Design Thinking for Innovation   University of Virginia   Darden School of Business     Reflection – Mind Mapping     Challenge :   Connecting time for passengers at airports has always been a topic for optimization by airlines and airports   alike . The main idea is to minimize connecting time at airports because this means flights with shortest   transit times may get preference on travelers ’ pick list when browsing for air travel itineraries . However ,   it is a balancing act to minimize the connecting time at airports . There ’s a whole chain of network partners   affecting this determined time , such as Air Traffic Management and Airport Operators whose priorities   are to ensure safe and economic flight operations . There ’s also the commercial aspect of airlines buying   “ landing slot times ” , one - hour time brackets for landing and takeoff . All of this mix and much more   complications such as lack of data and data sharing among partners made the Minimum Connecting Time   something close to an intuitive judgement .   With the advent of Big Data and Cloud technologies , it was decided to give the Connecting Time another   go . This time , the challenge was first to determine the “ actual connecting time ” in terminals , predict ,   based on machine learning , the future connecting time , and then attempt to optimize by looking at all   findings to come up with a minimum connecting time .     Selection :   Mind map was chosen because of its intuitive approach engaging various stakeholders to find data sources   and mines to tackle this challenge . One needs to keep in mind that up to that point , such an undertaking   was not attempted on this scale , there was always limited bilateral work between airports and airlines ,   but this was truly a wholistic and global approach . The project team figured out that in order to get the   best ideas out of people , they needed to be a same physical space to allow interactions and represent a   whole array of sectors , fields of study and expertise – a mosaic of stakeholders ( including avid travelers ) .   The workshop participants were grouped in teams to discuss about the potential sources of data and   approaches that would help provide insights on solving the challenge .     Application :   Prior to coming to the point of the mind mapping , the project team investigated the “ what is ” but asking   SMEs , passengers , academics ( mathematicians , OR ) , and big data experts . The project team used   customer diaries , empathy maps and visualization . The common denominator between all those   stakeholders where that they were themselves travelers and ended up sympathizing with this challenge   on a personal basis rather than a research problem . The project team then placed all those collected   artifacts and data on a white wall and asked the workshop participants to cluster leads that could help   unravel the challenge . It was interactive and there was lots of room for discussions and debates ,   eventually the project team got a whole work cut out for them sorting through the post - it notes and trying   to make sense of the solution taking shape .     Insight :   All previous failed attempts to tackle this challenge on a global scale assumed that airports and airlines   needed to share data and this data collection effort was proven to be too much of a hassle . However , the   project team along with the workshop participants discovered , that , by using passenger transactional data   the data science team could extrapolate the needed information . This data resides in an industry 

 organization and was virtually untapped . The passenger / flight data along with data from open source   platform would make it possible to start a data lake ( collecting , cleansing and storing ) and apply data   science techniques to provide prediction .     Approach :   Mind Mapping is a great way to gain insights , ideas and out - of - the box feedback . I would employ this again   thin the future because it gets people talking to each other , brainstorming and challenging their   assumptions when they see the big picture and listen to their value chain neighbors . In the example I   provided , Mind Mapping worked effectively , however the undertaken scope by the project team was   enormous and could have easily been split up to phases or teams . It was a massive global challenge that   is still in its infancy stage but is set to revolutionize the experience of travel passengers . Also , it means   huge gains for airlines , cost savings for airports and potential reduction in carbon emissions . Mind Map   allowed the project team to figure out a back - door to data , bypassing the exhausting data collection effort ,   therefore it made the work - in - progress to solve this challenge possible . 


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: Design Thinking for Innovation   University of Virginia   Darden School of Business     Reflection – Mind Mapping     Challenge :   Connecting time for passengers at airports has always been a topic for optimization by airlines and airports   alike . The main idea is to minimize connecting time at airports because this means flights with shortest   transit times may get preference on travelers ’ pick list when browsing for air travel itineraries . However ,   it is a balancing act to minimize the connecting time at airports . There ’s a whole chain of network partners   affecting this determined time , such as Air Traffic Management and Airport Operators whose priorities   are to ensure safe and economic flight operations . There ’s also the commercial aspect of airlines buying   “ landing slot times ” , one - hour time brackets for landing and takeoff . All of this mix and much more   complications such as lack of data and data sharing among partners made the Minimum Connecting Time   something close to an intuitive judgement .   With the advent of Big Data and Cloud technologies , it was decided to give the Connecting Time another   go . This time , the challenge was first to determine the “ actual connecting time ” in terminals , predict ,   based on machine learning , the future connecting time , and then attempt to optimize by looking at all   findings to come up with a minimum connecting time .     Selection :   Mind map was chosen because of its intuitive approach engaging various stakeholders to find data sources   and mines to tackle this challenge . One needs to keep in mind that up to that point , such an undertaking   was not attempted on this scale , there was always limited bilateral work between airports and airlines ,   but this was truly a wholistic and global approach . The project team figured out that in order to get the   best ideas out of people , they needed to be a same physical space to allow interactions and represent a   whole array of sectors , fields of study and expertise – a mosaic of stakeholders ( including avid travelers ) .   The workshop participants were grouped in teams to discuss about the potential sources of data and   approaches that would help provide insights on solving the challenge .     Application :   Prior to coming to the point of the mind mapping , the project team investigated the “ what is ” but asking   SMEs , passengers , academics ( mathematicians , OR ) , and big data experts . The project team used   customer diaries , empathy maps and visualization . The common denominator between all those   stakeholders where that they were themselves travelers and ended up sympathizing with this challenge   on a personal basis rather than a research problem . The project team then placed all those collected   artifacts and data on a white wall and asked the workshop participants to cluster leads that could help   unravel the challenge . It was interactive and there was lots of room for discussions and debates ,   eventually the project team got a whole work cut out for them sorting through the post - it notes and trying   to make sense of the solution taking shape .     Insight :   All previous failed attempts to tackle this challenge on a global scale assumed that airports and airlines   needed to share data and this data collection effort was proven to be too much of a hassle . However , the   project team along with the workshop participants discovered , that , by using passenger transactional data   the data science team could extrapolate the needed information . This data resides in an industry 

 organization and was virtually untapped . The passenger / flight data along with data from open source   platform would make it possible to start a data lake ( collecting , cleansing and storing ) and apply data   science techniques to provide prediction .     Approach :   Mind Mapping is a great way to gain insights , ideas and out - of - the box feedback . I would employ this again   thin the future because it gets people talking to each other , brainstorming and challenging their   assumptions when they see the big picture and listen to their value chain neighbors . In the example I   provided , Mind Mapping worked effectively , however the undertaken scope by the project team was   enormous and could have easily been split up to phases or teams . It was a massive global challenge that   is still in its infancy stage but is set to revolutionize the experience of travel passengers . Also , it means   huge gains for airlines , cost savings for airports and potential reduction in carbon emissions . Mind Map   allowed the project team to figure out a back - door to data , bypassing the exhausting data collection effort ,   therefore it made the work - in - progress to solve this challenge possible .
### Output: 