{
  "format": "dataset-manifest/1",
  "slug": "network-incidents",
  "version": 1,
  "seed": "network-incidents:1",
  "spec": {
    "format": "dataset/1",
    "slug": "network-incidents",
    "name": "Network Incidents",
    "tagline": "Network incident records, including timestamps, affected cells, subscribers, and related tickets.",
    "description": "Network incidents disrupt service flow, impacting the experience of subscribers connected to various cells across a telecom network. Each incident is an event that affects one or more cells, with specific start and end times that define the duration of the disruption. Subscribers in the affected areas experience a service interruption, leading to the creation of tickets that document customer complaints and issues related to these incidents. The seamless management of these incidents is crucial for maintaining network reliability and subscriber satisfaction.\n\nThe dataset comprises three interconnected tables: cells, incidents, and tickets. Each row in the cells table represents a unique geographic area within the network, while the incidents table records specific disruptions, linking to the respective cells that were affected. The tickets table tracks customer complaints associated with those incidents, allowing for a detailed view of the service issues arising from each network incident. By joining the incidents table with cells, developers can identify which areas were impacted, and by linking tickets, they can delve into customer experiences and resolutions.\n\nDevelopers can utilize this dataset to answer important questions such as how frequently incidents occur in specific areas, the average duration of service disruptions, and the volume of customer complaints generated from each incident. This provides a practical avenue for exploring incident management processes and customer service efficiency in the telecom domain. Practicing with this synthetic data enables users to enhance their skills in data manipulation, analysis, and visualization while simulating real-world telecommunications scenarios.",
    "domain": "telecom",
    "shape": "event-log",
    "useCases": [
      "etl-pipelines",
      "analytics-demo"
    ],
    "keywords": [
      "network incidents",
      "telecom",
      "cellular",
      "incident management",
      "data analysis"
    ],
    "locale": "en",
    "license": "CC0-1.0",
    "version": 1,
    "tables": {
      "cells": {
        "description": "This table contains details about the network cells, including their unique identifiers and locations. Each row represents a distinct cell in the network.",
        "rows": 100,
        "columns": {
          "cell_id": {
            "kind": "pk",
            "description": "Unique identifier for the cell."
          },
          "location": {
            "kind": "city",
            "description": "The geographical location of the cell.",
            "nullable": true
          },
          "status": {
            "kind": "enum",
            "values": [
              "active",
              "inactive"
            ],
            "description": "Current operational status of the cell."
          }
        }
      },
      "incidents": {
        "description": "This table records incidents affecting the network cells, detailing their start and end times, the cells involved, and the number of affected subscribers.",
        "rows": {
          "per": "cells",
          "via": "cell_id",
          "min": 1,
          "max": 5,
          "skew": "long-tail"
        },
        "columns": {
          "incident_id": {
            "kind": "pk",
            "description": "Unique identifier for the incident."
          },
          "cell_id": {
            "kind": "fk",
            "ref": "cells.cell_id",
            "description": "Reference to the affected cell."
          },
          "start_time": {
            "kind": "datetime",
            "from": "2023-01-01",
            "to": "2024-12-31",
            "description": "Start time of the incident.",
            "dayProfile": "waking"
          },
          "end_time": {
            "kind": "datetime",
            "after": "start_time",
            "withinSeconds": 3600,
            "description": "End time of the incident, occurring within one hour of the start."
          },
          "affected_subscribers": {
            "kind": "int",
            "min": 1,
            "max": 1000,
            "description": "Number of subscribers affected by the incident.",
            "nullable": true
          }
        }
      },
      "tickets": {
        "description": "This table captures the tickets raised in response to incidents, including their resolution status and timestamps.",
        "rows": {
          "per": "incidents",
          "via": "incident_id",
          "min": 1,
          "max": 3,
          "skew": "long-tail"
        },
        "columns": {
          "ticket_id": {
            "kind": "pk",
            "description": "Unique identifier for the ticket."
          },
          "incident_id": {
            "kind": "fk",
            "ref": "incidents.incident_id",
            "description": "Reference to the incident for which the ticket was raised."
          },
          "created_at": {
            "kind": "datetime",
            "from": "2023-01-01",
            "to": "2024-12-31",
            "description": "Timestamp when the ticket was created.",
            "dayProfile": "business"
          },
          "resolved_at": {
            "kind": "datetime",
            "after": "created_at",
            "withinDays": 7,
            "description": "Timestamp when the ticket was resolved, occurring within 7 days of creation.",
            "nullable": true,
            "when": {
              "column": "status",
              "in": [
                "closed",
                "in-progress"
              ]
            }
          },
          "status": {
            "kind": "enum",
            "values": [
              "open",
              "closed",
              "in-progress"
            ],
            "description": "Current status of the ticket."
          }
        }
      }
    },
    "structure": "multi-table",
    "traits": [
      "timestamped",
      "categorical",
      "nullable"
    ],
    "analysisQuestions": [
      "What is the average duration of incidents across different cells?",
      "Which cells experience the most frequent incidents?",
      "How quickly are tickets resolved after they are created?",
      "What patterns can be observed in subscriber impact during incidents?",
      "Which days of the week have the highest number of incidents?"
    ],
    "suggestedQueries": [
      {
        "title": "Join incidents with cells",
        "sql": "SELECT incidents.*, cells.location FROM incidents JOIN cells ON incidents.cell_id = cells.cell_id;",
        "dialect": "postgres"
      },
      {
        "title": "Count incidents per cell",
        "sql": "SELECT cell_id, COUNT(*) as incident_count FROM incidents GROUP BY cell_id;",
        "dialect": "postgres"
      },
      {
        "title": "Average affected subscribers by status",
        "sql": "SELECT tickets.status, AVG(incidents.affected_subscribers) FROM tickets JOIN incidents ON tickets.incident_id = incidents.incident_id GROUP BY tickets.status;",
        "dialect": "postgres"
      },
      {
        "title": "Tickets resolution time analysis",
        "sql": "SELECT AVG(EXTRACT(EPOCH FROM (resolved_at - created_at))) / 3600 as avg_resolution_time FROM tickets;",
        "dialect": "postgres"
      }
    ],
    "usageScenarios": [
      {
        "title": "Network Performance Analysis",
        "body": "Telecom analysts can utilize this dataset to assess the performance of network cells by examining incidents over time. By identifying which cells frequently experience incidents, they can prioritize upgrades or maintenance to improve overall service quality."
      },
      {
        "title": "Customer Impact Evaluation",
        "body": "Customer service teams can leverage the dataset to analyze the impact of network incidents on subscribers. Understanding the number of affected subscribers helps in strategizing communication efforts and improving customer satisfaction during outages."
      },
      {
        "title": "Incident Response Optimization",
        "body": "Operations managers can use this dataset to evaluate the efficiency of incident response processes. By analyzing ticket resolution times and correlating them with incident durations, they can identify areas for improvement in their ticketing systems."
      }
    ]
  },
  "counts": {
    "cells": 100,
    "incidents": 230,
    "tickets": 360
  },
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