{
  "format": "dataset-manifest/1",
  "slug": "augmented-reality-ar-photo-filters-app",
  "version": 1,
  "seed": "augmented-reality-ar-photo-filters-app:1",
  "spec": {
    "format": "dataset/1",
    "slug": "augmented-reality-ar-photo-filters-app",
    "name": "Augmented Reality Filter Interactions",
    "tagline": "User interactions with augmented reality filters, favorites, music tracks, and promotional event spikes.",
    "description": "Users engage with a platform that enhances their photos through a diverse array of augmented reality filters and effects. This business thrives on creativity and aims to elevate user interaction, allowing individuals to experiment with their images by applying various filters, selecting music tracks, and participating in promotional events. The synergy between these elements drives user engagement, leading to a dynamic environment where certain filters spike in popularity during special events, while others maintain consistent appeal over time. \n\nThe dataset features several interconnected tables. The 'users' table catalogues each individual who interacts with the platform, while 'filters' contains the different augmented reality effects available for use. The 'user_filter_interactions' table captures each instance of a user applying a specific filter, detailing how often and in what context these interactions occur. The 'user_favorite_filters' table highlights the filters that users prefer, helping to identify trends in user preferences. Additionally, the 'music_tracks' table records the soundtracks that users pair with their images, enriching the overall experience. Lastly, 'promotional_events' chronicles the special events that encourage increased filter usage, showcasing which filters become popular during these times. \n\nThis dataset is useful for answering questions such as: Which filters are favored by different user demographics? How do promotional events affect user engagement with various filters? What trends can be observed in filter usage over time? By exploring these aspects, developers can gain insights into user behavior and preferences in augmented reality interactions. The data is synthetic, providing a rich opportunity for analysis without concerns over privacy issues.",
    "domain": "saas",
    "shape": "graph-edges",
    "useCases": [
      "sql-practice",
      "teaching"
    ],
    "keywords": [
      "augmented reality",
      "photo filters",
      "user interactions",
      "music tracks",
      "promotional events",
      "data analysis"
    ],
    "locale": "en",
    "license": "CC0-1.0",
    "version": 1,
    "tables": {
      "users": {
        "description": "This table contains user profiles, detailing their unique identifiers and basic information. Each user can interact with multiple filters and have favorite selections.",
        "rows": 150,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each user."
          },
          "username": {
            "kind": "words",
            "min": 3,
            "max": 15,
            "description": "The user's chosen display name.",
            "nullable": true
          },
          "email": {
            "kind": "email",
            "from": "username",
            "description": "The user's email address for account-related communications.",
            "nullable": true
          },
          "created_at": {
            "kind": "datetime",
            "from": "2020-01-01",
            "to": "2023-12-31",
            "description": "The date the user registered on the platform.",
            "dayProfile": "waking"
          }
        }
      },
      "filters": {
        "description": "This table lists all available AR filters, including their unique identifiers and details about each filter. Each filter can be used by multiple users.",
        "rows": 100,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each filter."
          },
          "name": {
            "kind": "words",
            "min": 3,
            "max": 20,
            "description": "The name of the AR filter.",
            "nullable": true
          },
          "category": {
            "kind": "enum",
            "values": [
              "fun",
              "artistic",
              "seasonal",
              "promotional"
            ],
            "description": "The category to which the filter belongs."
          },
          "created_at": {
            "kind": "datetime",
            "from": "2020-01-01",
            "to": "2023-12-31",
            "description": "The date the filter was added to the platform.",
            "dayProfile": "waking"
          }
        }
      },
      "user_filter_interactions": {
        "description": "This table records each interaction a user has with a filter, linking users to their chosen filters and tracking the engagement metrics.",
        "rows": {
          "per": "users",
          "via": "user_id",
          "min": 1,
          "max": 10,
          "skew": "long-tail"
        },
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each interaction record."
          },
          "user_id": {
            "kind": "fk",
            "ref": "users.id",
            "description": "The ID of the user interacting with the filter."
          },
          "filter_id": {
            "kind": "fk",
            "ref": "filters.id",
            "description": "The ID of the filter being used."
          },
          "interaction_date": {
            "kind": "datetime",
            "from": "2020-01-01",
            "to": "2023-12-31",
            "description": "The date and time when the interaction occurred.",
            "dayProfile": "waking",
            "trend": "growing"
          },
          "likes": {
            "kind": "int",
            "min": 0,
            "max": 1000,
            "description": "The number of likes received for this interaction.",
            "nullable": true
          }
        }
      },
      "user_favorite_filters": {
        "description": "This table captures users' favorite filters, providing a link between users and their preferred filters for quick access.",
        "rows": {
          "per": "users",
          "via": "user_id",
          "min": 1,
          "max": 5,
          "skew": "long-tail"
        },
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each favorite record."
          },
          "user_id": {
            "kind": "fk",
            "ref": "users.id",
            "description": "The ID of the user who favorited the filter."
          },
          "filter_id": {
            "kind": "fk",
            "ref": "filters.id",
            "description": "The ID of the favorite filter."
          }
        }
      },
      "music_tracks": {
        "description": "This table lists music tracks available for users to pair with their AR filter photos, enhancing the overall experience.",
        "rows": 50,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each music track."
          },
          "title": {
            "kind": "words",
            "min": 3,
            "max": 30,
            "description": "The title of the music track."
          },
          "artist": {
            "kind": "words",
            "min": 3,
            "max": 30,
            "description": "The artist or band who performed the track.",
            "nullable": true
          },
          "duration": {
            "kind": "int",
            "min": 30,
            "max": 300,
            "description": "Duration of the track in seconds."
          }
        }
      },
      "promotional_events": {
        "description": "This table details promotional events where specific filters are highlighted, showing the impact on user engagement during these periods.",
        "rows": 20,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each promotional event."
          },
          "filter_id": {
            "kind": "fk",
            "ref": "filters.id",
            "description": "The ID of the filter featured in the promotional event."
          },
          "start_date": {
            "kind": "date",
            "from": "2020-01-01",
            "to": "2023-12-31",
            "description": "The start date of the promotional event.",
            "trend": "seasonal"
          },
          "end_date": {
            "kind": "date",
            "from": "2020-01-01",
            "to": "2023-12-31",
            "description": "The end date of the promotional event.",
            "trend": "seasonal"
          }
        }
      }
    },
    "structure": "multi-table",
    "traits": [
      "categorical",
      "timestamped",
      "nullable",
      "self-referencing"
    ],
    "analysisQuestions": [
      "What filters are most frequently used during promotional events?",
      "How many unique filters does each user interact with over time?",
      "Which music tracks are commonly paired with specific filters?",
      "What trends can be observed in user engagement throughout the year?",
      "Do certain categories of filters become more popular during specific times?"
    ],
    "suggestedQueries": [
      {
        "title": "Join Users with Filter Interactions",
        "sql": "SELECT u.username, f.name, fi.interaction_date FROM users u JOIN user_filter_interactions fi ON u.id = fi.user_id JOIN filters f ON fi.filter_id = f.id;",
        "dialect": "postgres"
      },
      {
        "title": "Count Filters Used by Each User",
        "sql": "SELECT user_id, COUNT(filter_id) AS filters_used FROM user_filter_interactions GROUP BY user_id;",
        "dialect": "postgres"
      },
      {
        "title": "Find Most Popular Filters",
        "sql": "SELECT filter_id, COUNT(*) AS usage_count FROM user_filter_interactions GROUP BY filter_id ORDER BY usage_count DESC;",
        "dialect": "postgres"
      },
      {
        "title": "Average Likes per Interaction by Filter",
        "sql": "SELECT filter_id, AVG(likes) AS avg_likes FROM user_filter_interactions GROUP BY filter_id;",
        "dialect": "postgres"
      },
      {
        "title": "Engagement Trends During Promotional Events",
        "sql": "SELECT pe.start_date, COUNT(fi.id) AS interactions FROM promotional_events pe JOIN user_filter_interactions fi ON fi.filter_id = pe.filter_id WHERE fi.interaction_date BETWEEN pe.start_date AND pe.end_date GROUP BY pe.start_date;",
        "dialect": "postgres"
      }
    ],
    "usageScenarios": [
      {
        "title": "Data Analysis for Marketing Teams",
        "body": "Marketing analysts can utilize this dataset to identify trends in user engagement with filters during promotional events. By analyzing which filters saw increased usage during specific campaigns, they can tailor future marketing strategies to enhance user interaction."
      },
      {
        "title": "Teaching SQL Concepts",
        "body": "Educators can use this dataset to teach SQL concepts in a real-world context. Students can practice writing queries that join multiple tables, aggregate data, and analyze user behavior, enhancing their understanding of database interactions."
      },
      {
        "title": "User Experience Research",
        "body": "User experience researchers can explore the dataset to understand which filters are favored by different user demographics. By analyzing user interactions and favorite selections, they can derive insights into user preferences and enhance the platform's offerings."
      },
      {
        "title": "Filter Development Insights",
        "body": "Developers can use this dataset to analyze the performance of various filters, determining which features are most engaging. Insights drawn from user interactions can guide the creation of new filters and enhancements to existing ones."
      }
    ]
  },
  "counts": {
    "filters": 100,
    "music_tracks": 50,
    "promotional_events": 20,
    "users": 150,
    "user_favorite_filters": 350,
    "user_filter_interactions": 599
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