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Data Warehousing and Mining Lab 2

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Data Warehousing and Mining Lab 1 Part B

Details

Roll Number : K057 Name : Tejas Kamal Sahoo Branch : Btech Cyber Security Year : 2nd Semester : 2 Date & Time : 11-01-2025 15:00

Questions

Question 1

Identify a case study / application using datawarehouse Identify the following aspects:-

  • Domain
  • Features/Functionalities

Answer 1

The current Selected Example is YouTube

Domain

  • Streaming/Entertainment (YouTube, a video streaming platform).

Features

  1. Data Collection : YouTube Collects good amount of data from users regarding watch History, search History, Likes, Comments, Shares and other Demographic Data
  2. User Profiling: YouTube profiles each user with their likes and dislikes viewing patterns, and engagement history.
  3. Content Recommendation Engine: Using algorithms, YouTube analyzes data in the warehouse to generate recommendations based on user preferences. These algorithms might consider factors like:
    • Past views
    • Liked/disliked videos
    • Subscriptions to channels
    • Time spent watching certain types of videos
    • Social connections (e.g., what users’ friends are watching)
  4. Real-Time Data Processing:
    Although YouTube’s data warehouse holds large, historical data, it also supports real-time analytics for up-to-the-minute content recommendations based on users’ latest interactions.
  5. Trending & Popular Content:
    YouTube’s data warehouse enables the identification of trending videos by aggregating data on a global scale. This helps recommend popular videos to a wider audience based on real-time viewership trends.
  6. Ad Personalization: YouTube also uses data warehousing for ad recommendations. Based on the user’s watch history and preferences, ads are tailored to increase engagement and ad revenue.
  7. Transcription: YouTube Transcribes and moderates the data with the help of the Transcripted Text.

Question 2

Analyse the following

  1. Software Implementing Data Warehouse & Mining
  2. Name of company
  3. Software functionalities

1. Software Implementing Data Warehouse & Mining

Software Name: YouTube (Google)

  • Company Name:
    • Google LLC (Parent company of YouTube)

2. Software Functionalities:

YouTube Data Warehouse
YouTube, as a part of Google, utilizes extensive data warehouse solutions, particularly within the Google Cloud ecosystem, to manage and analyze the massive amounts of data generated by users. Its key functionalities include:

  • Data Storage & Management:
    YouTube collects and stores data in highly scalable data warehouses that include user activity, video metadata, watch history, comments, likes, dislikes, and much more. This data is stored in systems like Google BigQuery (a managed data warehouse).

    • A simple table consisting Likes, Video Metadata, History, Comments
    • A simple analysis when recommending content for warehousing.
    • Pre calculate audience batches

  • Data Integration:
    YouTube integrates data from various sources, such as user interactions, advertisements, third-party apps, and devices to create a consolidated view of activity on its platform.

    • Advertisement integration using google Ad Sense.
    • Payment to content creators using data analytics.

  • Data Streaming & Real-Time Analytics:
    YouTube handles large volumes of real-time data through streaming analytics, allowing for immediate feedback on user activities and interactions. This data is processed to ensure personalized content recommendations and efficient ad targeting.

    • Insights on live streams.
    • Insights on video performance.

  • Data Security and Privacy:
    YouTube uses advanced encryption and privacy mechanisms to secure the data collected and ensure that user data is protected, while complying with global privacy laws.

  • User Interaction Data:
    YouTube stores detailed data about user interactions with the platform, such as watch time, clicks, subscriptions, and engagement metrics (likes, comments, shares), all contributing to creating a detailed user profile.

YouTube Data Mining
YouTube uses advanced data mining techniques to extract useful patterns and insights from the vast amount of data stored in its warehouse. The key functionalities of YouTube’s data mining include:

  • Content Recommendation Engine:
    YouTube uses machine learning algorithms and data mining techniques to generate personalized recommendations for each user. These algorithms analyze past behavior, engagement patterns, and other factors to predict what content the user is likely to enjoy.

  • User Segmentation:
    By analyzing user behavior, YouTube segments its audience into different categories based on interests, demographics, location, etc., to provide more targeted content and advertisements.

  • Trend Detection:
    Data mining tools are employed to detect emerging trends across the platform. This helps YouTube identify viral content, trending topics, and new content preferences, allowing for dynamic adjustments in content recommendations.

  • Video Ranking and Search Optimization:
    YouTube’s data mining algorithms assess various factors like user engagement, watch time, and video quality to rank videos in search results. Videos that garner higher interaction are prioritized.

  • Ad Personalization:
    YouTube applies data mining to tailor ads based on users’ interests, previous interactions with the platform, and demographic data, ensuring ads are relevant and engaging for each user.

  • Behavior Prediction:
    Data mining techniques are used to predict users’ future behaviors, such as what videos they are likely to watch next, which ads they might engage with, or which new channels they might subscribe to.

  • Anomaly Detection:
    YouTube uses anomaly detection algorithms to spot unusual patterns in user activity, such as bot activity or suspicious behavior, ensuring the platform maintains authenticity and engagement quality.

References

Geeks For Geeks

Information
  • date: 2025.01.11
  • time: 14:45

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