{
  "format": "dataset-manifest/1",
  "slug": "iot-browser-interaction-ledger",
  "version": 2,
  "seed": "iot-browser-interaction-ledger:1",
  "spec": {
    "format": "dataset/1",
    "slug": "iot-browser-interaction-ledger",
    "name": "IoT Browser Interaction Ledger",
    "tagline": "User interactions with IoT devices via browser tabs, including visits, transactions, and downloads.",
    "description": "Users engage with their IoT devices through a modern browser, enabling them to control and monitor devices effortlessly from a unified interface. This intricate web of interactions is reflected in the dataset, which captures user accounts, browser tabs, and their historical visits to various IoT platforms. Each user may have multiple browser tabs open simultaneously, which signifies a high frequency of engagement with specific devices across different sessions. Meanwhile, transactions and downloads occur less frequently, highlighting selective interactions where users make conscious decisions to engage with specific IoT services or content.\n\nThe dataset features several interconnected tables that illustrate the relationships between these interactions. The 'users' table outlines individual user profiles with unique identifiers, while the 'browser_tabs' table tracks each tab opened by a user, containing information about the active IoT platforms being accessed. The 'visits' table stores timestamps and details about when and how often users interact with their tabs, providing a timeline of engagement patterns. The 'transactions' table captures financial exchanges linked to users' interactions, detailing credit card usage and transaction records. Lastly, the 'downloads' table documents the files users download from various IoT platforms, giving insight into the content that draws user interest.\n\nQuestions this data can help answer include: What are the most popular IoT platforms among users? How frequently do users interact with their devices, and which devices are most commonly engaged with? What patterns emerge around transactions and downloads, and how do they correlate with user activity? This dataset is excellent for practicing data analysis skills and exploring user behavior in the context of IoT device interaction. It's important to note that all data in this dataset is synthetic, designed for practice and exploration.",
    "domain": "iot",
    "shape": "ledger",
    "useCases": [
      "sql-practice",
      "teaching"
    ],
    "keywords": [
      "IoT",
      "browser",
      "user interaction",
      "datasets",
      "analytics",
      "Microsoft Edge"
    ],
    "locale": "en",
    "license": "CC0-1.0",
    "version": 1,
    "tables": {
      "users": {
        "description": "This table records user account details, linking to their browser interactions and transactions.",
        "rows": 150,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each user."
          },
          "full_name": {
            "kind": "fullName",
            "description": "User's full name.",
            "nullable": true
          },
          "email": {
            "kind": "email",
            "from": "full_name",
            "description": "User's email address.",
            "nullable": true
          },
          "created_at": {
            "kind": "datetime",
            "from": "2022-01-01",
            "to": "2023-10-01",
            "description": "Account creation date.",
            "dayProfile": "waking"
          }
        }
      },
      "browser_tabs": {
        "description": "This table captures details about the browser tabs opened by users, linked to their user accounts.",
        "rows": 300,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each tab."
          },
          "user_id": {
            "kind": "fk",
            "ref": "users.id",
            "description": "References the user's account."
          },
          "url": {
            "kind": "url",
            "description": "The URL of the IoT device or platform accessed.",
            "nullable": true
          },
          "opened_at": {
            "kind": "datetime",
            "from": "2023-01-01",
            "to": "2023-10-01",
            "description": "Timestamp when the tab was opened.",
            "dayProfile": "waking"
          },
          "is_active": {
            "kind": "bool",
            "trueRate": 0.7,
            "description": "Indicates if the tab is currently active.",
            "nullable": true
          }
        }
      },
      "visits": {
        "description": "This table logs the visits made by users to different IoT platforms, linked to the browser tabs.",
        "rows": 450,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each visit."
          },
          "tab_id": {
            "kind": "fk",
            "ref": "browser_tabs.id",
            "description": "References the tab associated with this visit."
          },
          "visit_time": {
            "kind": "datetime",
            "from": "2023-01-01",
            "to": "2023-10-01",
            "description": "Timestamp of the visit.",
            "dayProfile": "waking",
            "trend": "growing"
          },
          "duration_seconds": {
            "kind": "int",
            "min": 1,
            "max": 3600,
            "description": "Duration of the visit in seconds.",
            "nullable": true,
            "distribution": "normal"
          }
        }
      },
      "transactions": {
        "description": "This table contains records of credit card transactions made by users during their interactions.",
        "rows": 100,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each transaction."
          },
          "user_id": {
            "kind": "fk",
            "ref": "users.id",
            "description": "References the user who made the transaction."
          },
          "amount": {
            "kind": "decimal",
            "min": 1,
            "max": 500,
            "scale": 2,
            "description": "Transaction amount in USD.",
            "nullable": true,
            "distribution": "long-tail"
          },
          "transaction_time": {
            "kind": "datetime",
            "from": "2023-01-01",
            "to": "2023-10-01",
            "description": "Timestamp of the transaction.",
            "dayProfile": "waking",
            "trend": "growing"
          }
        }
      },
      "downloads": {
        "description": "This table logs file downloads initiated by users while interacting with IoT platforms.",
        "rows": 75,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each download."
          },
          "tab_id": {
            "kind": "fk",
            "ref": "browser_tabs.id",
            "description": "References the tab where the download was initiated."
          },
          "file_name": {
            "kind": "title",
            "description": "Name of the downloaded file.",
            "nullable": true,
            "pool": [
              "snapshot",
              "report",
              "data",
              "log",
              "analysis",
              "summary",
              "record",
              "export",
              "document",
              "archive",
              "metrics",
              "results",
              "info",
              "output",
              "file",
              "dataset",
              "capture",
              "event",
              "history",
              "view",
              "snapshot-2023",
              "snapshot-2022",
              "snapshot-2021",
              "snapshot-2020",
              "snapshot-2019",
              "snapshot-2018",
              "snapshot-2017",
              "snapshot-2016",
              "snapshot-2015",
              "snapshot-2014"
            ],
            "min": 1,
            "max": 3
          },
          "download_time": {
            "kind": "datetime",
            "from": "2023-01-01",
            "to": "2023-10-01",
            "description": "Timestamp of when the download occurred."
          }
        }
      }
    },
    "structure": "multi-table",
    "traits": [
      "timestamped",
      "high-cardinality",
      "nullable"
    ],
    "analysisQuestions": [
      "What devices do users interact with the most through their browser?",
      "Which users have the highest number of visits to IoT platforms?",
      "What time of day do users typically engage with their IoT devices?",
      "How often do users make transactions related to their IoT interactions?",
      "Which files are downloaded most frequently by users?"
    ],
    "suggestedQueries": [
      {
        "title": "Join Users and Visits",
        "sql": "SELECT users.full_name, visits.visit_time FROM users JOIN visits ON users.id = visits.user_id;",
        "dialect": "postgres"
      },
      {
        "title": "Count Visits by User",
        "sql": "SELECT user_id, COUNT(*) FROM visits GROUP BY user_id;",
        "dialect": "postgres"
      },
      {
        "title": "Average Duration of Visits",
        "sql": "SELECT AVG(duration_seconds) FROM visits;",
        "dialect": "postgres"
      },
      {
        "title": "Transactions and Total Amount",
        "sql": "SELECT user_id, SUM(amount) FROM transactions GROUP BY user_id;",
        "dialect": "postgres"
      },
      {
        "title": "Downloads by User",
        "sql": "SELECT users.full_name, COUNT(downloads.id) FROM downloads JOIN browser_tabs ON downloads.tab_id = browser_tabs.id JOIN users ON browser_tabs.user_id = users.id GROUP BY users.full_name;",
        "dialect": "postgres"
      }
    ],
    "usageScenarios": [
      {
        "title": "Data Analysis for IoT Engagement",
        "body": "Data analysts can utilize this dataset to gain insights into user engagement with IoT devices through the Microsoft Edge browser. By analyzing visit patterns and transaction behaviors, they can identify trends and areas for improvement in user experience."
      },
      {
        "title": "Teaching SQL and Data Relationships",
        "body": "Educators can use this dataset to teach students about SQL querying and the importance of data relationships. The multi-table structure provides an excellent opportunity for students to practice JOIN operations and aggregate functions."
      },
      {
        "title": "User Behavior Research",
        "body": "Researchers studying user behavior in technology can leverage this dataset to analyze how users interact with IoT platforms via their browser. By examining visit frequencies and transaction rates, they can draw conclusions about user preferences and habits."
      },
      {
        "title": "Testing Analytical Tools",
        "body": "Developers of analytical tools can use this dataset to test and validate their software. By simulating real-world user interactions with IoT devices, they can ensure their tools handle various scenarios effectively and accurately."
      }
    ]
  },
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    "users": 150,
    "browser_tabs": 300,
    "downloads": 75,
    "transactions": 100,
    "visits": 450
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          "min": "Alice Evans",
          "max": "Zoe Nakamura"
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