Table Login Logs - warwickfoster/qurantools GitHub Wiki

The login-logs table records user login activity, including timestamps, IP addresses, and user information. This data is vital for security audits and tracking user access patterns.

Analysis of the login-logs Table

Below is the detailed analysis and description of each field in the login-logs table, with the table name included as a left-hand column.

Table Name Field Name Description
login-logs Record ID A unique identifier for each login record, serving as the primary key for indexing and referencing.
login-logs User ID The unique identifier of the user who logged in, enabling tracking and analysis of user-specific login activity.
login-logs Institution ID Identifies the institution associated with the user, if applicable. A NULL value indicates no institutional affiliation is recorded for the user.
login-logs Email Address The email address of the user who logged in, providing an additional reference for identifying the user.
login-logs Login Date The date when the login occurred, allowing for temporal analysis of login activity.
login-logs Login Time The time when the login occurred, complementing Login Date for precise tracking of login events.
login-logs Login IP The IP address from which the user logged in, providing contextual information for monitoring and security purposes.
login-logs DATE AND TIME A combined timestamp of the login event, consolidating Login Date and Login Time for convenience in chronological analysis.

Key Insights

  1. Field Relationships:

    • Record ID uniquely identifies each login event, ensuring traceability and data integrity.
    • User ID and Email Address link the login to a specific user, allowing for user-specific analysis.

  2. Login Context:

    • Login IP provides insights into the location or network from which the login occurred, which is useful for security monitoring.
    • DATE AND TIME simplifies chronological sorting and analysis of login events.

  3. Applications:

    • Tracks user login activity to monitor engagement and detect potential security issues.
    • Identifies peak login times or trends for optimizing application availability and performance.

Example Interpretation of Data:

  • Row 1:
    • Record ID: 972
    • User ID: 1525
    • Institution ID: NULL
    • Email Address: <EMAIL TEXT>
    • Login Date: 2025-01-01
    • Login Time: 07:41:33
    • Login IP: 127.0.0.1
    • DATE AND TIME: 2025-01-01 18:41:33
    • Indicates that user 1525 logged in on January 1, 2025, at 7:41 AM from IP address 127.0.0.1.

Contextual Significance:

  1. User Engagement Analysis:
    • Tracks user activity by analyzing login frequency and patterns.
  2. Security Monitoring:
    • IP tracking allows detection of suspicious activity, such as logins from unusual locations or multiple failed attempts.
  3. Temporal Trends:
    • Combining Login Date and Login Time enables identification of peak login times for resource allocation or system optimization.

First 10 Rows Example

Record ID User ID Institution ID Email Address Login Date Login Time Login IP DATE AND TIME
972 1525 nan 2025-01-01 07:41:33 127.0.0.1 2025-01-01 18:41:33
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