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Big data visualization
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- 2. Content • Introduction • The3v’s of Big Data • Big Data Life Cycle • Role of Visualization in Big Data • Importance of Data Visualization • Design Principle • Steps for Interactive Data Visualization • Visualization Techniques • Make Visualization more interactive • Visualization Challenges • References
- 3. Introduction • Data: Anypiece of Information formatted in a special Way Stats… Tabular Data… Different Forms of Data …
- 4. Three V’s ofData Volume Variety Velocity • Scale of data • Amount of data • Different forms of Data • Speed of Generation • Rate of analysis
- 5. What is BigData • "Big Data are high-volume, high-velocity, and/or high-variety information assets that require new forms of processing to enable enhanced decision making, insight discovery and process optimization” (Gartner 2012) • Complicated (intelligent) analysis of data may make a small data “appear” to be “big” • Any data that exceeds our current capability of processing can be regarded as “big”
- 6. Why Big Dataa “big Deal” • Private Sector • Walmart handles more than 1 million customer transactions every hour, which is imported into databases estimated to contain more than 2.5 petabytes of data • Facebook handles 40 billion photos from its user base. • Falcon Credit Card Fraud Detection System protects 2.1 billion active accounts world-wide • Science • Large Synoptic Survey Telescope will generate 140 Terabyte of data every 5 days. • Biomedical computation like decoding human Genome & personalized medicine • Social science revolution
- 7. Visualization visualization is theprocess of displaying data/information in graphical charts, figures and bars.
- 8. What is BigData Visualization ?? • Big Data visualization is representing data in some systematic form including attributes and variables for the unit of information • It uses more interactive, graphical illustrations - including personalization and animation - to display figures and establish connections among pieces of information • It refers to the implementation of more contemporary visualization techniques to illustrate the relationships within data
- 9. Big Data LifeCycle • Generic process model, Big data analytics processes based on building blocks • Some building blocks can be skipped, depending on the operating contexts and to go back (two-way street) is admitted Collection Cleaning Integration Visualization Analysis Presentation Dissemination
- 10. Role of Visualizationin Big Data Life Cycle • Data visualization can play a specific role in several phases of the Big Data Life Cycle • Data types can affect visualization design • Visualization methods can informs data cleaning and the choice of analysis algorithms Along the Big Data life cycle, visualization methods can be properly incorporated in three phases: • Pre-processing, staging, handling • Exploratory data analysis • Presentation of analytical results
- 11. Why Data VisualizationImportant • The human brain processes information much easily , using charts or graphs to visualize large amounts of complex data. • It is a quick, easy way to convey concepts in a universal manner. • We can experiment with different scenarios by making slight adjustments. • It become easy to predict the future possibilities.
- 12. Design Principle • Objective •Think about the content • Data • Numerical : Values measure Something • Continuous : Continuity of values • Discrete : Discrete set of values • Categorical : Values encode a classification • Ordinal : Category naturally ordered • Nominal : Categories unordered • Audience • Get to know the audience
- 13. Steps to InteractiveData Visualization • Step 1: • Step 2: • Step 3: • Step 4: • Step 5: • Step 6: • Step 7: • Step 8: • Step 9: Identify Desired Goals Understand Data Constraints Design Conceptual Model Source & Model Data Design the User Interface Build Core Technology User Test and Refine Launch to Targeted Audience Stay Updated
- 14. Dedicated big datavisualization techniques Word Cloud • Displays how frequently words appear in a given body of text • Words in cloud are of different types • More the size- higher the frequency • Used for sentiment analysis of customer’s social media posts
- 15. Symbol Maps • Mapswith symbol • Symbol differ in size, easy to compare • Used by companies to know the popularity of their product in different areas
- 16. Connectivity Charts • Showsthe links b/w phenomena or events • Based on Connected Graphs theory • Fig shows the connections between machinery failures and their triggers
- 17. Visualization techniques thatwork for both traditional and big data Line Charts • It looks behavior of one or several variable over time • It identify the trends between variables. For traditional • Shows sales, profit, revenue of last 12 months For Big Data • Tracks avg. no. of complaints to call center. • Total application click by weeks
- 18. Heat Maps • Two-dimensionalrepresentation of data • Use Color to represent Data • provides an immediate visual summary of information • More elaborate heat maps allow the viewer to understand complex data sets
- 19. Bar Charts • Itallow comparing the values of different variables. • Graph represents categories on one axis and a discrete value in the other. • The goal is to show the relationship between the two axes. • can also show big changes in data over time.
- 20. Pie Charts • Itis a circular statistical graphic. • It is divided into slices to illustrate numerical proportion • Arc length proportional to quantity it represents.
- 21. Making Visualization moreInteractive Visualization can be interactive rather than static.. • helps users adjust the amount of information for displayFiltering • very effective in producing different insights.Rearranging • Zoom In & Zoom OutZooming • Interactive selection of data entitiesSelecting • useful for relating information among multiple viewsLinking
- 22. Visualization Challenges • Visualnoise: Most of the objects in dataset are too relative to each other. Users cannot divide them as separate objects on the screen. • Information loss: Reduction of visible data sets can be used, but leads to information loss. • Large image perception: Data visualization methods are not only limited by aspect ratio and resolution of device, but also by physical perception limits. • High rate of image change: Users observe data and cannot react to the number of data change or its intensity on display. • High performance requirements: It can be hardly noticed in static visualization because of lower visualization speed requirements--high performance requirement.
- 23. Benefits : DataVisualization • Improved Decision-making • Better ad-hoc data analysis • Improved collaboration/information sharing • Time savings • Increased return of investment (ROI) • Time savings • Reduced burden on IT
- 24. References • https://www.promptcloud.com/blog/design-principles-for- effective-data-visualization • https://www.idashboards.com/blog/2017/07/26/data- visualization-and-the-9-fundamental-design-principles/ •https://www.irjet.net/archives/V4/i1/IRJET-V4I182.pdf • http://pubs.sciepub.com/dt/1/1/7/
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