Transcript Lecture 31
Introduction to Data Visualization Definition of Data Visualization Terms related to Data Visualization Data Mining Data Recovery Data Redundancy Data Acquisition Data Validation Data Integrity Data Verification Data Aggregation Continued…. Data mining analytic process designed to explore data analyzing data from different perspectives summarizing it into useful information Data recovery handling the data through the data from damaged, failed, corrupted, or inaccessible secondary storage media recovery required due to physical damage to the storage device or logical damage to the file system Continued…. Data redundancy additional to the actual data permits correction of errors Data acquisition process of sampling signals measure real world physical conditions converting the resulting samples into digital numeric values Data validation process of ensuring that a program operates on clean, correct and useful data Continued…. Data integrity maintaining and assuring the accuracy and consistency of data ensure data is recorded exactly as intended Data verification different types of data are checked for accuracy and inconsistencies after data migration is done Data aggregation information is gathered and expressed in a summary form to get more information about particular groups Continued…. Need for data visualization Importance of data visualization Limitation of spreadsheet Interpretation through data visualization identify areas that need attention or improvement understand what factors influence design system predict how to change system design accordingly predict the efficiency of system Interactive Visualization Humans interact with computers to create graphic illustrations of information Process can be made more efficient Human input Response time Continued…. Combination of disciplines data visualization to provide a meaningful solution requires insights from diverse fields like statistics, data mining, graphic design, and information visualization software-based information visualization adds building blocks for interacting with and representing various kinds of abstract data Continued…. Process of data visualization Acquire Parse Filter Mine Represent Refine Interact Acquire Obtain the data, whether from a file on a disk or a source over a network Parse Provide some structure for the data’s meaning, and order it into categories Filter Remove all but the data of interest Mine Apply methods from statistics or data mining as a way to discern patterns or place the data in mathematical context Represent Choose a basic visual model, such as a bar graph, list, or tree. Refine Improve the basic representation to make it clearer and more visually engaging. Interact Add methods for manipulating the data or controlling what features are visible. Continued…. Iteration and Combination of steps of data visualization Unique requirements for each project each data set is different the point of visualization is to expose that fascinating aspect of the data and make it self-evident readily available representation toolkits are useful starting points they must be customized during an in-depth study of the task Continued…. Avoid usage of excess data Audience of problem Quantitative messages Time-Series Ranking Part-to-Whole Deviation Frequency-Distribution Correlation Nominal Comparison Geographic or Geospatial Time-series: A single variable is captured over a period of time, such as the unemployment rate over a 10-year period. A line chart may be used to demonstrate the trend Ranking: Categorical subdivisions are ranked in ascending or descending order, such as a ranking of sales performance by sales persons during a single period A bar chart may be used to show the comparison across the sales persons Part-to-whole: Categorical subdivisions are measured as a ratio to the whole A pie chart or bar chart can show the comparison of ratios, such as the market share represented by competitors in a market Deviation: Categorical subdivisions are compared again a reference, such as a comparison of actual vs. budget expenses for several departments of a business for a given time period A bar chart can show comparison of the actual versus the reference amount Frequency distribution: Shows the number of observations of a particular variable for given interval, such as the number of years in which the stock market return is between intervals such as 0-10%, 1120%, etc. A histogram, a type of bar chart, may be used for this analysis A boxplot helps visualize key statistics about the distribution, such as mean, median, quartiles, etc. Correlation: Comparison between observations represented by two variables (X,Y) to determine if they tend to move in the same or opposite directions For example, plotting unemployment (X) and inflation (Y) for a sample of months. A scatter plot is typically used for this message Nominal comparison: Comparing categorical subdivisions in no particular order, such as the sales volume by product code A bar chart may be used for this comparison Geographic or geospatial: Comparison of a variable across a map or layout, such as the unemployment rate by state or the number of persons on the various floors of a building A cartogram is a typical graphic used Continued…. Characteristics of effective graphical display show the data avoid distorting what the data have to say present many numbers in a small space make large data sets coherent encourage the eye to compare different pieces of data reveal the data at several levels of detail, from a broad overview to the fine structure serve a reasonably clear purpose: description, exploration, tabulation or decoration be closely integrated with the statistical and verbal descriptions of a data set Continued…. Visual perception and data visualization Effective graphics take advantage of pre-attentive processing and attributes and the relative strength of these attributes Types of information display Tables Graphs Data display requires planning Data collection Benefits of data visualization Visualization is so powerful and effective that it can change someone’s mind in a flash it encompasses various dataset quickly, effectively and efficiently and makes it accessible to the interested viewers It motivates us to a deep insight with quick access It gives us opportunity to approach huge data and makes it easily comprehensible, be it the field of entertainment, current affairs, financial issues or political affairs It also builds in us a deep insight, prompting us to take a good decision and an immediate action if needed It has emerged in the business world lately as geospatial visualization The popularity of geo-spatial visualization has occurred due to lot of websites providing web services, attracting visitor’s interest Data Visualization with C++ Chapter 1 “Arrays, Pointers and Structures” Chapter 2 “Objects and Classes” Chapter 4 “Inheritance” Chapter 6 “Algorithm Analysis” Chapter 1"Arrays, Pointers and Structures" In this chapter we examined the basics of pointers, arrays, and structures The pointer variable emulates the real-life indirect answer. In C++ it is an object that stores the address where some other data reside. The pointer is special because it can be dereferenced, thus allowing access to those other data The NULL pointer holds the constant 0, indicating that it is not currently pointing at valid data A reference parameter is an alias. It is like a pointer constant, except that the compiler implicitly dereferences it on every access Reference variables allow three forms of parameter passing: call by value, call by reference, and call by constant reference Choosing the best form for a particular application is an important part of the design process Continued…. An array is a collection of identically typed objects In C++ there is a primitive version with second-class semantics A vector is also part of the standard library In both cases, no index range checking is performed, and out- of-bounds array accesses can corrupt other objects. Because primitive arrays are second-class, they cannot be copied by using the assignment operator Instead they must be copied element by element; however, a vector can be copied in a single assignment statement A vector can be expanded as needed by calling resize Continued…. Structures are also used to store several objects, but unlike arrays, the objects need not be identically typed Each object in the structure is a member, and is accessed by the . member operator The -> operator is used to access a member of a structure that is accessed indirectly through a pointer We also noted that a list of items can be stored noncontiguously by using a linked list The advantage is that less space is used for large objects than in the array-doubling technique The penalty is that access of the ith item is no longer constanttime but requires examination of i structures Chapter 2 “Objects and Classes" In this chapter we described the C++ class construct The class is the C++ mechanism used to create new types. Through it we can define construction and destruction of objects, define copy semantics, define input and output operations, overload almost all operators, define implicit and explicit type conversion operations (sometimes a bad thing) provide for information hiding and atomicity The class consists of two parts: the interface and the implementation The interface tells the user of the class what the class does. The implementation does it The implementation frequently contains proprietary code and in some cases is distributed only in precompiled form Continued…. Information hiding can be enforced by using the private section in the interface Initialization of objects is controlled by the constructor functions, and the destructor function is called when an object goes out of scope The destructor typically performs clean up work, closing files and freeing memory Finally, when implementing a class, the use of const and correct parameter passing mechanisms, as well as the decision about whether to accept a default for the Big Three, write our own Big Three, or completely disallow copying is crucial for not only efficiency but also in some cases, correctness