SlideShare una empresa de Scribd logo
1 de 27
Preliminary Proposal, Stockholm 18 January 2012



                Marina Santini
   CityTimes is an aggregator. It offers pieces of
    local news that can be found on local
    newspapers + city life information, such as
    restaurants, shops, products, activities for
    children (through advertising or information
    flows coming from social media).
   http://www.santini.se/geotimes/

   Shows recent news in reversed chronological order from different
    sources.
   Positions news on the map and diplays the excerpts in the
    bubbles.
    When users browse another page, the news and the map change
    accordingly and the markers on the map are built dynamically

   Examples of Social Media Potential
       Information flows from other websites (for ex, through Twitter)
       Connection to social networks (ex, Like and Follow)
       Local ads
       Google AdSense
       Basically all kind of local information can be included in this kind of
        website.
   WP interface
   WP API
   Personal code
   Template tags (ex: the_title(), the_permalink(),
    etc.)

   Handy!
   the visualization of the map with the markers.
   the storage of the full article in the database
    and its visualization, and other functions .
   WARNING: No refactoring has been applied
    yet, so the code contains duplications.

   Automatically download RSS feeds from
    newspapers at regular intervals (plug-in)
   Automatically tag RSS feeds with locations
    (plug-in)
   Based on the location tags, markers are shown
    on a map. Marker’s info windows show the
    RSS feed located in that part of the city. When
    no location are mentioned in the RSS feed text,
    the default location is the neighborhood of the
    local newspaper (my code)
   When clicking on a single post, the user will
    see the locations contained in the post on the
    map, and has the possibility of reading the
    extended version of the article and then the
    full article in the newspaper website (my code)
The string "slussen, stockholm, sweden"
returns many different locations
   Result Address: Slussen T-bana, Stockholm urban
    area, Sweden
   Result Address: Östra Slussgatan, Stockholm urban
    area, Sweden
   Result Address: Hammarby Slussväg, 118 60
    Stockholm, Sweden
   Lia’s Geotimes’ website has not yet a mobile
    version.
   Wordpress mobile has been released recently
GTRO can be saved on the home screen (like a
    native app) and started directly from the
    mobile.

1) Find Directions
2) Optimize Routes
3) Store My Locations
4) Find Products on the map

   (GTRO2) http://www.santini.se/gtro2/
   iOS native apps
       . iOS apps resemble the built-in applications on iOS-
        based devices in that they reside on the device itself
        and take advantage of the features of the iOS
        environment.
   Web Content
       Web content is hosted by a website that people visit
        through their iOS-based device
         Web apps
         Optimized web pages
         Compatible web pages
   I am inclined to think that the "web-based app"
    way is the best way to go. But, of course, it
    depends...
       This choice might be a little limited at present, but
        emulation frameworks (e.g. iWebKit5) or special libraries
        (e.g JQuery Mobile) are growing fast. Handy with “Add
        to Home Screen” to access the web app directly, just like a
        native app.

   In general, it is possible and fast to create a good
    web app without going native. Additionally, it is
    much easier to capitalize on a web app (in terms of
    reuse on different platforms) rather than on a
    native app, I think.
   In this project, I overlooked that ”APPS RESPOND
    TO GESTURES, NOT CLICKS”. I overlooked the
    gestural context and I focussed on the features.

   Designing an app is different from designing an website
    because an app require s a different behaviour, a
    different posture, a different manuality. An app must be
    thought as an app from the beginning, and not adapted
    along the way.

   It is not only a matter of ”STYLING” (colors, round
    corners, all the JQTouch stuff).
   It is a matter of human-mobile interaction!
   Move from GoogleMaps V2 to GoogleMaps V3
   Speed up the map visualization
   Create an automatized colored classification function, so user
    can visually identify the type of news they are interested in (ex,
    sport, politics, crime, fashion, etc.)
   Create a computational advertising function, where the ads
    relevant to news locations (ex, local shops, local cinema, etc.) are
    automatically displayed, and not manually put up.
   Create a automatic comment analysis function to identify users’
    problems, orientations, sentiments, opinions and attitudes.
   Study more the target user group in order to see if CityTimes’
    information architecture matches the audience’ needs (usuability
    is ok, I think, because WordPress is very handy for many types
    of users).
   Create a CityTimes’ mobile version
   Create two versions of the Route Optimizer application
    (website and app) and integrate them with? CityTimes.
   The Service:

    You can create your own service, managed by
    Grannar (for ex), which would offer additional
    local information to what you already offer
    now. For example, suggestions for local
    restaurants and their reputation (through
    users’ reviews), where to find a open
    pharmacy in the vicinity on Sunday night, etc.
   The Blueprint:

  you can sell model or create a kind of
  franchising model ”powered by CityTimes”, that
  can be used as a standlone website or be
  appended to the main website. For example,
  for tourism and leisure time:
”Visit Stockholm”
  (http://www.visitstockholm.com/en/)
”TimeOut ”
  (http://www.timeout.com/stockholm/)
CityTimes
CityTimes

Más contenido relacionado

Destacado

Lecture 7: Learning from Massive Datasets
Lecture 7: Learning from Massive DatasetsLecture 7: Learning from Massive Datasets
Lecture 7: Learning from Massive DatasetsMarina Santini
 
Lecture 6: Hidden Variables and Expectation-Maximization
Lecture 6: Hidden Variables and Expectation-MaximizationLecture 6: Hidden Variables and Expectation-Maximization
Lecture 6: Hidden Variables and Expectation-MaximizationMarina Santini
 
Lecture 5: Interval Estimation
Lecture 5: Interval Estimation Lecture 5: Interval Estimation
Lecture 5: Interval Estimation Marina Santini
 
Lecture 2: From Semantics To Semantic-Oriented Applications
Lecture 2: From Semantics To Semantic-Oriented ApplicationsLecture 2: From Semantics To Semantic-Oriented Applications
Lecture 2: From Semantics To Semantic-Oriented ApplicationsMarina Santini
 
Text analytics and R - Open Question: is it a good match?
Text analytics and R - Open Question: is it a good match?Text analytics and R - Open Question: is it a good match?
Text analytics and R - Open Question: is it a good match?Marina Santini
 
Lecture 3: Semantic Role Labelling
Lecture 3: Semantic Role LabellingLecture 3: Semantic Role Labelling
Lecture 3: Semantic Role LabellingMarina Santini
 
Mathematics for Language Technology: Introduction to Probability Theory
Mathematics for Language Technology: Introduction to Probability TheoryMathematics for Language Technology: Introduction to Probability Theory
Mathematics for Language Technology: Introduction to Probability TheoryMarina Santini
 
Lecture 2: Preliminaries (Understanding and Preprocessing data)
Lecture 2: Preliminaries (Understanding and Preprocessing data)Lecture 2: Preliminaries (Understanding and Preprocessing data)
Lecture 2: Preliminaries (Understanding and Preprocessing data)Marina Santini
 
Lecture 03: Machine Learning for Language Technology - Linear Classifiers
Lecture 03: Machine Learning for Language Technology - Linear ClassifiersLecture 03: Machine Learning for Language Technology - Linear Classifiers
Lecture 03: Machine Learning for Language Technology - Linear ClassifiersMarina Santini
 
Lecture 10: SVM and MIRA
Lecture 10: SVM and MIRALecture 10: SVM and MIRA
Lecture 10: SVM and MIRAMarina Santini
 
Lecture 01: Machine Learning for Language Technology - Introduction
 Lecture 01: Machine Learning for Language Technology - Introduction Lecture 01: Machine Learning for Language Technology - Introduction
Lecture 01: Machine Learning for Language Technology - IntroductionMarina Santini
 
Lecture 4: The Weka Package
Lecture 4: The Weka PackageLecture 4: The Weka Package
Lecture 4: The Weka PackageMarina Santini
 
Semantics and Computational Semantics
Semantics and Computational SemanticsSemantics and Computational Semantics
Semantics and Computational SemanticsMarina Santini
 
Lecture 02: Machine Learning for Language Technology - Decision Trees and Nea...
Lecture 02: Machine Learning for Language Technology - Decision Trees and Nea...Lecture 02: Machine Learning for Language Technology - Decision Trees and Nea...
Lecture 02: Machine Learning for Language Technology - Decision Trees and Nea...Marina Santini
 
Lecture: Ontologies and the Semantic Web
Lecture: Ontologies and the Semantic WebLecture: Ontologies and the Semantic Web
Lecture: Ontologies and the Semantic WebMarina Santini
 
Lecture 1: Introduction to the Course (Practical Information)
Lecture 1: Introduction to the Course (Practical Information)Lecture 1: Introduction to the Course (Practical Information)
Lecture 1: Introduction to the Course (Practical Information)Marina Santini
 
Lecture 9: Machine Learning in Practice (2)
Lecture 9: Machine Learning in Practice (2)Lecture 9: Machine Learning in Practice (2)
Lecture 9: Machine Learning in Practice (2)Marina Santini
 
Lecture: Vector Semantics (aka Distributional Semantics)
Lecture: Vector Semantics (aka Distributional Semantics)Lecture: Vector Semantics (aka Distributional Semantics)
Lecture: Vector Semantics (aka Distributional Semantics)Marina Santini
 
09 semantic web & ontologies
09 semantic web & ontologies09 semantic web & ontologies
09 semantic web & ontologiesMarina Santini
 
Lecture 8: Machine Learning in Practice (1)
Lecture 8: Machine Learning in Practice (1) Lecture 8: Machine Learning in Practice (1)
Lecture 8: Machine Learning in Practice (1) Marina Santini
 

Destacado (20)

Lecture 7: Learning from Massive Datasets
Lecture 7: Learning from Massive DatasetsLecture 7: Learning from Massive Datasets
Lecture 7: Learning from Massive Datasets
 
Lecture 6: Hidden Variables and Expectation-Maximization
Lecture 6: Hidden Variables and Expectation-MaximizationLecture 6: Hidden Variables and Expectation-Maximization
Lecture 6: Hidden Variables and Expectation-Maximization
 
Lecture 5: Interval Estimation
Lecture 5: Interval Estimation Lecture 5: Interval Estimation
Lecture 5: Interval Estimation
 
Lecture 2: From Semantics To Semantic-Oriented Applications
Lecture 2: From Semantics To Semantic-Oriented ApplicationsLecture 2: From Semantics To Semantic-Oriented Applications
Lecture 2: From Semantics To Semantic-Oriented Applications
 
Text analytics and R - Open Question: is it a good match?
Text analytics and R - Open Question: is it a good match?Text analytics and R - Open Question: is it a good match?
Text analytics and R - Open Question: is it a good match?
 
Lecture 3: Semantic Role Labelling
Lecture 3: Semantic Role LabellingLecture 3: Semantic Role Labelling
Lecture 3: Semantic Role Labelling
 
Mathematics for Language Technology: Introduction to Probability Theory
Mathematics for Language Technology: Introduction to Probability TheoryMathematics for Language Technology: Introduction to Probability Theory
Mathematics for Language Technology: Introduction to Probability Theory
 
Lecture 2: Preliminaries (Understanding and Preprocessing data)
Lecture 2: Preliminaries (Understanding and Preprocessing data)Lecture 2: Preliminaries (Understanding and Preprocessing data)
Lecture 2: Preliminaries (Understanding and Preprocessing data)
 
Lecture 03: Machine Learning for Language Technology - Linear Classifiers
Lecture 03: Machine Learning for Language Technology - Linear ClassifiersLecture 03: Machine Learning for Language Technology - Linear Classifiers
Lecture 03: Machine Learning for Language Technology - Linear Classifiers
 
Lecture 10: SVM and MIRA
Lecture 10: SVM and MIRALecture 10: SVM and MIRA
Lecture 10: SVM and MIRA
 
Lecture 01: Machine Learning for Language Technology - Introduction
 Lecture 01: Machine Learning for Language Technology - Introduction Lecture 01: Machine Learning for Language Technology - Introduction
Lecture 01: Machine Learning for Language Technology - Introduction
 
Lecture 4: The Weka Package
Lecture 4: The Weka PackageLecture 4: The Weka Package
Lecture 4: The Weka Package
 
Semantics and Computational Semantics
Semantics and Computational SemanticsSemantics and Computational Semantics
Semantics and Computational Semantics
 
Lecture 02: Machine Learning for Language Technology - Decision Trees and Nea...
Lecture 02: Machine Learning for Language Technology - Decision Trees and Nea...Lecture 02: Machine Learning for Language Technology - Decision Trees and Nea...
Lecture 02: Machine Learning for Language Technology - Decision Trees and Nea...
 
Lecture: Ontologies and the Semantic Web
Lecture: Ontologies and the Semantic WebLecture: Ontologies and the Semantic Web
Lecture: Ontologies and the Semantic Web
 
Lecture 1: Introduction to the Course (Practical Information)
Lecture 1: Introduction to the Course (Practical Information)Lecture 1: Introduction to the Course (Practical Information)
Lecture 1: Introduction to the Course (Practical Information)
 
Lecture 9: Machine Learning in Practice (2)
Lecture 9: Machine Learning in Practice (2)Lecture 9: Machine Learning in Practice (2)
Lecture 9: Machine Learning in Practice (2)
 
Lecture: Vector Semantics (aka Distributional Semantics)
Lecture: Vector Semantics (aka Distributional Semantics)Lecture: Vector Semantics (aka Distributional Semantics)
Lecture: Vector Semantics (aka Distributional Semantics)
 
09 semantic web & ontologies
09 semantic web & ontologies09 semantic web & ontologies
09 semantic web & ontologies
 
Lecture 8: Machine Learning in Practice (1)
Lecture 8: Machine Learning in Practice (1) Lecture 8: Machine Learning in Practice (1)
Lecture 8: Machine Learning in Practice (1)
 

Similar a CityTimes

Customized check in procedures
Customized check in proceduresCustomized check in procedures
Customized check in proceduresColdbeans Software
 
Augmented Reality Social Media Mobile Application
Augmented Reality Social Media Mobile Application Augmented Reality Social Media Mobile Application
Augmented Reality Social Media Mobile Application ManekTech
 
Marketing - ShrinkRay
Marketing - ShrinkRayMarketing - ShrinkRay
Marketing - ShrinkRayBLUERUSH
 
Mconf14 aperto vanity url-app
Mconf14 aperto vanity url-appMconf14 aperto vanity url-app
Mconf14 aperto vanity url-appAperto Nachname
 
There is an App for...Vanity URLs
There is an App for...Vanity URLsThere is an App for...Vanity URLs
There is an App for...Vanity URLsMagnolia
 
Responsive Web Design vs. Mobile Web App: What is best for Enterprise - Whit...
Responsive Web Design vs. Mobile Web App:  What is best for Enterprise - Whit...Responsive Web Design vs. Mobile Web App:  What is best for Enterprise - Whit...
Responsive Web Design vs. Mobile Web App: What is best for Enterprise - Whit...RapidValue
 
Mobile Calendar Application - Tourism Development Company
Mobile Calendar Application - Tourism Development CompanyMobile Calendar Application - Tourism Development Company
Mobile Calendar Application - Tourism Development CompanyStacy-Ann Duhaney
 
Gps based search coupons on map view ios, android mobile application
Gps based search coupons on map view   ios, android mobile applicationGps based search coupons on map view   ios, android mobile application
Gps based search coupons on map view ios, android mobile applicationMike Taylor
 
Code4EU @Open Knowledge Foundation, OKFest Geneve 25 sept 2013
Code4EU @Open Knowledge Foundation, OKFest Geneve 25 sept 2013Code4EU @Open Knowledge Foundation, OKFest Geneve 25 sept 2013
Code4EU @Open Knowledge Foundation, OKFest Geneve 25 sept 2013Katalin Gallyas
 
Creating Mobile Aps without Coding
Creating Mobile Aps without CodingCreating Mobile Aps without Coding
Creating Mobile Aps without CodingJack Molisani
 
Advertising in social networks
Advertising in social networksAdvertising in social networks
Advertising in social networksColdbeans Software
 
Multiscreen and Beyond – Topics and Focus Areas (Wolfram Nagel)
Multiscreen and Beyond – Topics and Focus Areas (Wolfram Nagel)Multiscreen and Beyond – Topics and Focus Areas (Wolfram Nagel)
Multiscreen and Beyond – Topics and Focus Areas (Wolfram Nagel)Wolfram Nagel
 

Similar a CityTimes (20)

Customized check in procedures
Customized check in proceduresCustomized check in procedures
Customized check in procedures
 
Augmented Reality Social Media Mobile Application
Augmented Reality Social Media Mobile Application Augmented Reality Social Media Mobile Application
Augmented Reality Social Media Mobile Application
 
Mobile App Testing
Mobile App TestingMobile App Testing
Mobile App Testing
 
Marketing - ShrinkRay
Marketing - ShrinkRayMarketing - ShrinkRay
Marketing - ShrinkRay
 
Mconf14 aperto vanity url-app
Mconf14 aperto vanity url-appMconf14 aperto vanity url-app
Mconf14 aperto vanity url-app
 
There is an App for...Vanity URLs
There is an App for...Vanity URLsThere is an App for...Vanity URLs
There is an App for...Vanity URLs
 
Barcamp
BarcampBarcamp
Barcamp
 
Data Visualization
Data VisualizationData Visualization
Data Visualization
 
Responsive Web Design vs. Mobile Web App: What is best for Enterprise - Whit...
Responsive Web Design vs. Mobile Web App:  What is best for Enterprise - Whit...Responsive Web Design vs. Mobile Web App:  What is best for Enterprise - Whit...
Responsive Web Design vs. Mobile Web App: What is best for Enterprise - Whit...
 
Mobile Calendar Application - Tourism Development Company
Mobile Calendar Application - Tourism Development CompanyMobile Calendar Application - Tourism Development Company
Mobile Calendar Application - Tourism Development Company
 
Mobile seo
Mobile seoMobile seo
Mobile seo
 
Gps based search coupons on map view ios, android mobile application
Gps based search coupons on map view   ios, android mobile applicationGps based search coupons on map view   ios, android mobile application
Gps based search coupons on map view ios, android mobile application
 
Code4EU @Open Knowledge Foundation, OKFest Geneve 25 sept 2013
Code4EU @Open Knowledge Foundation, OKFest Geneve 25 sept 2013Code4EU @Open Knowledge Foundation, OKFest Geneve 25 sept 2013
Code4EU @Open Knowledge Foundation, OKFest Geneve 25 sept 2013
 
Creating Mobile Aps without Coding
Creating Mobile Aps without CodingCreating Mobile Aps without Coding
Creating Mobile Aps without Coding
 
Advertising in social networks
Advertising in social networksAdvertising in social networks
Advertising in social networks
 
Multiscreen and Beyond – Topics and Focus Areas (Wolfram Nagel)
Multiscreen and Beyond – Topics and Focus Areas (Wolfram Nagel)Multiscreen and Beyond – Topics and Focus Areas (Wolfram Nagel)
Multiscreen and Beyond – Topics and Focus Areas (Wolfram Nagel)
 
Microsoft Virtuele Earth
Microsoft Virtuele EarthMicrosoft Virtuele Earth
Microsoft Virtuele Earth
 
Meridian Platform Data Sheet
Meridian Platform Data SheetMeridian Platform Data Sheet
Meridian Platform Data Sheet
 
Presentation fyp 1
Presentation fyp 1Presentation fyp 1
Presentation fyp 1
 
Report_Maryna Razakhatskaya
Report_Maryna RazakhatskayaReport_Maryna Razakhatskaya
Report_Maryna Razakhatskaya
 

Más de Marina Santini

Can We Quantify Domainhood? Exploring Measures to Assess Domain-Specificity i...
Can We Quantify Domainhood? Exploring Measures to Assess Domain-Specificity i...Can We Quantify Domainhood? Exploring Measures to Assess Domain-Specificity i...
Can We Quantify Domainhood? Exploring Measures to Assess Domain-Specificity i...Marina Santini
 
Towards a Quality Assessment of Web Corpora for Language Technology Applications
Towards a Quality Assessment of Web Corpora for Language Technology ApplicationsTowards a Quality Assessment of Web Corpora for Language Technology Applications
Towards a Quality Assessment of Web Corpora for Language Technology ApplicationsMarina Santini
 
A Web Corpus for eCare: Collection, Lay Annotation and Learning -First Results-
A Web Corpus for eCare: Collection, Lay Annotation and Learning -First Results-A Web Corpus for eCare: Collection, Lay Annotation and Learning -First Results-
A Web Corpus for eCare: Collection, Lay Annotation and Learning -First Results-Marina Santini
 
An Exploratory Study on Genre Classification using Readability Features
An Exploratory Study on Genre Classification using Readability FeaturesAn Exploratory Study on Genre Classification using Readability Features
An Exploratory Study on Genre Classification using Readability FeaturesMarina Santini
 
Lecture: Semantic Word Clouds
Lecture: Semantic Word CloudsLecture: Semantic Word Clouds
Lecture: Semantic Word CloudsMarina Santini
 
Lecture: Summarization
Lecture: SummarizationLecture: Summarization
Lecture: SummarizationMarina Santini
 
Lecture: Question Answering
Lecture: Question AnsweringLecture: Question Answering
Lecture: Question AnsweringMarina Santini
 
IE: Named Entity Recognition (NER)
IE: Named Entity Recognition (NER)IE: Named Entity Recognition (NER)
IE: Named Entity Recognition (NER)Marina Santini
 
Lecture: Word Sense Disambiguation
Lecture: Word Sense DisambiguationLecture: Word Sense Disambiguation
Lecture: Word Sense DisambiguationMarina Santini
 
Semantic Role Labeling
Semantic Role LabelingSemantic Role Labeling
Semantic Role LabelingMarina Santini
 
Lecture 4 Decision Trees (2): Entropy, Information Gain, Gain Ratio
Lecture 4 Decision Trees (2): Entropy, Information Gain, Gain RatioLecture 4 Decision Trees (2): Entropy, Information Gain, Gain Ratio
Lecture 4 Decision Trees (2): Entropy, Information Gain, Gain RatioMarina Santini
 
Lecture 3b: Decision Trees (1 part)
Lecture 3b: Decision Trees (1 part)Lecture 3b: Decision Trees (1 part)
Lecture 3b: Decision Trees (1 part) Marina Santini
 
Lecture 3: Basic Concepts of Machine Learning - Induction & Evaluation
Lecture 3: Basic Concepts of Machine Learning - Induction & EvaluationLecture 3: Basic Concepts of Machine Learning - Induction & Evaluation
Lecture 3: Basic Concepts of Machine Learning - Induction & EvaluationMarina Santini
 
Lecture 1: What is Machine Learning?
Lecture 1: What is Machine Learning?Lecture 1: What is Machine Learning?
Lecture 1: What is Machine Learning?Marina Santini
 
Lecture: Joint, Conditional and Marginal Probabilities
Lecture: Joint, Conditional and Marginal Probabilities Lecture: Joint, Conditional and Marginal Probabilities
Lecture: Joint, Conditional and Marginal Probabilities Marina Santini
 
Lecture: Context-Free Grammars
Lecture: Context-Free GrammarsLecture: Context-Free Grammars
Lecture: Context-Free GrammarsMarina Santini
 
Lecture: Regular Expressions and Regular Languages
Lecture: Regular Expressions and Regular LanguagesLecture: Regular Expressions and Regular Languages
Lecture: Regular Expressions and Regular LanguagesMarina Santini
 

Más de Marina Santini (20)

Can We Quantify Domainhood? Exploring Measures to Assess Domain-Specificity i...
Can We Quantify Domainhood? Exploring Measures to Assess Domain-Specificity i...Can We Quantify Domainhood? Exploring Measures to Assess Domain-Specificity i...
Can We Quantify Domainhood? Exploring Measures to Assess Domain-Specificity i...
 
Towards a Quality Assessment of Web Corpora for Language Technology Applications
Towards a Quality Assessment of Web Corpora for Language Technology ApplicationsTowards a Quality Assessment of Web Corpora for Language Technology Applications
Towards a Quality Assessment of Web Corpora for Language Technology Applications
 
A Web Corpus for eCare: Collection, Lay Annotation and Learning -First Results-
A Web Corpus for eCare: Collection, Lay Annotation and Learning -First Results-A Web Corpus for eCare: Collection, Lay Annotation and Learning -First Results-
A Web Corpus for eCare: Collection, Lay Annotation and Learning -First Results-
 
An Exploratory Study on Genre Classification using Readability Features
An Exploratory Study on Genre Classification using Readability FeaturesAn Exploratory Study on Genre Classification using Readability Features
An Exploratory Study on Genre Classification using Readability Features
 
Lecture: Semantic Word Clouds
Lecture: Semantic Word CloudsLecture: Semantic Word Clouds
Lecture: Semantic Word Clouds
 
Lecture: Summarization
Lecture: SummarizationLecture: Summarization
Lecture: Summarization
 
Relation Extraction
Relation ExtractionRelation Extraction
Relation Extraction
 
Lecture: Question Answering
Lecture: Question AnsweringLecture: Question Answering
Lecture: Question Answering
 
IE: Named Entity Recognition (NER)
IE: Named Entity Recognition (NER)IE: Named Entity Recognition (NER)
IE: Named Entity Recognition (NER)
 
Lecture: Word Sense Disambiguation
Lecture: Word Sense DisambiguationLecture: Word Sense Disambiguation
Lecture: Word Sense Disambiguation
 
Lecture: Word Senses
Lecture: Word SensesLecture: Word Senses
Lecture: Word Senses
 
Sentiment Analysis
Sentiment AnalysisSentiment Analysis
Sentiment Analysis
 
Semantic Role Labeling
Semantic Role LabelingSemantic Role Labeling
Semantic Role Labeling
 
Lecture 4 Decision Trees (2): Entropy, Information Gain, Gain Ratio
Lecture 4 Decision Trees (2): Entropy, Information Gain, Gain RatioLecture 4 Decision Trees (2): Entropy, Information Gain, Gain Ratio
Lecture 4 Decision Trees (2): Entropy, Information Gain, Gain Ratio
 
Lecture 3b: Decision Trees (1 part)
Lecture 3b: Decision Trees (1 part)Lecture 3b: Decision Trees (1 part)
Lecture 3b: Decision Trees (1 part)
 
Lecture 3: Basic Concepts of Machine Learning - Induction & Evaluation
Lecture 3: Basic Concepts of Machine Learning - Induction & EvaluationLecture 3: Basic Concepts of Machine Learning - Induction & Evaluation
Lecture 3: Basic Concepts of Machine Learning - Induction & Evaluation
 
Lecture 1: What is Machine Learning?
Lecture 1: What is Machine Learning?Lecture 1: What is Machine Learning?
Lecture 1: What is Machine Learning?
 
Lecture: Joint, Conditional and Marginal Probabilities
Lecture: Joint, Conditional and Marginal Probabilities Lecture: Joint, Conditional and Marginal Probabilities
Lecture: Joint, Conditional and Marginal Probabilities
 
Lecture: Context-Free Grammars
Lecture: Context-Free GrammarsLecture: Context-Free Grammars
Lecture: Context-Free Grammars
 
Lecture: Regular Expressions and Regular Languages
Lecture: Regular Expressions and Regular LanguagesLecture: Regular Expressions and Regular Languages
Lecture: Regular Expressions and Regular Languages
 

Último

A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)Gabriella Davis
 
Driving Behavioral Change for Information Management through Data-Driven Gree...
Driving Behavioral Change for Information Management through Data-Driven Gree...Driving Behavioral Change for Information Management through Data-Driven Gree...
Driving Behavioral Change for Information Management through Data-Driven Gree...Enterprise Knowledge
 
IAC 2024 - IA Fast Track to Search Focused AI Solutions
IAC 2024 - IA Fast Track to Search Focused AI SolutionsIAC 2024 - IA Fast Track to Search Focused AI Solutions
IAC 2024 - IA Fast Track to Search Focused AI SolutionsEnterprise Knowledge
 
Tata AIG General Insurance Company - Insurer Innovation Award 2024
Tata AIG General Insurance Company - Insurer Innovation Award 2024Tata AIG General Insurance Company - Insurer Innovation Award 2024
Tata AIG General Insurance Company - Insurer Innovation Award 2024The Digital Insurer
 
🐬 The future of MySQL is Postgres 🐘
🐬  The future of MySQL is Postgres   🐘🐬  The future of MySQL is Postgres   🐘
🐬 The future of MySQL is Postgres 🐘RTylerCroy
 
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...apidays
 
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdfThe Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdfEnterprise Knowledge
 
How to convert PDF to text with Nanonets
How to convert PDF to text with NanonetsHow to convert PDF to text with Nanonets
How to convert PDF to text with Nanonetsnaman860154
 
How to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerHow to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerThousandEyes
 
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
04-2024-HHUG-Sales-and-Marketing-Alignment.pptxHampshireHUG
 
The Codex of Business Writing Software for Real-World Solutions 2.pptx
The Codex of Business Writing Software for Real-World Solutions 2.pptxThe Codex of Business Writing Software for Real-World Solutions 2.pptx
The Codex of Business Writing Software for Real-World Solutions 2.pptxMalak Abu Hammad
 
Axa Assurance Maroc - Insurer Innovation Award 2024
Axa Assurance Maroc - Insurer Innovation Award 2024Axa Assurance Maroc - Insurer Innovation Award 2024
Axa Assurance Maroc - Insurer Innovation Award 2024The Digital Insurer
 
WhatsApp 9892124323 ✓Call Girls In Kalyan ( Mumbai ) secure service
WhatsApp 9892124323 ✓Call Girls In Kalyan ( Mumbai ) secure serviceWhatsApp 9892124323 ✓Call Girls In Kalyan ( Mumbai ) secure service
WhatsApp 9892124323 ✓Call Girls In Kalyan ( Mumbai ) secure servicePooja Nehwal
 
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...Miguel Araújo
 
Handwritten Text Recognition for manuscripts and early printed texts
Handwritten Text Recognition for manuscripts and early printed textsHandwritten Text Recognition for manuscripts and early printed texts
Handwritten Text Recognition for manuscripts and early printed textsMaria Levchenko
 
08448380779 Call Girls In Greater Kailash - I Women Seeking Men
08448380779 Call Girls In Greater Kailash - I Women Seeking Men08448380779 Call Girls In Greater Kailash - I Women Seeking Men
08448380779 Call Girls In Greater Kailash - I Women Seeking MenDelhi Call girls
 
GenCyber Cyber Security Day Presentation
GenCyber Cyber Security Day PresentationGenCyber Cyber Security Day Presentation
GenCyber Cyber Security Day PresentationMichael W. Hawkins
 
Breaking the Kubernetes Kill Chain: Host Path Mount
Breaking the Kubernetes Kill Chain: Host Path MountBreaking the Kubernetes Kill Chain: Host Path Mount
Breaking the Kubernetes Kill Chain: Host Path MountPuma Security, LLC
 
Injustice - Developers Among Us (SciFiDevCon 2024)
Injustice - Developers Among Us (SciFiDevCon 2024)Injustice - Developers Among Us (SciFiDevCon 2024)
Injustice - Developers Among Us (SciFiDevCon 2024)Allon Mureinik
 
Partners Life - Insurer Innovation Award 2024
Partners Life - Insurer Innovation Award 2024Partners Life - Insurer Innovation Award 2024
Partners Life - Insurer Innovation Award 2024The Digital Insurer
 

Último (20)

A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)
 
Driving Behavioral Change for Information Management through Data-Driven Gree...
Driving Behavioral Change for Information Management through Data-Driven Gree...Driving Behavioral Change for Information Management through Data-Driven Gree...
Driving Behavioral Change for Information Management through Data-Driven Gree...
 
IAC 2024 - IA Fast Track to Search Focused AI Solutions
IAC 2024 - IA Fast Track to Search Focused AI SolutionsIAC 2024 - IA Fast Track to Search Focused AI Solutions
IAC 2024 - IA Fast Track to Search Focused AI Solutions
 
Tata AIG General Insurance Company - Insurer Innovation Award 2024
Tata AIG General Insurance Company - Insurer Innovation Award 2024Tata AIG General Insurance Company - Insurer Innovation Award 2024
Tata AIG General Insurance Company - Insurer Innovation Award 2024
 
🐬 The future of MySQL is Postgres 🐘
🐬  The future of MySQL is Postgres   🐘🐬  The future of MySQL is Postgres   🐘
🐬 The future of MySQL is Postgres 🐘
 
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
 
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdfThe Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
 
How to convert PDF to text with Nanonets
How to convert PDF to text with NanonetsHow to convert PDF to text with Nanonets
How to convert PDF to text with Nanonets
 
How to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerHow to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected Worker
 
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
 
The Codex of Business Writing Software for Real-World Solutions 2.pptx
The Codex of Business Writing Software for Real-World Solutions 2.pptxThe Codex of Business Writing Software for Real-World Solutions 2.pptx
The Codex of Business Writing Software for Real-World Solutions 2.pptx
 
Axa Assurance Maroc - Insurer Innovation Award 2024
Axa Assurance Maroc - Insurer Innovation Award 2024Axa Assurance Maroc - Insurer Innovation Award 2024
Axa Assurance Maroc - Insurer Innovation Award 2024
 
WhatsApp 9892124323 ✓Call Girls In Kalyan ( Mumbai ) secure service
WhatsApp 9892124323 ✓Call Girls In Kalyan ( Mumbai ) secure serviceWhatsApp 9892124323 ✓Call Girls In Kalyan ( Mumbai ) secure service
WhatsApp 9892124323 ✓Call Girls In Kalyan ( Mumbai ) secure service
 
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
 
Handwritten Text Recognition for manuscripts and early printed texts
Handwritten Text Recognition for manuscripts and early printed textsHandwritten Text Recognition for manuscripts and early printed texts
Handwritten Text Recognition for manuscripts and early printed texts
 
08448380779 Call Girls In Greater Kailash - I Women Seeking Men
08448380779 Call Girls In Greater Kailash - I Women Seeking Men08448380779 Call Girls In Greater Kailash - I Women Seeking Men
08448380779 Call Girls In Greater Kailash - I Women Seeking Men
 
GenCyber Cyber Security Day Presentation
GenCyber Cyber Security Day PresentationGenCyber Cyber Security Day Presentation
GenCyber Cyber Security Day Presentation
 
Breaking the Kubernetes Kill Chain: Host Path Mount
Breaking the Kubernetes Kill Chain: Host Path MountBreaking the Kubernetes Kill Chain: Host Path Mount
Breaking the Kubernetes Kill Chain: Host Path Mount
 
Injustice - Developers Among Us (SciFiDevCon 2024)
Injustice - Developers Among Us (SciFiDevCon 2024)Injustice - Developers Among Us (SciFiDevCon 2024)
Injustice - Developers Among Us (SciFiDevCon 2024)
 
Partners Life - Insurer Innovation Award 2024
Partners Life - Insurer Innovation Award 2024Partners Life - Insurer Innovation Award 2024
Partners Life - Insurer Innovation Award 2024
 

CityTimes

  • 1. Preliminary Proposal, Stockholm 18 January 2012 Marina Santini
  • 2.
  • 3.
  • 4. CityTimes is an aggregator. It offers pieces of local news that can be found on local newspapers + city life information, such as restaurants, shops, products, activities for children (through advertising or information flows coming from social media).
  • 5. http://www.santini.se/geotimes/  Shows recent news in reversed chronological order from different sources.  Positions news on the map and diplays the excerpts in the bubbles. When users browse another page, the news and the map change accordingly and the markers on the map are built dynamically  Examples of Social Media Potential  Information flows from other websites (for ex, through Twitter)  Connection to social networks (ex, Like and Follow)  Local ads  Google AdSense  Basically all kind of local information can be included in this kind of website.
  • 6. WP interface  WP API  Personal code
  • 7.
  • 8. Template tags (ex: the_title(), the_permalink(), etc.)  Handy!
  • 9. the visualization of the map with the markers.  the storage of the full article in the database and its visualization, and other functions .  WARNING: No refactoring has been applied yet, so the code contains duplications. 
  • 10. Automatically download RSS feeds from newspapers at regular intervals (plug-in)  Automatically tag RSS feeds with locations (plug-in)  Based on the location tags, markers are shown on a map. Marker’s info windows show the RSS feed located in that part of the city. When no location are mentioned in the RSS feed text, the default location is the neighborhood of the local newspaper (my code)
  • 11. When clicking on a single post, the user will see the locations contained in the post on the map, and has the possibility of reading the extended version of the article and then the full article in the newspaper website (my code)
  • 12. The string "slussen, stockholm, sweden" returns many different locations  Result Address: Slussen T-bana, Stockholm urban area, Sweden  Result Address: Östra Slussgatan, Stockholm urban area, Sweden  Result Address: Hammarby Slussväg, 118 60 Stockholm, Sweden
  • 13. Lia’s Geotimes’ website has not yet a mobile version.  Wordpress mobile has been released recently
  • 14.
  • 15. GTRO can be saved on the home screen (like a native app) and started directly from the mobile. 1) Find Directions 2) Optimize Routes 3) Store My Locations 4) Find Products on the map  (GTRO2) http://www.santini.se/gtro2/
  • 16.
  • 17.
  • 18.
  • 19. iOS native apps  . iOS apps resemble the built-in applications on iOS- based devices in that they reside on the device itself and take advantage of the features of the iOS environment.  Web Content  Web content is hosted by a website that people visit through their iOS-based device  Web apps  Optimized web pages  Compatible web pages
  • 20. I am inclined to think that the "web-based app" way is the best way to go. But, of course, it depends...  This choice might be a little limited at present, but emulation frameworks (e.g. iWebKit5) or special libraries (e.g JQuery Mobile) are growing fast. Handy with “Add to Home Screen” to access the web app directly, just like a native app.  In general, it is possible and fast to create a good web app without going native. Additionally, it is much easier to capitalize on a web app (in terms of reuse on different platforms) rather than on a native app, I think.
  • 21. In this project, I overlooked that ”APPS RESPOND TO GESTURES, NOT CLICKS”. I overlooked the gestural context and I focussed on the features.  Designing an app is different from designing an website because an app require s a different behaviour, a different posture, a different manuality. An app must be thought as an app from the beginning, and not adapted along the way.  It is not only a matter of ”STYLING” (colors, round corners, all the JQTouch stuff).  It is a matter of human-mobile interaction!
  • 22.
  • 23. Move from GoogleMaps V2 to GoogleMaps V3  Speed up the map visualization  Create an automatized colored classification function, so user can visually identify the type of news they are interested in (ex, sport, politics, crime, fashion, etc.)  Create a computational advertising function, where the ads relevant to news locations (ex, local shops, local cinema, etc.) are automatically displayed, and not manually put up.  Create a automatic comment analysis function to identify users’ problems, orientations, sentiments, opinions and attitudes.  Study more the target user group in order to see if CityTimes’ information architecture matches the audience’ needs (usuability is ok, I think, because WordPress is very handy for many types of users).  Create a CityTimes’ mobile version  Create two versions of the Route Optimizer application (website and app) and integrate them with? CityTimes.
  • 24. The Service: You can create your own service, managed by Grannar (for ex), which would offer additional local information to what you already offer now. For example, suggestions for local restaurants and their reputation (through users’ reviews), where to find a open pharmacy in the vicinity on Sunday night, etc.
  • 25. The Blueprint: you can sell model or create a kind of franchising model ”powered by CityTimes”, that can be used as a standlone website or be appended to the main website. For example, for tourism and leisure time: ”Visit Stockholm” (http://www.visitstockholm.com/en/) ”TimeOut ” (http://www.timeout.com/stockholm/)