Collection & Storing Analyzing 33

35 Visual Analytics Methods! Visual Analytics Methods! Combine:! human flexibility, creativity, and background knowledge and! Aim:! the storing and processing capabilities of computer systems! to gain insight into complex problems 34

36 Visual Analytics Tools! The goal of visual analytics tools is to separate interesting data from non-interesting data! Visual analytics tools allow users to interactively search data sources for features of interest, special patterns, and unusual activity! The aim is to allow human users to make wellinformed decisions in complex situations 35

37 Visual Analytics: An Updated Definition! Visual analytics combines automated analysis techniques with interactive visualizations for an effective understanding, reasoning, and decision making on the basis of very large and complex datasets. [ Mastering the Information Age: Solving Problems with Visual Analytics, Eurographics Association (2010), Edited by Daniel Keim, Jörn Kohlhammer, Geoffrey Ellis] 36

38 Visual Analytics Tools vs. Visual Representation! In visual analytics tools:! Information is not only presented to the analyst in the Visual Environment (VE)! It is a dialogue between analyst and the data! similar to the one expect to occur between two human analysts! Visual representation in such an interaction is just an interface into or view of the data! In this case, the analyst:! observes the current data representation! interprets it! thinks of the next question to ask! formulates a strategy of how to continue the dialogue 37

39 Visual Analytics Workflow Pre- Processing Visual Mapping Visualization User Interaction User Interaction Input Data Data Selection Feedback Loop Selected Data Automated Analysis Visual Mapping Hypothesis Hypothesis Generation User Interaction Insight User Interaction [Keim et al., Visual Analytics: Scope and Challenges. In: Visual Data Mining, Simoff, Böhlen, Mazeika (eds.), Springer 2008.] 38

40 Visual Analytics! Building blocks of visual analytics research Infrastructure Human perception & cognition Data management Visualization Data mining Spatio-temporal data analysis Evaluation [ Mastering the Information Age: Solving Problems with Visual Analytics, Eurographics Association (2010), Edited by Daniel Keim, Jörn Kohlhammer, Geoffrey Ellis] 39

41 Visual Analytics! Scope of visual analytics [Keim et al., Visual Analytics: Scope and Challenges. In: Visual Data Mining, Simoff, Böhlen, Mazeika (eds.), Springer 2008.] 40

42 Visual Analytics: Application Areas! Physics and Astronomy! Business! Environmental monitoring! Disaster and emergency management! Security! Software analytics! Biology, medicine, and health! Engineering analytics! Personal information management! Mobile graphics and traffic! Etc. [Keim et al., Visual Analytics: Scope and Challenges. In: Visual Data Mining, Simoff, Böhlen, Mazeika (eds.), Springer 2008.] 41

43 Visual Analytics: Application Areas (cont.)! Physics and Astronomy:! Flow visualization, fluid dynamics, molecular dynamics, nuclear science, astrophysics, etc.! Date size:! Sloan Digital Sky Survey, COMPLETE: TB of data per day! Large Hadron Collider (LHC, CERN): 1 PB of data per year! Business! Stocks exchange behavior, market indices, etc.! Main challenges:! Analyze the data under multiple perspectives and assumptions! Understand historical and current situations! Forecast trends and identify recurring situations 42

44 Visual Analytics: Applications (cont.) [ 43

45 Visual Analytics: Application Areas (cont.)! Environmental Monitoring! Weather forecasts, global warming, hurricane warning, etc.! Disaster and Emergency Management! Planning countermeasures for natural or meteorological catastrophes, e.g.:! flood, waves, volcanoes, storm, fire, etc. 44

46 Visual Analytics: Application Areas (cont.) Visual support for the simulation of climate models provided by CGV (Coordinated Graph Visualization) [ Mastering the Information Age: Solving Problems with Visual Analytics, Eurographics Association (2010), Edited by Daniel Keim, Jörn Kohlhammer, Geoffrey Ellis] 45

47 Visual Analytics: Application Areas (cont.)! Software Analytics! Debugging, maintenance, restructuring, optimization! Engineering Analytics! flow visualization (e.g., air resistance of vehicles, air flow inside an engine), simulation of a car crash 46

48 Software Analytics Example [ Khan et al.: ecity+: A Visual Environment for Analying Software Structure and Evolution. AVI '14, ACM, 2014.] 47

49 Software Analytics Example [ Khan et al.: ecity+: A Visual Environment for Analying Software Structure and Evolution. AVI '14, ACM, 2014.] 48