Kalleidos (Spring 2011) IPRO 303

“Over the last few years there has been an explosion of different websites that allow average person to contribute different content such as pictures, comments, stories and reviews about places around them. While there are many companies that focus on aggregating such data into common stream - complexity of interpreting such data in a meaningful actionable way remains a challenge for many companies including NAVTEQ.” [Appendix B]  Identify new content from real time feeds which include 1. Points of Interest and their attribution (e.g. Hours of Operation, Languages Spoken at locale, etc…) 2. Any associated content (e.g. events, relationships to other Points of Interest (e.g. same owner, etc…)  Create visualizations based on this content - some basic examples could include 1. Hot/Not So Hot real estate areas (new construction, sales) based on agent and broker social media updates 2. High/Low crime areas based on social media stream 3. Hot/Not city zones based on number of people tweeting/updating Facebook/checking in/etc… in certain areas, parties, POIs.Sponsorship: NAVTEQDeliverable

Kalleidos (Spring 2011) IPRO 303: KalleidosIPRO303MidTermPresentationSp11

“Over the last few years there has been an explosion of different websites that allow average person to contribute different content such as pictures, comments, stories and reviews about places around them. While there are many companies that focus on aggregating such data into common stream - complexity of interpreting such data in a meaningful actionable way remains a challenge for many companies including NAVTEQ.” [Appendix B]  Identify new content from real time feeds which include 1. Points of Interest and their attribution (e.g. Hours of Operation, Languages Spoken at locale, etc…) 2. Any associated content (e.g. events, relationships to other Points of Interest (e.g. same owner, etc…)  Create visualizations based on this content - some basic examples could include 1. Hot/Not So Hot real estate areas (new construction, sales) based on agent and broker social media updates 2. High/Low crime areas based on social media stream 3. Hot/Not city zones based on number of people tweeting/updating Facebook/checking in/etc… in certain areas, parties, POIs.Sponsorship: NAVTEQDeliverable

Kalleidos (Spring 2011) IPRO 303: KalleidosIPRO303BrochureSp11

“Over the last few years there has been an explosion of different websites that allow average person to contribute different content such as pictures, comments, stories and reviews about places around them. While there are many companies that focus on aggregating such data into common stream - complexity of interpreting such data in a meaningful actionable way remains a challenge for many companies including NAVTEQ.” [Appendix B]  Identify new content from real time feeds which include 1. Points of Interest and their attribution (e.g. Hours of Operation, Languages Spoken at locale, etc…) 2. Any associated content (e.g. events, relationships to other Points of Interest (e.g. same owner, etc…)  Create visualizations based on this content - some basic examples could include 1. Hot/Not So Hot real estate areas (new construction, sales) based on agent and broker social media updates 2. High/Low crime areas based on social media stream 3. Hot/Not city zones based on number of people tweeting/updating Facebook/checking in/etc… in certain areas, parties, POIs.Sponsorship: NAVTEQDeliverable

Kalleidos (Spring 2011) IPRO 303: KalleidosIPRO303FinalReportSp11_redacted

“Over the last few years there has been an explosion of different websites that allow average person to contribute different content such as pictures, comments, stories and reviews about places around them. While there are many companies that focus on aggregating such data into common stream - complexity of interpreting such data in a meaningful actionable way remains a challenge for many companies including NAVTEQ.” [Appendix B]  Identify new content from real time feeds which include 1. Points of Interest and their attribution (e.g. Hours of Operation, Languages Spoken at locale, etc…) 2. Any associated content (e.g. events, relationships to other Points of Interest (e.g. same owner, etc…)  Create visualizations based on this content - some basic examples could include 1. Hot/Not So Hot real estate areas (new construction, sales) based on agent and broker social media updates 2. High/Low crime areas based on social media stream 3. Hot/Not city zones based on number of people tweeting/updating Facebook/checking in/etc… in certain areas, parties, POIs.Sponsorship: NAVTEQDeliverable

Kalleidos (Spring 2011) IPRO 303: KalleidosIPRO303ProjectPlanSp11_redacted

“Over the last few years there has been an explosion of different websites that allow average person to contribute different content such as pictures, comments, stories and reviews about places around them. While there are many companies that focus on aggregating such data into common stream - complexity of interpreting such data in a meaningful actionable way remains a challenge for many companies including NAVTEQ.” [Appendix B]  Identify new content from real time feeds which include 1. Points of Interest and their attribution (e.g. Hours of Operation, Languages Spoken at locale, etc…) 2. Any associated content (e.g. events, relationships to other Points of Interest (e.g. same owner, etc…)  Create visualizations based on this content - some basic examples could include 1. Hot/Not So Hot real estate areas (new construction, sales) based on agent and broker social media updates 2. High/Low crime areas based on social media stream 3. Hot/Not city zones based on number of people tweeting/updating Facebook/checking in/etc… in certain areas, parties, POIs.Sponsorship: NAVTEQDeliverable