�ݺ�ߣshows by User: JenniferHuiLi

�ݺ�ߣshows by User: JenniferHuiLi / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: JenniferHuiLi / Sat, 12 Dec 2015 21:34:48 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: JenniferHuiLi A Supervised Modeling Approach to Determine Elite Status of Yelp Members /JenniferHuiLi/a-supervised-modeling-approach-to-determine-elite-status-of-yelp-members finalpaper-dataanalysis-151212213448
Yelp, which was founded in 2004 by two PayPal executives, is a crowd-sourced multinational company headquartered in San Francisco, CA. Yelp’s goal is to connect people with great local businesses. Yelp has over 77 million cumulative reviews from yelpers around the world. Yelpers share their everyday local business experiences, giving voice to con- sumers and bringing word of mouth online. Approximately 142 million unique visitors used Yelp’s website, and approx- imately 79 million unique visitors visited Yelp via their mo- bile device, on a monthly average [1]. Embed among all these business reviews and yelpers is a classification between Elite and Non-elite yelpers. Yelp Elite is a way for Yelp to recognize and reward users who are active on Yelp. Elite-worthiness is based on a number of things, including well-written reviews, high quality tips, a detailed personal profile, an active voting and compliment- ing record, and a history of playing well with others [2]. Elite status is earned every year and is determined by a commit- tee. Elite yelpers have profiles with special badges and the elite yelpers are invited to private events and parties. For the data analytics course project, our team will at- tempt to crack the code using a systematic algorithm to pre- dict users’ Elite worthiness. We will use the Yelp academic set and the associated user attributes to determine the most accurate algorithm to predict elite status. Our goal for the project is to predict with 95% accuracy if a user obtains elite status for any particular year within the Yelp Academic set. We should note that there are some inherent risk using the Yelp academic data set. Our team has no insight into any additional or hidden indicators that may be used in determin- ing Elite status beyond the data field that was provided in the Yelp Academic set. The academic dataset only has 12% of the reviews from 370K users. Our algorithm and modeling is based on the the data provided that exists in the academic data set.]]>
Yelp, which was founded in 2004 by two PayPal executives, is a crowd-sourced multinational company headquartered in San Francisco, CA. Yelp’s goal is to connect people with great local businesses. Yelp has over 77 million cumulative reviews from yelpers around the world. Yelpers share their everyday local business experiences, giving voice to con- sumers and bringing word of mouth online. Approximately 142 million unique visitors used Yelp’s website, and approx- imately 79 million unique visitors visited Yelp via their mo- bile device, on a monthly average [1]. Embed among all these business reviews and yelpers is a classification between Elite and Non-elite yelpers. Yelp Elite is a way for Yelp to recognize and reward users who are active on Yelp. Elite-worthiness is based on a number of things, including well-written reviews, high quality tips, a detailed personal profile, an active voting and compliment- ing record, and a history of playing well with others [2]. Elite status is earned every year and is determined by a commit- tee. Elite yelpers have profiles with special badges and the elite yelpers are invited to private events and parties. For the data analytics course project, our team will at- tempt to crack the code using a systematic algorithm to pre- dict users’ Elite worthiness. We will use the Yelp academic set and the associated user attributes to determine the most accurate algorithm to predict elite status. Our goal for the project is to predict with 95% accuracy if a user obtains elite status for any particular year within the Yelp Academic set. We should note that there are some inherent risk using the Yelp academic data set. Our team has no insight into any additional or hidden indicators that may be used in determin- ing Elite status beyond the data field that was provided in the Yelp Academic set. The academic dataset only has 12% of the reviews from 370K users. Our algorithm and modeling is based on the the data provided that exists in the academic data set.]]> Sat, 12 Dec 2015 21:34:48 GMT /JenniferHuiLi/a-supervised-modeling-approach-to-determine-elite-status-of-yelp-members JenniferHuiLi@slideshare.net(JenniferHuiLi) A Supervised Modeling Approach to Determine Elite Status of Yelp Members JenniferHuiLi Yelp, which was founded in 2004 by two PayPal executives, is a crowd-sourced multinational company headquartered in San Francisco, CA. Yelp’s goal is to connect people with great local businesses. Yelp has over 77 million cumulative reviews from yelpers around the world. Yelpers share their everyday local business experiences, giving voice to con- sumers and bringing word of mouth online. Approximately 142 million unique visitors used Yelp’s website, and approx- imately 79 million unique visitors visited Yelp via their mo- bile device, on a monthly average [1]. Embed among all these business reviews and yelpers is a classification between Elite and Non-elite yelpers. Yelp Elite is a way for Yelp to recognize and reward users who are active on Yelp. Elite-worthiness is based on a number of things, including well-written reviews, high quality tips, a detailed personal profile, an active voting and compliment- ing record, and a history of playing well with others [2]. Elite status is earned every year and is determined by a commit- tee. Elite yelpers have profiles with special badges and the elite yelpers are invited to private events and parties. For the data analytics course project, our team will at- tempt to crack the code using a systematic algorithm to pre- dict users’ Elite worthiness. We will use the Yelp academic set and the associated user attributes to determine the most accurate algorithm to predict elite status. Our goal for the project is to predict with 95% accuracy if a user obtains elite status for any particular year within the Yelp Academic set. We should note that there are some inherent risk using the Yelp academic data set. Our team has no insight into any additional or hidden indicators that may be used in determin- ing Elite status beyond the data field that was provided in the Yelp Academic set. The academic dataset only has 12% of the reviews from 370K users. Our algorithm and modeling is based on the the data provided that exists in the academic data set. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/finalpaper-dataanalysis-151212213448-thumbnail.jpg?width=120&height=120&fit=bounds" /> Yelp, which was founded in 2004 by two PayPal executives, is a crowd-sourced multinational company headquartered in San Francisco, CA. Yelp’s goal is to connect people with great local businesses. Yelp has over 77 million cumulative reviews from yelpers around the world. Yelpers share their everyday local business experiences, giving voice to con- sumers and bringing word of mouth online. Approximately 142 million unique visitors used Yelp’s website, and approx- imately 79 million unique visitors visited Yelp via their mo- bile device, on a monthly average [1]. Embed among all these business reviews and yelpers is a classification between Elite and Non-elite yelpers. Yelp Elite is a way for Yelp to recognize and reward users who are active on Yelp. Elite-worthiness is based on a number of things, including well-written reviews, high quality tips, a detailed personal profile, an active voting and compliment- ing record, and a history of playing well with others [2]. Elite status is earned every year and is determined by a commit- tee. Elite yelpers have profiles with special badges and the elite yelpers are invited to private events and parties. For the data analytics course project, our team will at- tempt to crack the code using a systematic algorithm to pre- dict users’ Elite worthiness. We will use the Yelp academic set and the associated user attributes to determine the most accurate algorithm to predict elite status. Our goal for the project is to predict with 95% accuracy if a user obtains elite status for any particular year within the Yelp Academic set. We should note that there are some inherent risk using the Yelp academic data set. Our team has no insight into any additional or hidden indicators that may be used in determin- ing Elite status beyond the data field that was provided in the Yelp Academic set. The academic dataset only has 12% of the reviews from 370K users. Our algorithm and modeling is based on the the data provided that exists in the academic data set.

A Supervised Modeling Approach to Determine Elite Status of Yelp Members from Jennifer (Hui) Li

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0 Emirates ICE System Mockup /slideshow/emirates-ice-system-mockup/53263290 keynote-datalake-150928070703-lva1-app6892
Built mockups to improve Emirates ICE entertainment system and to collect passenger data.]]>
Built mockups to improve Emirates ICE entertainment system and to collect passenger data.]]> Mon, 28 Sep 2015 07:07:03 GMT /slideshow/emirates-ice-system-mockup/53263290 JenniferHuiLi@slideshare.net(JenniferHuiLi) Emirates ICE System Mockup JenniferHuiLi Built mockups to improve Emirates ICE entertainment system and to collect passenger data. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/keynote-datalake-150928070703-lva1-app6892-thumbnail.jpg?width=120&height=120&fit=bounds" /> Built mockups to improve Emirates ICE entertainment system and to collect passenger data.

Emirates ICE System Mockup from Jennifer (Hui) Li

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0 Survivable Social Network on a Chip - Foundation of software engineering /slideshow/foundation-of-software-engineering-ssnoc-final-presentation/43495960 foundationofsoftwareengineering-ssnoc-finalpresentation-150114024003-conversion-gate02
Survivable Social Network on a Chip is an application that operates in disaster situations where there is no wifi or cellular connection. Survivors can communicate through this application by connecting to the chip and generate a small local network to help each other. It has below functionalities: 1. Join community 2. Share status (red, yellow, green) 3. Post announcement on public wall 4. Private chat 5. Locate people on map 6. Search chat history 7. Make a call]]>
Survivable Social Network on a Chip is an application that operates in disaster situations where there is no wifi or cellular connection. Survivors can communicate through this application by connecting to the chip and generate a small local network to help each other. It has below functionalities: 1. Join community 2. Share status (red, yellow, green) 3. Post announcement on public wall 4. Private chat 5. Locate people on map 6. Search chat history 7. Make a call]]> Wed, 14 Jan 2015 02:40:03 GMT /slideshow/foundation-of-software-engineering-ssnoc-final-presentation/43495960 JenniferHuiLi@slideshare.net(JenniferHuiLi) Survivable Social Network on a Chip - Foundation of software engineering JenniferHuiLi Survivable Social Network on a Chip is an application that operates in disaster situations where there is no wifi or cellular connection. Survivors can communicate through this application by connecting to the chip and generate a small local network to help each other. It has below functionalities: 1. Join community 2. Share status (red, yellow, green) 3. Post announcement on public wall 4. Private chat 5. Locate people on map 6. Search chat history 7. Make a call <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/foundationofsoftwareengineering-ssnoc-finalpresentation-150114024003-conversion-gate02-thumbnail.jpg?width=120&height=120&fit=bounds" /> Survivable Social Network on a Chip is an application that operates in disaster situations where there is no wifi or cellular connection. Survivors can communicate through this application by connecting to the chip and generate a small local network to help each other. It has below functionalities: 1. Join community 2. Share status (red, yellow, green) 3. Post announcement on public wall 4. Private chat 5. Locate people on map 6. Search chat history 7. Make a call

Survivable Social Network on a Chip - Foundation of software engineering from Jennifer (Hui) Li

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0 Safety Critical Research /slideshow/safety-critical-research/42336143 fse-sa5-safetycriticalresearchprojectpresentationfinal1-141204000211-conversion-gate01
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]]> Thu, 04 Dec 2014 00:02:11 GMT /slideshow/safety-critical-research/42336143 JenniferHuiLi@slideshare.net(JenniferHuiLi) Safety Critical Research JenniferHuiLi <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/fse-sa5-safetycriticalresearchprojectpresentationfinal1-141204000211-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds" />

Safety Critical Research from Jennifer (Hui) Li

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0 Software Metrics - Lease Management Case Study /slideshow/software-metrics-lease-management-case-study/41732394 task1presentation-team2touchstone-141118202923-conversion-gate01
Problem Statement This case is based on the lease management system example in Boehm & Turner’s Balancing Agility and Discipline , pages 84 – 89. Please start by reading that information. Consider the following situation: The lease management system project has modified its XP approach to include a few plan-driven elements. Most notably, they now use high-level architecture plans, improved milestone completion criteria, and design patterns. However, the project remains true to its XP beliefs. For example, they will continue to use more complete and accurate “as-discovered” criteria for determining milestone completions and to reprioritize story cards at the beginning of each development increment. The project team estimates the duration of each iteration as part of the iteration kickoff meeting; they are no longer tied to fixed-length cycles of development and deployment, since analyses showed that fixed-length XP cycles were inappropriate for large, complex systems like the lease management system. However, the team is committed to approximately one-month iteration cycles and will take on only the number of tasks that they esitmate they can complete in that amount of time. The CIO is holding the project’s manager, Tarak Srinivas, responsible for timely completion of iterations on the system, because new lease management capabilities must be available to stakeholders when promised. The system must, of course, continue to be highly reliable and meet user needs as described in the story cards (which do get modified, as described in Balancing Agility and Discipline 84 – 89).]]>
Problem Statement This case is based on the lease management system example in Boehm & Turner’s Balancing Agility and Discipline , pages 84 – 89. Please start by reading that information. Consider the following situation: The lease management system project has modified its XP approach to include a few plan-driven elements. Most notably, they now use high-level architecture plans, improved milestone completion criteria, and design patterns. However, the project remains true to its XP beliefs. For example, they will continue to use more complete and accurate “as-discovered” criteria for determining milestone completions and to reprioritize story cards at the beginning of each development increment. The project team estimates the duration of each iteration as part of the iteration kickoff meeting; they are no longer tied to fixed-length cycles of development and deployment, since analyses showed that fixed-length XP cycles were inappropriate for large, complex systems like the lease management system. However, the team is committed to approximately one-month iteration cycles and will take on only the number of tasks that they esitmate they can complete in that amount of time. The CIO is holding the project’s manager, Tarak Srinivas, responsible for timely completion of iterations on the system, because new lease management capabilities must be available to stakeholders when promised. The system must, of course, continue to be highly reliable and meet user needs as described in the story cards (which do get modified, as described in Balancing Agility and Discipline 84 – 89).]]> Tue, 18 Nov 2014 20:29:22 GMT /slideshow/software-metrics-lease-management-case-study/41732394 JenniferHuiLi@slideshare.net(JenniferHuiLi) Software Metrics - Lease Management Case Study JenniferHuiLi Problem Statement This case is based on the lease management system example in Boehm & Turner’s Balancing Agility and Discipline , pages 84 – 89. Please start by reading that information. Consider the following situation: The lease management system project has modified its XP approach to include a few plan-driven elements. Most notably, they now use high-level architecture plans, improved milestone completion criteria, and design patterns. However, the project remains true to its XP beliefs. For example, they will continue to use more complete and accurate “as-discovered” criteria for determining milestone completions and to reprioritize story cards at the beginning of each development increment. The project team estimates the duration of each iteration as part of the iteration kickoff meeting; they are no longer tied to fixed-length cycles of development and deployment, since analyses showed that fixed-length XP cycles were inappropriate for large, complex systems like the lease management system. However, the team is committed to approximately one-month iteration cycles and will take on only the number of tasks that they esitmate they can complete in that amount of time. The CIO is holding the project’s manager, Tarak Srinivas, responsible for timely completion of iterations on the system, because new lease management capabilities must be available to stakeholders when promised. The system must, of course, continue to be highly reliable and meet user needs as described in the story cards (which do get modified, as described in Balancing Agility and Discipline 84 – 89). <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/task1presentation-team2touchstone-141118202923-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds" /> Problem Statement This case is based on the lease management system example in Boehm & Turner’s Balancing Agility and Discipline , pages 84 – 89. Please start by reading that information. Consider the following situation: The lease management system project has modified its XP approach to include a few plan-driven elements. Most notably, they now use high-level architecture plans, improved milestone completion criteria, and design patterns. However, the project remains true to its XP beliefs. For example, they will continue to use more complete and accurate “as-discovered” criteria for determining milestone completions and to reprioritize story cards at the beginning of each development increment. The project team estimates the duration of each iteration as part of the iteration kickoff meeting; they are no longer tied to fixed-length cycles of development and deployment, since analyses showed that fixed-length XP cycles were inappropriate for large, complex systems like the lease management system. However, the team is committed to approximately one-month iteration cycles and will take on only the number of tasks that they esitmate they can complete in that amount of time. The CIO is holding the project’s manager, Tarak Srinivas, responsible for timely completion of iterations on the system, because new lease management capabilities must be available to stakeholders when promised. The system must, of course, continue to be highly reliable and meet user needs as described in the story cards (which do get modified, as described in Balancing Agility and Discipline 84 – 89).

Software Metrics - Lease Management Case Study from Jennifer (Hui) Li

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0 Facebook Business Analysis and Prognosis /slideshow/facebook-business-analysis-and-prognosis-40753821/40753821 esm-t4-facebook-jli-ldsilva-slideshare-141027015202-conversion-gate02
Here is a business analysis of Facebook in 2014. We considered Facebook's financial stability, SWOT analysis, internal and external analysis, business strategy. Prognosis is based on above factors. Comments are very welcome.]]>
Here is a business analysis of Facebook in 2014. We considered Facebook's financial stability, SWOT analysis, internal and external analysis, business strategy. Prognosis is based on above factors. Comments are very welcome.]]> Mon, 27 Oct 2014 01:52:02 GMT /slideshow/facebook-business-analysis-and-prognosis-40753821/40753821 JenniferHuiLi@slideshare.net(JenniferHuiLi) Facebook Business Analysis and Prognosis JenniferHuiLi Here is a business analysis of Facebook in 2014. We considered Facebook's financial stability, SWOT analysis, internal and external analysis, business strategy. Prognosis is based on above factors. Comments are very welcome. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/esm-t4-facebook-jli-ldsilva-slideshare-141027015202-conversion-gate02-thumbnail.jpg?width=120&height=120&fit=bounds" /> Here is a business analysis of Facebook in 2014. We considered Facebook's financial stability, SWOT analysis, internal and external analysis, business strategy. Prognosis is based on above factors. Comments are very welcome.

Facebook Business Analysis and Prognosis from Jennifer (Hui) Li

]]> 746 5 https://cdn.slidesharecdn.com/ss_thumbnails/esm-t4-facebook-jli-ldsilva-slideshare-141027015202-conversion-gate02-thumbnail.jpg?width=120&height=120&fit=bounds presentation 000000 http://activitystrea.ms/schema/1.0/post

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0 https://cdn.slidesharecdn.com/profile-photo-JenniferHuiLi-48x48.jpg?cb=1529471874 + Interdisciplinary background (Software Engineering, Marketing and Management) + Professional in product management and commercialization https://cdn.slidesharecdn.com/ss_thumbnails/finalpaper-dataanalysis-151212213448-thumbnail.jpg?width=320&height=320&fit=bounds JenniferHuiLi/a-supervised-modeling-approach-to-determine-elite-status-of-yelp-members A Supervised Modeling ... https://cdn.slidesharecdn.com/ss_thumbnails/keynote-datalake-150928070703-lva1-app6892-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/emirates-ice-system-mockup/53263290 Emirates ICE System Mo... https://cdn.slidesharecdn.com/ss_thumbnails/foundationofsoftwareengineering-ssnoc-finalpresentation-150114024003-conversion-gate02-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/foundation-of-software-engineering-ssnoc-final-presentation/43495960 Survivable Social Netw...