VISUALIZATION 

 Visualization or visualisation ( see spelling differences ) is any technique for   creating images , diagrams , or animations to communicate a message . Visualization through   visual imagery has been an effective way to communicate both abstract and concrete ideas   since the dawn of humanity . Examples from history include cave paintings , Egyptian   hieroglyphs , Greek geometry , and Leonardo da Vinci 's revolutionary methods of technical   drawing for engineering and scientific purposes . 

 Visualization today has ever - expanding applications in science , education , engineering ( e.g. ,   product visualization ) , interactive multimedia , medicine , etc . Typical of a visualization   application is the field of computer graphics . The invention of computer graphics ( and 3D   computer graphics ) may be the most important development in visualization since the   invention of central perspective in the Renaissance period . The development of animation also   helped advance visualization . 

 APPLICATION 

 Scientific visualization   As a subject in computer science , scientific visualization is the use of interactive , sensory   representations , typically visual , of abstract data to reinforce cognition , hypothesis building ,   and reasoning . Scientific visualization is the transformation , selection , or representation of data   from simulations or experiments , with an implicit or explicit geometric structure , to allow the   exploration , analysis , and understanding of the data . Scientific visualization focuses and   emphasizes the representation of higher order data using primarily graphics and animation   techniques.[5][6 ] It is a very important part of visualization and maybe the first one , as the   visualization of experiments and phenomena is as old as science itself . Traditional areas of   scientific visualization are flow visualization , medical visualization , astrophysical visualization ,   and chemical visualization . There are several different techniques to visualize scientific data ,   with isosurface reconstruction and direct volume rendering being the more common . 

 Information visualization   Information visualization concentrates on the use of computer - supported tools to explore large   amount of abstract data . The term " information visualization " was originally coined by the User   Interface Research Group at Xerox PARC and included Jock Mackinlay.[citation needed ] Practical   application of information visualization in computer programs involves selecting , transforming ,   and representing abstract data in a form that facilitates human interaction for exploration and   understanding . Important aspects of information visualization are dynamics of visual   representation and the interactivity . Strong techniques enable the user to modify the   visualization in real - time , thus affording unparalleled perception of patterns and structural   relations in the abstract data in question . 

 Educational visualization 

 Educational visualization is using a simulation to create an image of something so it can be   taught about . This is very useful when teaching about a topic that is difficult to otherwise see ,   for example , atomic structure , because atoms are far too small to be studied easily without   expensive and difficult to use scientific equipment . 

 Knowledge visualization[edit ] 

 The use of visual representations to transfer knowledge between at least two persons aims to   improve the transfer of knowledge by using computer and non - computer - based visualization   methods complementarily.[8 ] Thus properly designed visualization is an important part of not   only data analysis but knowledge transfer process , too.[9 ] Knowledge transfer may be   significantly improved using hybrid designs as it enhances information density but may   decrease clarity as well . For example , visualization of a 3D scalar field may be implemented   using iso - surfaces for field distribution and textures for the gradient of the field.[10 ] Examples of   such visual formats are sketches , diagrams , images , objects , interactive visualizations ,   information visualization applications , and imaginary visualizations as in stories . While   information visualization concentrates on the use of computer - supported tools to derive new   insights , knowledge visualization focuses on transferring insights and creating   new knowledge in groups . Beyond the mere transfer of facts , knowledge visualization aims to   further transfer insights , experiences , attitudes , values , expectations , perspectives , opinions ,   and predictions by using various complementary visualizations . See also : picture   dictionary , visual dictionary 

 Product visualization[edit ] 

 Product visualization involves visualization software technology for the viewing and   manipulation of 3D models , technical drawing and other related documentation of   manufactured components and large assemblies of products . It is a key part of product lifecycle   management . Product visualization software typically provides high levels of photorealism so   that a product can be viewed before it is actually manufactured . This supports functions ranging   from design and styling to sales and marketing . Technical visualization is an important aspect of   product development . Originally technical drawings were made by hand , but with the rise of   advanced computer graphics the drawing board has been replaced by computer - aided   design ( CAD ) . CAD - drawings and models have several advantages over hand - made drawings   such as the possibility of 3 - D modeling , rapid prototyping , and simulation . 3D product   visualization promises more interactive experiences for online shoppers , but also challenges   retailers to overcome hurdles in the production of 3D content , as large - scale 3D content   production can be extremely costly and time - consuming.[11][12 ] 

 Visual communication[edit ] 

 Visual communication is the communication of ideas through the visual display of information .   Primarily associated with two dimensional images , it includes : alphanumerics , art , signs ,   and electronic resources . Recent research in the field has focused on web design and   graphically - oriented usability . 

 Visual analytics[edit ] 

 Visual analytics focuses on human interaction with visualization systems as part of a larger   process of data analysis . Visual analytics has been defined as " the science of analytical   reasoning supported by the interactive visual interface".[13 ] 

 Its focus is on human information discourse ( interaction ) within massive , dynamically changing   information spaces . Visual analytics research concentrates on support for perceptual and   cognitive operations that enable users to detect the expected and discover the unexpected in   complex information spaces . 

 Technologies resulting from visual analytics find their application in almost all fields , but are   being driven by critical needs ( and funding ) in biology and national security . 

 INSIGHT 

 visualization is critical to data analysis . It provides a front line of attack , revealing intricate   structure in data that can not be absorbed in any other way . We discover unimagined effects ,   and we challenge imagined ones . 

 Data by itself , consisting of bits and bytes stored in a file on a computer hard drive , is invisible .   In order to be able to see and make any sense of data , we need to visualize it . In this chapter   I ’m going to use a broader understanding of the term visualizing , that includes even pure   textual representations of data . For instance , just loading a dataset into a spreadsheet software   can be considered as data visualization . The invisible data suddenly turns into a visible ‘ picture ’   on our screen . Thus , the questions should not be whether journalists need to visualize data or   not , but which kind of visualization may be the most useful in which situation .   In other words : when does it makes sense to go beyond the table visualization ? The short   answer is : almost always . Tables alone are definitely not sufficient to give us an overview of a   dataset . And tables alone do n’t allow us to immediately identify patterns within the data . The   most common example here are geographical patterns which can only be observed after   visualizing data on a map . But there are also other kinds of patterns which we will see later in   this chapter . 

 Using visualization to Discover Insights 

 It is unrealistic to expect that data visualization tools and techniques will unleash a barrage of   ready - made stories from datasets . There are no rules , no ‘ protocol ’ that will guarantee us a   story . Instead , I think it makes more sense to look for ‘ insights ’ , which can be artfully woven   into stories in the hands of a good journalist . 

 Every new visualization is likely to give us some insights into our data . Some of those insights   might be already known ( but perhaps not yet proven ) while other insights might be completely   new or even surprising to us . Some new insights might mean the beginning of a story , while   others could just be the result of errors in the data , which are most likely to be found by   visualizing the data . 

 APPROACH- 

 Two Approaches to Data Visualization — Understanding and Persuasion . ... This type   of visualization is often a collection of basic charts and graphs . The goal is to present the data in   an in - depth and unbiased way . The second type is visualization for persuasion . 


EMAIL: 
ID_NUM: 
NAME_STUDENT: 
PHONE_NUM: 
STREET_ADDRESS: 
URL_PERSONAL: 
USERNAME: 

### Task: You are a PII (Personal Identity Information) Detection. Given a essays written by students, you have to identify ONLY the following:
NAME_STUDENT - The full or partial name of a student that is not necessarily the author of the essay. This excludes instructors, authors, and other person names.
EMAIL - A student’s email address.
USERNAME - A student's username on any platform.
ID_NUM - A number or sequence of characters that could be used to identify a student, such as a student ID or a social security number.
PHONE_NUM - A phone number associated with a student.
URL_PERSONAL - A URL that might be used to identify a student.
STREET_ADDRESS - A full or partial street address that is associated with the student, such as their home address.
### Input: VISUALIZATION 

 Visualization or visualisation ( see spelling differences ) is any technique for   creating images , diagrams , or animations to communicate a message . Visualization through   visual imagery has been an effective way to communicate both abstract and concrete ideas   since the dawn of humanity . Examples from history include cave paintings , Egyptian   hieroglyphs , Greek geometry , and Leonardo da Vinci 's revolutionary methods of technical   drawing for engineering and scientific purposes . 

 Visualization today has ever - expanding applications in science , education , engineering ( e.g. ,   product visualization ) , interactive multimedia , medicine , etc . Typical of a visualization   application is the field of computer graphics . The invention of computer graphics ( and 3D   computer graphics ) may be the most important development in visualization since the   invention of central perspective in the Renaissance period . The development of animation also   helped advance visualization . 

 APPLICATION 

 Scientific visualization   As a subject in computer science , scientific visualization is the use of interactive , sensory   representations , typically visual , of abstract data to reinforce cognition , hypothesis building ,   and reasoning . Scientific visualization is the transformation , selection , or representation of data   from simulations or experiments , with an implicit or explicit geometric structure , to allow the   exploration , analysis , and understanding of the data . Scientific visualization focuses and   emphasizes the representation of higher order data using primarily graphics and animation   techniques.[5][6 ] It is a very important part of visualization and maybe the first one , as the   visualization of experiments and phenomena is as old as science itself . Traditional areas of   scientific visualization are flow visualization , medical visualization , astrophysical visualization ,   and chemical visualization . There are several different techniques to visualize scientific data ,   with isosurface reconstruction and direct volume rendering being the more common . 

 Information visualization   Information visualization concentrates on the use of computer - supported tools to explore large   amount of abstract data . The term " information visualization " was originally coined by the User   Interface Research Group at Xerox PARC and included Jock Mackinlay.[citation needed ] Practical   application of information visualization in computer programs involves selecting , transforming ,   and representing abstract data in a form that facilitates human interaction for exploration and   understanding . Important aspects of information visualization are dynamics of visual   representation and the interactivity . Strong techniques enable the user to modify the   visualization in real - time , thus affording unparalleled perception of patterns and structural   relations in the abstract data in question . 

 Educational visualization 

 Educational visualization is using a simulation to create an image of something so it can be   taught about . This is very useful when teaching about a topic that is difficult to otherwise see ,   for example , atomic structure , because atoms are far too small to be studied easily without   expensive and difficult to use scientific equipment . 

 Knowledge visualization[edit ] 

 The use of visual representations to transfer knowledge between at least two persons aims to   improve the transfer of knowledge by using computer and non - computer - based visualization   methods complementarily.[8 ] Thus properly designed visualization is an important part of not   only data analysis but knowledge transfer process , too.[9 ] Knowledge transfer may be   significantly improved using hybrid designs as it enhances information density but may   decrease clarity as well . For example , visualization of a 3D scalar field may be implemented   using iso - surfaces for field distribution and textures for the gradient of the field.[10 ] Examples of   such visual formats are sketches , diagrams , images , objects , interactive visualizations ,   information visualization applications , and imaginary visualizations as in stories . While   information visualization concentrates on the use of computer - supported tools to derive new   insights , knowledge visualization focuses on transferring insights and creating   new knowledge in groups . Beyond the mere transfer of facts , knowledge visualization aims to   further transfer insights , experiences , attitudes , values , expectations , perspectives , opinions ,   and predictions by using various complementary visualizations . See also : picture   dictionary , visual dictionary 

 Product visualization[edit ] 

 Product visualization involves visualization software technology for the viewing and   manipulation of 3D models , technical drawing and other related documentation of   manufactured components and large assemblies of products . It is a key part of product lifecycle   management . Product visualization software typically provides high levels of photorealism so   that a product can be viewed before it is actually manufactured . This supports functions ranging   from design and styling to sales and marketing . Technical visualization is an important aspect of   product development . Originally technical drawings were made by hand , but with the rise of   advanced computer graphics the drawing board has been replaced by computer - aided   design ( CAD ) . CAD - drawings and models have several advantages over hand - made drawings   such as the possibility of 3 - D modeling , rapid prototyping , and simulation . 3D product   visualization promises more interactive experiences for online shoppers , but also challenges   retailers to overcome hurdles in the production of 3D content , as large - scale 3D content   production can be extremely costly and time - consuming.[11][12 ] 

 Visual communication[edit ] 

 Visual communication is the communication of ideas through the visual display of information .   Primarily associated with two dimensional images , it includes : alphanumerics , art , signs ,   and electronic resources . Recent research in the field has focused on web design and   graphically - oriented usability . 

 Visual analytics[edit ] 

 Visual analytics focuses on human interaction with visualization systems as part of a larger   process of data analysis . Visual analytics has been defined as " the science of analytical   reasoning supported by the interactive visual interface".[13 ] 

 Its focus is on human information discourse ( interaction ) within massive , dynamically changing   information spaces . Visual analytics research concentrates on support for perceptual and   cognitive operations that enable users to detect the expected and discover the unexpected in   complex information spaces . 

 Technologies resulting from visual analytics find their application in almost all fields , but are   being driven by critical needs ( and funding ) in biology and national security . 

 INSIGHT 

 visualization is critical to data analysis . It provides a front line of attack , revealing intricate   structure in data that can not be absorbed in any other way . We discover unimagined effects ,   and we challenge imagined ones . 

 Data by itself , consisting of bits and bytes stored in a file on a computer hard drive , is invisible .   In order to be able to see and make any sense of data , we need to visualize it . In this chapter   I ’m going to use a broader understanding of the term visualizing , that includes even pure   textual representations of data . For instance , just loading a dataset into a spreadsheet software   can be considered as data visualization . The invisible data suddenly turns into a visible ‘ picture ’   on our screen . Thus , the questions should not be whether journalists need to visualize data or   not , but which kind of visualization may be the most useful in which situation .   In other words : when does it makes sense to go beyond the table visualization ? The short   answer is : almost always . Tables alone are definitely not sufficient to give us an overview of a   dataset . And tables alone do n’t allow us to immediately identify patterns within the data . The   most common example here are geographical patterns which can only be observed after   visualizing data on a map . But there are also other kinds of patterns which we will see later in   this chapter . 

 Using visualization to Discover Insights 

 It is unrealistic to expect that data visualization tools and techniques will unleash a barrage of   ready - made stories from datasets . There are no rules , no ‘ protocol ’ that will guarantee us a   story . Instead , I think it makes more sense to look for ‘ insights ’ , which can be artfully woven   into stories in the hands of a good journalist . 

 Every new visualization is likely to give us some insights into our data . Some of those insights   might be already known ( but perhaps not yet proven ) while other insights might be completely   new or even surprising to us . Some new insights might mean the beginning of a story , while   others could just be the result of errors in the data , which are most likely to be found by   visualizing the data . 

 APPROACH- 

 Two Approaches to Data Visualization — Understanding and Persuasion . ... This type   of visualization is often a collection of basic charts and graphs . The goal is to present the data in   an in - depth and unbiased way . The second type is visualization for persuasion .
### Output: 