Walter MoodyProfessor BendelWRTG 39417 April 2017Buffalo W.docxcelenarouzie
Walter Moody
Professor Bendel
WRTG 394
17 April 2017
Buffalo Wild Wings Proposal Memo:
Dear Jeff,
I am proposing to carry out a study involving the restaurant corporation, Buffalo Wild Wings. Specifically, I’d like to study the effectiveness and efficiency of the POS (Point of Sale) training. In order to complete this study, I will need to meet with several, servers, cooks, shift leaders, and even managers in order to determine the true effectiveness of these systems, as well as the training associated with them. I am asking here for formal permission to carry out this study at Buffalo Wild Wings. This study will show whether or not the current POS system and training for employees to use this POS system is up to date, or if changes need to be made for the sake of the effectiveness of the restaurant.
Many months ago, I was a dedicated server who went through POS training, and then used the POS system in the restaurant all shift long, everyday, for months. After a long while, all the problems and issues with both the POS system and with POS assistance from the shift leaders began to add up to make my work life unbearably stressful. According to Oronsky, technological training equips the teams and they become confident while doing their job (945). The problem/need for this study is that Buffalo Wild Wings desperately needs a new POS system, new POS training, or maybe even both. According to Huo, the technology has a policy of assisting its customers to make the best decision on the POS system that best serves their needs and allows them to meet demand and remain competitive while offering quality services (242). However, those are not the benefits that I see when using this specific system. Benefits to the company of increasing aid to or fixing this problem include but are not limited to: quicker order times, better table turnover, better employee knowledge, increased employer morale, faster checkouts, less managerial stress, and much more. Technological training is paramount to the understanding of the POS system procedures. Understanding the system reduces lag time between when an order is received and when delivery occurs (Ham 79). I will conduct my research based on articles and previous studies done by Buffalo Wild Wings. I will also head into the close Buffalo Wild Wings location in Laurel, MD to hopefully interview and gain perspective from some of the managers and workers in order to study if they have the same issues that I went through while working at Buffalo Wild Wings. In addition, I will collect data to see if increased training leads to better table times, tips, and better overall workplace morale.
To complete this research for this study, I will need a couple weeks to complete this study both effectively and efficiently. Also, I will fortunately not need any budget or money to invest because I have previously worked at BWW and can perform the study with off shift managers at opportune times during the slow parts of the wor.
Yelp's Review Filtering Algorithm PowerpointYao Yao
- Data scraping, cluster, stratify
- Feature creation from metadata, NLP sentiment, spelling, readability, deceptive and extreme text classifiers
- Balance and scale, logistic regression, feature selection, final model
- Find features that correspond to Yelp's Algorithm and evaluate
Video of presentation: https://youtu.be/uavbPKiUg9M
Poster: https://www.slideshare.net/YaoYao44/yelps-review-filtering-algorithm-poster
Paper:
Github: https://github.com/post2web/capstone
Walter MoodyProfessor BendelWRTG 39417 April 2017Buffalo W.docxcelenarouzie
Walter Moody
Professor Bendel
WRTG 394
17 April 2017
Buffalo Wild Wings Proposal Memo:
Dear Jeff,
I am proposing to carry out a study involving the restaurant corporation, Buffalo Wild Wings. Specifically, I’d like to study the effectiveness and efficiency of the POS (Point of Sale) training. In order to complete this study, I will need to meet with several, servers, cooks, shift leaders, and even managers in order to determine the true effectiveness of these systems, as well as the training associated with them. I am asking here for formal permission to carry out this study at Buffalo Wild Wings. This study will show whether or not the current POS system and training for employees to use this POS system is up to date, or if changes need to be made for the sake of the effectiveness of the restaurant.
Many months ago, I was a dedicated server who went through POS training, and then used the POS system in the restaurant all shift long, everyday, for months. After a long while, all the problems and issues with both the POS system and with POS assistance from the shift leaders began to add up to make my work life unbearably stressful. According to Oronsky, technological training equips the teams and they become confident while doing their job (945). The problem/need for this study is that Buffalo Wild Wings desperately needs a new POS system, new POS training, or maybe even both. According to Huo, the technology has a policy of assisting its customers to make the best decision on the POS system that best serves their needs and allows them to meet demand and remain competitive while offering quality services (242). However, those are not the benefits that I see when using this specific system. Benefits to the company of increasing aid to or fixing this problem include but are not limited to: quicker order times, better table turnover, better employee knowledge, increased employer morale, faster checkouts, less managerial stress, and much more. Technological training is paramount to the understanding of the POS system procedures. Understanding the system reduces lag time between when an order is received and when delivery occurs (Ham 79). I will conduct my research based on articles and previous studies done by Buffalo Wild Wings. I will also head into the close Buffalo Wild Wings location in Laurel, MD to hopefully interview and gain perspective from some of the managers and workers in order to study if they have the same issues that I went through while working at Buffalo Wild Wings. In addition, I will collect data to see if increased training leads to better table times, tips, and better overall workplace morale.
To complete this research for this study, I will need a couple weeks to complete this study both effectively and efficiently. Also, I will fortunately not need any budget or money to invest because I have previously worked at BWW and can perform the study with off shift managers at opportune times during the slow parts of the wor.
Yelp's Review Filtering Algorithm PowerpointYao Yao
- Data scraping, cluster, stratify
- Feature creation from metadata, NLP sentiment, spelling, readability, deceptive and extreme text classifiers
- Balance and scale, logistic regression, feature selection, final model
- Find features that correspond to Yelp's Algorithm and evaluate
Video of presentation: https://youtu.be/uavbPKiUg9M
Poster: https://www.slideshare.net/YaoYao44/yelps-review-filtering-algorithm-poster
Paper:
Github: https://github.com/post2web/capstone
Restaurant recommendation system is a very popular service whose so-
phistication keeps increasing everyday.In this paper we present a per-
sonalised restaurant recommendation system which has two parts to
it. The rst part recommends users' restaurants based on their restau-
rant review history. The second part recommends business owners with
places perfect to open a restaurant with a particular cuisine where the
owner would get the best trac for the restaurant. Using Zomato data,
we built a restaurant recommendation system for the individuals and
business owners. For each user in our data we nd out the cuisine
preferences and other restrictions such as services oered, ambience,
average rating, etc. and based on that we recommend the restaurants
accordingly. We propose a metric that takes the popularity as well as
the sentiment of opinions for the food items based on the user gener-
ated reviews as opposed to other systems where which only consider
the features mentioned above to recommend restaurants.
The first public presentation of the next generation SEO tool, InfluenceFinder.
Using science to filter large lists of URL InfluenceFinder spiders link maps to identify actionable relevant and authoritative sites.
Consider it to be MajesticSEO and Linkscape on steroids
Search Engine Optimization (SEO) Seminar ReportNandu B Rajan
SEO Seminar Report.
Gives basic idea about Search Engine Optimization.
While nobody can guarantee top level positioning in search engine organic results, proper search engine optimization can help. Because the search engines, such as Google, Yahoo!, and Bing, are so important today it is necessary to make each page in a Web site conform to the principles of good SEO as much as possible.
To do this it is necessary to:
• Understand the basics of how search engines rate sites
• Use proper keywords and phrases throughout the Web site
• Avoid giving the appearance of spamming the search engines
• Write all text for real people, not just for search engines
• Use well-formed alternate attributes on images
• Make sure that the necessary meta tags (and title tag) are installed in the head of each Web page
• Have good incoming links to establish popularity
• Make sure the Web site is regularly updated so that the content is fresh
Search Engine Optimization (SEO) Seminar ReportNandu B Rajan
SEO Seminar Report.
Gives basic idea about Search Engine Optimization.
While nobody can guarantee top level positioning in search engine organic results, proper search engine optimization can help. Because the search engines, such as Google, Yahoo!, and Bing, are so important today it is necessary to make each page in a Web site conform to the principles of good SEO as much as possible.
To do this it is necessary to:
• Understand the basics of how search engines rate sites
• Use proper keywords and phrases throughout the Web site
• Avoid giving the appearance of spamming the search engines
• Write all text for real people, not just for search engines
• Use well-formed alternate attributes on images
• Make sure that the necessary meta tags (and title tag) are installed in the head of each Web page
• Have good incoming links to establish popularity
• Make sure the Web site is regularly updated so that the content is fresh
Complete step by step guide to learn DATA SCIENCE skills for scraping websites!!
Web scraping is an automatic method to obtain large amounts of data from websites. Most of this data is
unstructured data in an HTML format which is then converted into structured data in a spreadsheet or a database
so that it can be used in various applications.
There are many different ways to perform web scraping to obtain data from websites. These include using online
services, particular API’s or even creating your code for web scraping from scratch. Many large websites, like
Google, Twitter, Facebook, StackOverow, etc. have API’s that allow you to access their data in a structured
format.
This is the best option, but there are other sites that don’t allow users to access large amounts of data in a
structured form or they are simply not that technologically advanced. In that situation, it’s best to use Web
Scraping to scrape the website for data, To learn more checkout webscraping
Project Report of MMK (MomMade Kitchen) Semester-4
Our project is a website which is an online grocery store. The Internet has made all of our lives easier. You can do almost anything online anymore, including purchasing your groceries. A lot of people have actually come to prefer buying their groceries online today. This website allows users to buy groceries online which are needed in day to day life. This includes fruits, vegetables, pulses, breads etc. This is a user friendly website in which customer can view the item and price of the item it is buying. Whenever you purchase your groceries online you will be able to shop any time of the day or night, at your own convenience, regardless of what the weather outside may be, and still get everything that you need and want.
This study applies text mining to analyze customer reviews and automatically assign a collective restaurant star rating based on five predetermined aspects: ambiance, cost, food, hygiene, and service. The application provides a web and mobile crowd sourcing platform where users share dining experiences and get insights about the strengths and weaknesses of a restaurant through user contributed feedback. Text reviews are tokenized into sentences. Noun-adjective pairs are extracted from each sentence using Stanford Core NLP library and are associated to aspects based on the bag of associated words fed into the system. The sentiment weight of the adjectives is determined through AFINN library. An overall restaurant star rating is computed based on the individual aspect rating. Further, a word cloud is generated to provide visual display of the most frequently occurring terms in the reviews. The more feedbacks are added the more reflective the sentiment score to the restaurants’ performance.
TEXT MINING CUSTOMER REVIEWS FOR ASPECTBASED RESTAURANT RATING ijcsit
ABSTRACT
This study applies text mining to analyze customer reviews and automatically assign a collective restaurant star rating based on five predetermined aspects: ambiance, cost, food, hygiene, and service. The application provides a web and mobile crowd sourcing platform where users share dining experiences and get insights about the strengths and weaknesses of a restaurant through user contributed feedback. Text reviews are tokenized into sentences. Noun-adjective pairs are extracted from each sentence using Stanford Core NLP library and are associated to aspects based on the bag of associated words fed into the system. The sentiment weight of the adjectives is determined through AFINN library. An overall restaurant star rating is computed based on the individual aspect rating. Further, a word cloud is generated to provide visual display of the most frequently occurring terms in the reviews. The more feedbacks are added the more reflective the sentiment score to the restaurants’ performance.
Le nuove frontiere dell'AI nell'RPA con UiPath Autopilot™UiPathCommunity
In questo evento online gratuito, organizzato dalla Community Italiana di UiPath, potrai esplorare le nuove funzionalità di Autopilot, il tool che integra l'Intelligenza Artificiale nei processi di sviluppo e utilizzo delle Automazioni.
📕 Vedremo insieme alcuni esempi dell'utilizzo di Autopilot in diversi tool della Suite UiPath:
Autopilot per Studio Web
Autopilot per Studio
Autopilot per Apps
Clipboard AI
GenAI applicata alla Document Understanding
👨🏫👨💻 Speakers:
Stefano Negro, UiPath MVPx3, RPA Tech Lead @ BSP Consultant
Flavio Martinelli, UiPath MVP 2023, Technical Account Manager @UiPath
Andrei Tasca, RPA Solutions Team Lead @NTT Data
More Related Content
Similar to Ranked-Restaurant Searching System using Data Mining
Restaurant recommendation system is a very popular service whose so-
phistication keeps increasing everyday.In this paper we present a per-
sonalised restaurant recommendation system which has two parts to
it. The rst part recommends users' restaurants based on their restau-
rant review history. The second part recommends business owners with
places perfect to open a restaurant with a particular cuisine where the
owner would get the best trac for the restaurant. Using Zomato data,
we built a restaurant recommendation system for the individuals and
business owners. For each user in our data we nd out the cuisine
preferences and other restrictions such as services oered, ambience,
average rating, etc. and based on that we recommend the restaurants
accordingly. We propose a metric that takes the popularity as well as
the sentiment of opinions for the food items based on the user gener-
ated reviews as opposed to other systems where which only consider
the features mentioned above to recommend restaurants.
The first public presentation of the next generation SEO tool, InfluenceFinder.
Using science to filter large lists of URL InfluenceFinder spiders link maps to identify actionable relevant and authoritative sites.
Consider it to be MajesticSEO and Linkscape on steroids
Search Engine Optimization (SEO) Seminar ReportNandu B Rajan
SEO Seminar Report.
Gives basic idea about Search Engine Optimization.
While nobody can guarantee top level positioning in search engine organic results, proper search engine optimization can help. Because the search engines, such as Google, Yahoo!, and Bing, are so important today it is necessary to make each page in a Web site conform to the principles of good SEO as much as possible.
To do this it is necessary to:
• Understand the basics of how search engines rate sites
• Use proper keywords and phrases throughout the Web site
• Avoid giving the appearance of spamming the search engines
• Write all text for real people, not just for search engines
• Use well-formed alternate attributes on images
• Make sure that the necessary meta tags (and title tag) are installed in the head of each Web page
• Have good incoming links to establish popularity
• Make sure the Web site is regularly updated so that the content is fresh
Search Engine Optimization (SEO) Seminar ReportNandu B Rajan
SEO Seminar Report.
Gives basic idea about Search Engine Optimization.
While nobody can guarantee top level positioning in search engine organic results, proper search engine optimization can help. Because the search engines, such as Google, Yahoo!, and Bing, are so important today it is necessary to make each page in a Web site conform to the principles of good SEO as much as possible.
To do this it is necessary to:
• Understand the basics of how search engines rate sites
• Use proper keywords and phrases throughout the Web site
• Avoid giving the appearance of spamming the search engines
• Write all text for real people, not just for search engines
• Use well-formed alternate attributes on images
• Make sure that the necessary meta tags (and title tag) are installed in the head of each Web page
• Have good incoming links to establish popularity
• Make sure the Web site is regularly updated so that the content is fresh
Complete step by step guide to learn DATA SCIENCE skills for scraping websites!!
Web scraping is an automatic method to obtain large amounts of data from websites. Most of this data is
unstructured data in an HTML format which is then converted into structured data in a spreadsheet or a database
so that it can be used in various applications.
There are many different ways to perform web scraping to obtain data from websites. These include using online
services, particular API’s or even creating your code for web scraping from scratch. Many large websites, like
Google, Twitter, Facebook, StackOverow, etc. have API’s that allow you to access their data in a structured
format.
This is the best option, but there are other sites that don’t allow users to access large amounts of data in a
structured form or they are simply not that technologically advanced. In that situation, it’s best to use Web
Scraping to scrape the website for data, To learn more checkout webscraping
Project Report of MMK (MomMade Kitchen) Semester-4
Our project is a website which is an online grocery store. The Internet has made all of our lives easier. You can do almost anything online anymore, including purchasing your groceries. A lot of people have actually come to prefer buying their groceries online today. This website allows users to buy groceries online which are needed in day to day life. This includes fruits, vegetables, pulses, breads etc. This is a user friendly website in which customer can view the item and price of the item it is buying. Whenever you purchase your groceries online you will be able to shop any time of the day or night, at your own convenience, regardless of what the weather outside may be, and still get everything that you need and want.
This study applies text mining to analyze customer reviews and automatically assign a collective restaurant star rating based on five predetermined aspects: ambiance, cost, food, hygiene, and service. The application provides a web and mobile crowd sourcing platform where users share dining experiences and get insights about the strengths and weaknesses of a restaurant through user contributed feedback. Text reviews are tokenized into sentences. Noun-adjective pairs are extracted from each sentence using Stanford Core NLP library and are associated to aspects based on the bag of associated words fed into the system. The sentiment weight of the adjectives is determined through AFINN library. An overall restaurant star rating is computed based on the individual aspect rating. Further, a word cloud is generated to provide visual display of the most frequently occurring terms in the reviews. The more feedbacks are added the more reflective the sentiment score to the restaurants’ performance.
TEXT MINING CUSTOMER REVIEWS FOR ASPECTBASED RESTAURANT RATING ijcsit
ABSTRACT
This study applies text mining to analyze customer reviews and automatically assign a collective restaurant star rating based on five predetermined aspects: ambiance, cost, food, hygiene, and service. The application provides a web and mobile crowd sourcing platform where users share dining experiences and get insights about the strengths and weaknesses of a restaurant through user contributed feedback. Text reviews are tokenized into sentences. Noun-adjective pairs are extracted from each sentence using Stanford Core NLP library and are associated to aspects based on the bag of associated words fed into the system. The sentiment weight of the adjectives is determined through AFINN library. An overall restaurant star rating is computed based on the individual aspect rating. Further, a word cloud is generated to provide visual display of the most frequently occurring terms in the reviews. The more feedbacks are added the more reflective the sentiment score to the restaurants’ performance.
Le nuove frontiere dell'AI nell'RPA con UiPath Autopilot™UiPathCommunity
In questo evento online gratuito, organizzato dalla Community Italiana di UiPath, potrai esplorare le nuove funzionalità di Autopilot, il tool che integra l'Intelligenza Artificiale nei processi di sviluppo e utilizzo delle Automazioni.
📕 Vedremo insieme alcuni esempi dell'utilizzo di Autopilot in diversi tool della Suite UiPath:
Autopilot per Studio Web
Autopilot per Studio
Autopilot per Apps
Clipboard AI
GenAI applicata alla Document Understanding
👨🏫👨💻 Speakers:
Stefano Negro, UiPath MVPx3, RPA Tech Lead @ BSP Consultant
Flavio Martinelli, UiPath MVP 2023, Technical Account Manager @UiPath
Andrei Tasca, RPA Solutions Team Lead @NTT Data
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
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In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
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GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
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23. • Supervised Learning
Someone gives us examples and the right answer
• Unsupervised Learning
We see examples but get no feedback
We need to find patterns in the data
• Semi-supervised Learning
Given a small number of examples with the right answers, we need to
find patterns in the data, so that we can predict the right answer for
unseen examples
• Reinforcement Learning
We take actions and get rewards
Have to learn how to get high rewards
Different Kinds of Learning
24. Make learning set with results preview
for judging
whether it is related to restaurant review or not
We need to classify them!
34. p(Is | s)
p(s | Is) *p(Is)
p(s)
p(ls) : possibility of choosing Is_Restaurant(assume as 0.5)
p(s | ls) : In Is_Restaurant set
possibility of choosing the given sentece
35. p(Is | s)
p(s | Is) * p(Is)
p(s)
p(s | ls) : In Is_Restaurant set,
possibility of choosing the given sentence
p(s | Is) = p(word | Is) * p(word | Is) * p(word | Is)…
We assume that each word cannot affect to each other...
36. Is_Restaurant
“It was good to eat delicious pasta in this restaurant!”
p(Is) = 0.5
p(restaurant | Is) =
1000
250
p(delicious | Is) =
1000
300
p(good | Is) =
1000
250
p(eat | Is) =
1000
0(1)
restaurant
delicious
good
200
250
300
250
word count
atmosphere
37. “It was good to eat delicious pasta in this restaurant!”
0.5 * 0.001 * 0.3 * 0.25 * 0.25 =
9.375 * 10^-6
Is_Restaurant
restaurant
delicious
good
200
250
300
250
word count
atmosphere
38. 0.001 ^ 4 = 1 * 10^-12
“It was good to eat delicious pasta in this restaurant!”
Is_Not_Restaurant
politic
hello
economy
atmosphere
350
200
150
300
word count
39. This is related to restaurant review!
with upper table...
“It was good to eat delicious pasta in this restaurant!”
45. Title Date Content Reply URL
Database for Blogs
Point for one blog review
= (date * 0.5) + (the number of reply * 0.5)
Point for one restaurant
= the average of reviews’ point
54. What we want to develop...
Improve filtering blog performance
On Ruby on Rails framework, using BlackLight
served as library to use Solr in Ruby on Rails
Front-End Design using HTML5, CSS3, Javascript, Jquery...
New Ranking Algorithm
55. Point of Restaurant =
Points of Blogger = Recency, Frequency, Density
Recency, Frequency, People
Based on Blogger’s action
New Ranking Algorithm
61. 서가앤쿡 환여횟집 빕스 설빙 뚝배기 이탈리아
삼겹살 맛집 냉면 맛집 샤브샤브 맛집 보리밥 맛집 초밥 맛집
99% 96% 93% 100% 98%
100% 100% 100% 100% 100%
100 blogs related to Is_Restaurant
Performance
For one keywords => 10 blogs
62. 정치 경제 문화 사회 한동대학교
소녀시대 중앙일보 한겨례 성경 기독교
59% 66% 45% 65% 42%
81% 67% 62% 54% 58%
100 blogs related to Is_Not_Restaurant
Performance
For one keywords => 10 blogs