1 Patent Application of Jinson Zhang, Edmund Zhang for THE 5Ws PATTERN-RATIO PARALLEL AXES INFORMATION TECHNOLOGY BACKGROUND [0001] We are living in an information age, and are faced with enormously complex amounts of information coming from every source in our daily lives. Such data, which is extremely large in volume, speed and complexity, is difficult for traditional statistical techniques to analyse. For example, millions of users posted comments on Facebook about their favourite players during the 2014 FIFA World Cup from different countries all over the world, and comments may have been sent from iPhone Apps or Internet browsers which contain text, image and/or video data. This creates multi-dimensional information based on statistical dimensions such as the sender's identity, the receiver's identity, the type of message and the vehicle of the message. [0002] It is hard to obtain proper statistics or analysis from this multi-dimensional information without strict classification. This has given rise to two challenges: firstly how to classify multi-dimensional information for better understanding and analysis, and secondly how to illustrate these statistical patterns in a visual structure. This invention defines the 5Ws classification for multi-dimensional information, and uses the existing parallel axes for visualization to draw and identify statistical patterns. Parallel axes were introduced by Alfred Inselberg in 1985 for parallel coordinate visualization, where set-lines were drawn between parallel axes to represent visual relationships between attributes according to the dataset. Different points along each axis represent different attributes of the same dimension. The parallel axis system, however, was not used to represent statistical patterns. 1 2 [0003] No previous work has previously defined our 5Ws dimensions nor designed the 5Ws dimensions as the parallel axes, and neither has any previous work designed statistical results or a Pattern-Ratio as an additional axis for displaying statistical patterns. SUMMARY [0001] The 5Ws Pattern-Ratio Parallel Axes are a visualisation and analytical tool used to illustrate information patterns. The 5Ws are a set of dimensions for any set of statistics, and stand for; When the information occurred, Where the information came from, What the information contained or described, How the information was conducted, Why the information occurred and Who the information affected. Each piece of information can therefore be described through the 5Ws dimensions, and each dimension is given its own axis by the parallel axis coordinate system. Our patent introduces one more axis, called the Pattern-Ratio Axis. The Pattern-Ratio describes the number of occurrences of a particular set of dimensions. It gives the number of times a particular set of attributes occurs as a percentage of the entire sample space of information. Lines called set-lines can be drawn across the axes to represent the 5Ws dimensions as well as the number of times this particular set of dimensions occurs. [0002] Through the 5Ws Pattern-Ratio Parallel Axes, the observer can gain valuable information about the sample space without overly cluttering the graph with unnecessary lines. For example, the total occurrences of a particular attribute are found by summing the occurrences of the individual lines that pass through that point. The 5Ws Pattern-Ratio Parallel Axes is therefore an efficient and effective way to illustrate information patterns, especially on a larger scale, and allows the observer to easily make connections between different dimensions. The 5Ws Pattern-Ratio Parallel Axes system is easy to read and analyse but does not lead to the loss of any information and maintains accuracy. 2 3 SUMMARY DRAWINGS [0001] Fig. 1 is the invention of 5Ws Pattern-Ratio Parallel Axes. [0002] Fig. 2 is the examples of 5Ws Pattern-Ratio Parallel Coordinates where Pattern-Ratio axis aside of 'Who' dimension. [0003] Fig. 3 is the examples of 5Ws Pattern-Ratio Parallel Coordinates where Pattern-Ratio axis aside of 'Why' dimension. [0004] Fig. 4 is the examples of 5Ws Pattern-Ratio Parallel Coordinates where Pattern-Ratio axis aside of 'Where' dimension. 3 4 DESCRIPTION OF EMBODIMENTS [0001] Fig. 1 shows the 5Ws Pattern-Ratio Parallel Axes which contains the 5Ws axes plus one Pattern-Ratio axis on the right hand side. The 5Ws axes are dimensions for 'When', 'Where', 'What', 'How', 'Why' and 'Who'. Each of the 5Ws axes can be numbers or characters to represent its dimensional values. The 'Pattern-Ratio' axis describes the number of occurrences of a particular set of dimensions. It is given as a percentage of the number of times a particular set of dimensions occurs relative to the entire sample space of information. It allows a comparison for statistical results in a visual structure. [0002] Table 1 shows an example of the 5Ws dimensional information and its statistics. The dimension 'When' indicates one element '2015'. The dimension 'Where' has two elements "Sydney" and "Melbourne". The dimensions 'What' has one element 'University Students'. The dimension 'How' has one element 'Full-time'. The dimension 'Why' has two elements 'Business' and 'Law'. The dimension "Who" has two elements "Male" and "Female". The eight different Patterns-Ratios are calculated inside the table. TABLE 1 When Where What How Why Who Total Pattern Ratio 1 2015 Sydney University Full- Business Male 12 =12/124 Students time =9.677% 2 2015 Sydney University Full- Business Female 13 =13/124 Students time =10.484% 3 2015 Sydney University Full- Law Male 14 =14/124 Students time =11.290% 4 2015 Sydney University Full- Law Female 15 =15/124 Students time =12.097% 5 2015 Melbourne University Full- Business Male 16 =16/124 Students time =12.903% 6 2015 Melbourne University Full- Business Female 17 =17/124 Students time =13.710% 7 2015 Melbourne University Full- Law Male 18 =18/124 Students time =14.516% 8 2015 Melbourne University Full- Law Female 19 =19/124 Students time =15.323% [0003] Fig. 2 shows an example in applying the 5Ws Pattern-Ratio Parallel Axes where the Pattern-Ratio Axis is located on the side of the 'Who' dimension (i.e. on the right-most side). It illustrates the visual statistical structure for the elements 'Female' and 'Male', allowing us to analyse each of the 'Who' attributes. From the graph, we see that four polylines pass 4 5 through the 'Female' attribute, the sum of which adds up to a total ratio of 51.613%. Repeating this process for the 'Male' attribute, we can therefore deduce that 48.387% of the sample space is male. [0004] Fig. 3 shows the example in applying the 5Ws Pattern-Ratio Parallel Axes where the Pattern-Ratio Axis located on the side of the 'Why' dimension. By shifting the position of the Pattern-Ratio axis, we now can illustrate the visual statistical structure for the elements 'Business' and 'Law'. As per the graph, by summing the set-lines passing through each attribute, we can deduce that 46.774% of all students study 'Business' and 53.226% study 'Law'. [0005] Fig. 3 shows a final example example in applying the 5Ws Pattern-Ratio Parallel Axes where the Pattern-Ratio Axis located on the side of the 'Where' dimension. This allows us to find the proportion of the students who live in 'Sydney' and 'Melbourne'. By adding each element, more students live in 'Melbourne' with 56.452% of students having this attribute, compared to 43.548 who live in 'Sydney'. [0006] These simple cases can be extended to situations involving millions of data incidents, each of which contain hundreds of dimensions. In these complex situations, the 5Ws Pattern Ratio Parallel Axes can provide a visualisation and analytical tool that normal statistical tools cannot compute, simply due to the enormous workload required. Our invention can also be used as an add-on chart in spreadsheets in order to provide more visualization tools for statistical analysis of business, information technology or scientific data. As an information visualization method in data analysis, the 5Ws Pattern-Ratio Parallel Axes system has great potential to meet business, government and organisation needs. 5 6 CITATIONS [0001]Alfred Inselberg (1985), "The Plane with Parallel Coordinates", Visual Computer, vol. 1, no 4, pp. 69-91 [0002]Wikipedia, "Five Ws", http://en.wikipedia.org/wiki/FiveWs [0003] Jinson Zhang and Mao Lin Huang (2013), "5Ws Model for BigData Analysis and Visualization", 2013 IEEE 16th International Conference on Computational Science and Engineering (CSE), pp. 1021-1028, 3-5 Dec 2013, DOI: 10.1109/CSE.2013.149 [0004] Jinson Zhang and Mao Lin Huang (2014), "Density approach: a new model for BigData analysis and visualization", Concurrency Computation Practice and Experience, online 1 July 2014, DOI: 10.1002/cpe.3337 [0005] Jinson Zhang, Mao Lin Huang, Wen Bo Wang, Liang Fu Lu, Zhaopeng Meng, "Big Data Density Analytics using Parallel Coordinate Visualization", 2014 IEEE 17th International Conference on Computational Science and Engineering (CSE), pp. 1115-1120, 19-21 Dec 2014, DOI: 10.1109/CSE.2014.219 [0006] Jinson Zhang, Mao Lin Huang, Zhaopeng Meng, "BigData visualization: Parallel coordinates using density approach", 2014 2nd International Conference on Systems and Informatics (ICSAI), pp. 1056-1063, 15-17 Nov 2014, DOI: 10.1109/ICSAI.2014.7009441 6