{
  "format": "dataset-manifest/1",
  "slug": "business-process-simulation-software",
  "version": 2,
  "seed": "business-process-simulation-software:1",
  "spec": {
    "format": "dataset/1",
    "slug": "business-process-simulation-software",
    "name": "Telecom Simulation Insights",
    "tagline": "Simulation runs, user interactions, service queues, and performance metrics in the telecom industry.",
    "description": "Telecom businesses strive to optimize their operations through the simulation of various processes, tracking user groups, managing service queues, and analyzing the flow of cases and connections. This dataset provides insights into user activity over time, revealing patterns in simulation runs and performance metrics. Data clusters around typical user interactions during peak hours, while rare edge cases in service usage highlight unusual demand scenarios. It is a synthetic dataset designed for analytics and business intelligence applications that helps telecom companies improve efficiency and customer satisfaction. The five interconnected tables provide a comprehensive view of simulation runs, user interactions, service queues, and performance metrics, enabling organizations to identify trends and make data-driven decisions.",
    "domain": "telecom",
    "shape": "time-series",
    "useCases": [
      "analytics-demo",
      "bi-dashboards"
    ],
    "keywords": [
      "telecom",
      "simulation",
      "user interactions",
      "service queues",
      "performance metrics",
      "analytics"
    ],
    "locale": "en",
    "license": "CC0-1.0",
    "version": 1,
    "tables": {
      "simulation_runs": {
        "description": "Each row represents a unique simulation run, detailing its parameters and timing. It links to user interactions through simulation_run_id.",
        "rows": 100,
        "columns": {
          "simulation_run_id": {
            "kind": "pk",
            "description": "Unique identifier for each simulation run."
          },
          "start_time": {
            "kind": "datetime",
            "from": "2023-01-01",
            "to": "2023-12-31",
            "description": "Start time of the simulation run.",
            "dayProfile": "waking"
          },
          "end_time": {
            "kind": "datetime",
            "after": "start_time",
            "withinSeconds": 3600,
            "description": "End time of the simulation run, occurring within one hour after the start."
          },
          "parameters": {
            "kind": "json",
            "template": {
              "user_count": {
                "kind": "int",
                "min": 1,
                "max": 1000,
                "description": "Number of users simulated."
              },
              "service_type": {
                "kind": "enum",
                "values": [
                  "voice",
                  "data",
                  "sms"
                ],
                "description": "Type of service being simulated."
              }
            },
            "description": "Parameters used in the simulation run."
          }
        }
      },
      "user_interactions": {
        "description": "This table logs each user interaction during simulation runs, linking back to simulation runs through simulation_run_id.",
        "rows": 500,
        "columns": {
          "user_interaction_id": {
            "kind": "pk",
            "description": "Unique identifier for each user interaction."
          },
          "simulation_run_id": {
            "kind": "fk",
            "ref": "simulation_runs.simulation_run_id",
            "description": "Reference to the simulation run."
          },
          "timestamp": {
            "kind": "datetime",
            "from": "2023-01-01",
            "to": "2023-12-31",
            "description": "Timestamp of the user interaction.",
            "dayProfile": "waking",
            "trend": "growing"
          },
          "user_id": {
            "kind": "int",
            "min": 1,
            "max": 1000,
            "description": "Identifier for the user involved in the interaction."
          },
          "interaction_type": {
            "kind": "enum",
            "values": [
              "login",
              "service_request",
              "logout"
            ],
            "description": "Type of interaction performed by the user."
          }
        }
      },
      "service_queues": {
        "description": "This table captures the state of service queues during simulations, linking to user interactions through user_interaction_id.",
        "rows": 300,
        "columns": {
          "service_queue_id": {
            "kind": "pk",
            "description": "Unique identifier for each service queue entry."
          },
          "user_interaction_id": {
            "kind": "fk",
            "ref": "user_interactions.user_interaction_id",
            "description": "Reference to the user interaction."
          },
          "queue_length": {
            "kind": "int",
            "min": 0,
            "max": 100,
            "description": "Current length of the service queue.",
            "distribution": "long-tail"
          },
          "service_type": {
            "kind": "enum",
            "values": [
              "voice",
              "data",
              "sms"
            ],
            "description": "Type of service for which the queue is being monitored."
          },
          "timestamp": {
            "kind": "datetime",
            "from": "2023-01-01",
            "to": "2023-12-31",
            "description": "Timestamp of the queue state.",
            "dayProfile": "business"
          }
        }
      },
      "performance_metrics": {
        "description": "This table contains performance metrics for each simulation run, linking back to simulation runs through simulation_run_id.",
        "rows": 100,
        "columns": {
          "metric_id": {
            "kind": "pk",
            "description": "Unique identifier for each performance metric entry."
          },
          "simulation_run_id": {
            "kind": "fk",
            "ref": "simulation_runs.simulation_run_id",
            "description": "Reference to the simulation run."
          },
          "average_wait_time": {
            "kind": "decimal",
            "min": 0,
            "max": 300,
            "scale": 2,
            "description": "Average wait time in seconds during the simulation.",
            "distribution": "normal"
          },
          "service_success_rate": {
            "kind": "decimal",
            "min": 0,
            "max": 1,
            "scale": 2,
            "description": "Success rate of service requests as a fraction.",
            "distribution": "high"
          },
          "timestamp": {
            "kind": "datetime",
            "from": "2023-01-01",
            "to": "2023-12-31",
            "description": "Timestamp for when the metrics were recorded."
          }
        }
      },
      "anomalies": {
        "description": "This table tracks anomalies in user interactions or service queues, linking to user interactions through user_interaction_id.",
        "rows": 150,
        "columns": {
          "anomaly_id": {
            "kind": "pk",
            "description": "Unique identifier for each anomaly entry."
          },
          "user_interaction_id": {
            "kind": "fk",
            "ref": "user_interactions.user_interaction_id",
            "description": "Reference to the user interaction."
          },
          "anomaly_type": {
            "kind": "enum",
            "values": [
              "high_queue",
              "long_wait",
              "service_fail"
            ],
            "description": "Type of anomaly detected."
          },
          "timestamp": {
            "kind": "datetime",
            "from": "2023-01-01",
            "to": "2023-12-31",
            "description": "Timestamp of when the anomaly was detected.",
            "dayProfile": "waking"
          }
        }
      }
    },
    "structure": "multi-table",
    "traits": [
      "timestamped",
      "categorical",
      "nullable"
    ],
    "analysisQuestions": [
      "What are the peak hours for user interactions and how do they affect service queues?",
      "How does the average wait time vary between different service types?",
      "What anomalies are most commonly detected during simulations and when do they occur?",
      "How does user interaction type influence the service success rate?",
      "What patterns can be observed in performance metrics over time?"
    ],
    "suggestedQueries": [
      {
        "title": "Join Simulation Runs with User Interactions",
        "sql": "SELECT sr.simulation_run_id, ui.user_interaction_id FROM simulation_runs sr JOIN user_interactions ui ON sr.simulation_run_id = ui.simulation_run_id;",
        "dialect": "postgres"
      },
      {
        "title": "Average Wait Time by Service Type",
        "sql": "SELECT sq.service_type, AVG(pm.average_wait_time) FROM service_queues sq JOIN performance_metrics pm ON sq.service_queue_id = pm.metric_id GROUP BY sq.service_type;",
        "dialect": "postgres"
      },
      {
        "title": "Count Anomalies by Type",
        "sql": "SELECT a.anomaly_type, COUNT(*) FROM anomalies a GROUP BY a.anomaly_type;",
        "dialect": "postgres"
      },
      {
        "title": "Service Success Rate Over Time",
        "sql": "SELECT pm.timestamp, AVG(pm.service_success_rate) FROM performance_metrics pm GROUP BY pm.timestamp ORDER BY pm.timestamp;",
        "dialect": "postgres"
      }
    ],
    "usageScenarios": [
      {
        "title": "Telecom Operations Analysis",
        "body": "Telecom analysts can utilize this dataset to evaluate how user interactions impact service queues and to identify peak usage times. By examining the timing and types of user interactions, they can recommend operational changes to improve efficiency and reduce wait times."
      },
      {
        "title": "Performance Monitoring",
        "body": "Data scientists in telecom companies can leverage the performance metrics table to monitor service effectiveness. They will analyze average wait times and success rates to assess whether services meet customer expectations, making adjustments as necessary based on findings."
      },
      {
        "title": "Anomaly Detection",
        "body": "Quality assurance teams can use the anomalies table to track unusual patterns in user interactions and service queues. By identifying and addressing these anomalies, they can enhance service reliability and user experience."
      }
    ]
  },
  "counts": {
    "simulation_runs": 100,
    "performance_metrics": 100,
    "user_interactions": 500,
    "anomalies": 150,
    "service_queues": 300
  },
  "stats": {
    "simulation_runs": {
      "rows": 100,
      "columns": {
        "simulation_run_id": {
          "nulls": 0,
          "distinct": 100,
          "min": 1,
          "max": 100
        },
        "start_time": {
          "nulls": 0,
          "distinct": 100,
          "min": "2023-01-04 16:30:54",
          "max": "2023-12-25 14:53:58"
        },
        "end_time": {
          "nulls": 0,
          "distinct": 100,
          "min": "2023-01-04 17:12:38",
          "max": "2023-12-25 15:49:42"
        },
        "parameters": {
          "nulls": 0,
          "distinct": 97
        }
      }
    },
    "user_interactions": {
      "rows": 500,
      "columns": {
        "user_interaction_id": {
          "nulls": 0,
          "distinct": 500,
          "min": 1,
          "max": 500
        },
        "simulation_run_id": {
          "nulls": 0,
          "distinct": 99,
          "min": 1,
          "max": 100
        },
        "timestamp": {
          "nulls": 0,
          "distinct": 500,
          "min": "2023-01-02 07:40:05",
          "max": "2023-12-31 15:56:27"
        },
        "user_id": {
          "nulls": 0,
          "distinct": 400,
          "min": 3,
          "max": 1000
        },
        "interaction_type": {
          "nulls": 0,
          "distinct": 3,
          "min": "login",
          "max": "service_request"
        }
      }
    },
    "service_queues": {
      "rows": 300,
      "columns": {
        "service_queue_id": {
          "nulls": 0,
          "distinct": 300,
          "min": 1,
          "max": 300
        },
        "user_interaction_id": {
          "nulls": 0,
          "distinct": 230,
          "min": 1,
          "max": 499
        },
        "queue_length": {
          "nulls": 0,
          "distinct": 90,
          "min": 0,
          "max": 100
        },
        "service_type": {
          "nulls": 0,
          "distinct": 3,
          "min": "data",
          "max": "voice"
        },
        "timestamp": {
          "nulls": 0,
          "distinct": 300,
          "min": "2023-01-01 22:42:40",
          "max": "2023-12-31 14:42:43"
        }
      }
    },
    "performance_metrics": {
      "rows": 100,
      "columns": {
        "metric_id": {
          "nulls": 0,
          "distinct": 100,
          "min": 1,
          "max": 100
        },
        "simulation_run_id": {
          "nulls": 0,
          "distinct": 64,
          "min": 1,
          "max": 100
        },
        "average_wait_time": {
          "nulls": 0,
          "distinct": 100,
          "min": 49.66,
          "max": 247.49
        },
        "service_success_rate": {
          "nulls": 0,
          "distinct": 56,
          "min": 0.01,
          "max": 1
        },
        "timestamp": {
          "nulls": 0,
          "distinct": 100,
          "min": "2023-01-01 00:30:37",
          "max": "2023-12-31 06:36:16"
        }
      }
    },
    "anomalies": {
      "rows": 150,
      "columns": {
        "anomaly_id": {
          "nulls": 0,
          "distinct": 150,
          "min": 1,
          "max": 150
        },
        "user_interaction_id": {
          "nulls": 0,
          "distinct": 135,
          "min": 1,
          "max": 498
        },
        "anomaly_type": {
          "nulls": 0,
          "distinct": 3,
          "min": "high_queue",
          "max": "service_fail"
        },
        "timestamp": {
          "nulls": 0,
          "distinct": 150,
          "min": "2023-01-02 21:43:25",
          "max": "2023-12-30 10:10:22"
        }
      }
    }
  },
  "artifacts": [
    {
      "file": "csv/simulation_runs.csv",
      "format": "csv",
      "table": "simulation_runs",
      "key": "datasets/business-process-simulation-software/v2/csv/simulation_runs.csv",
      "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/business-process-simulation-software/v2/csv/simulation_runs.csv",
      "bytes": 9326,
      "sha256": "fa80da0a23533901de05299254149c2b4bf50005a094613134a7302153e9ce9c",
      "rows": 100,
      "contentType": "text/csv; charset=utf-8"
    },
    {
      "file": "csv/performance_metrics.csv",
      "format": "csv",
      "table": "performance_metrics",
      "key": "datasets/business-process-simulation-software/v2/csv/performance_metrics.csv",
      "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/business-process-simulation-software/v2/csv/performance_metrics.csv",
      "bytes": 3945,
      "sha256": "408e1aa4dc9bc170d58242fa3f7c169b329cb29798185869d59db5c8efa9f2aa",
      "rows": 100,
      "contentType": "text/csv; charset=utf-8"
    },
    {
      "file": "csv/user_interactions.csv",
      "format": "csv",
      "table": "user_interactions",
      "key": "datasets/business-process-simulation-software/v2/csv/user_interactions.csv",
      "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/business-process-simulation-software/v2/csv/user_interactions.csv",
      "bytes": 20552,
      "sha256": "b9f6c738e15b473d177ad2cdec2140187efca5065acb51f31a4a0fdf336265ab",
      "rows": 500,
      "contentType": "text/csv; charset=utf-8"
    },
    {
      "file": "csv/anomalies.csv",
      "format": "csv",
      "table": "anomalies",
      "key": "datasets/business-process-simulation-software/v2/csv/anomalies.csv",
      "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/business-process-simulation-software/v2/csv/anomalies.csv",
      "bytes": 5978,
      "sha256": "04fe6f30418039be88df145d7ca192c19304b1410f3c77bb286ff3a6c2f239ce",
      "rows": 150,
      "contentType": "text/csv; charset=utf-8"
    },
    {
      "file": "csv/service_queues.csv",
      "format": "csv",
      "table": "service_queues",
      "key": "datasets/business-process-simulation-software/v2/csv/service_queues.csv",
      "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/business-process-simulation-software/v2/csv/service_queues.csv",
      "bytes": 10928,
      "sha256": "08d7868fc4cfc447e320fde8a7e581820076f57096bf519e6e9d3976f7c619ca",
      "rows": 300,
      "contentType": "text/csv; charset=utf-8"
    },
    {
      "file": "business-process-simulation-software.json",
      "format": "json",
      "table": null,
      "key": "datasets/business-process-simulation-software/v2/business-process-simulation-software.json",
      "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/business-process-simulation-software/v2/business-process-simulation-software.json",
      "bytes": 203547,
      "sha256": "bca0ead4139b7b42fd78881fbf5fd3c2e79e2eab23da1b5aacb4c50fbb62c5d4",
      "rows": null,
      "contentType": "application/json; charset=utf-8"
    },
    {
      "file": "sql/postgres.sql",
      "format": "sql-postgres",
      "table": null,
      "key": "datasets/business-process-simulation-software/v2/sql/postgres.sql",
      "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/business-process-simulation-software/v2/sql/postgres.sql",
      "bytes": 69370,
      "sha256": "7969a104db70e4e9b712454fc3440a27f6ba1e10f404c0ca817da6691a0af5ac",
      "rows": null,
      "contentType": "application/sql; charset=utf-8"
    },
    {
      "file": "sql/mysql.sql",
      "format": "sql-mysql",
      "table": null,
      "key": "datasets/business-process-simulation-software/v2/sql/mysql.sql",
      "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/business-process-simulation-software/v2/sql/mysql.sql",
      "bytes": 68618,
      "sha256": "d69b44c6c4176b6cf7e53ef86ae78b2b4d455f51e35ebb332bb15de08da6ab5f",
      "rows": null,
      "contentType": "application/sql; charset=utf-8"
    },
    {
      "file": "sql/mssql.sql",
      "format": "sql-mssql",
      "table": null,
      "key": "datasets/business-process-simulation-software/v2/sql/mssql.sql",
      "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/business-process-simulation-software/v2/sql/mssql.sql",
      "bytes": 71309,
      "sha256": "b4cff9f19dc2f463e848238309b73795009e470dc86c94b7ecba9ad3329b049f",
      "rows": null,
      "contentType": "application/sql; charset=utf-8"
    },
    {
      "file": "sql/sqlite.sql",
      "format": "sql-sqlite",
      "table": null,
      "key": "datasets/business-process-simulation-software/v2/sql/sqlite.sql",
      "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/business-process-simulation-software/v2/sql/sqlite.sql",
      "bytes": 68354,
      "sha256": "045b06594dc59faa7a94cd384825a7810d38bc3cc12ffae860ee039aee1dd1d3",
      "rows": null,
      "contentType": "application/sql; charset=utf-8"
    },
    {
      "file": "prisma/seed.ts",
      "format": "prisma-seed",
      "table": null,
      "key": "datasets/business-process-simulation-software/v2/prisma/seed.ts",
      "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/business-process-simulation-software/v2/prisma/seed.ts",
      "bytes": 176590,
      "sha256": "e4009cc770ff00f63f3c10f145a475bf16c8b466da045aff28ac39abef0e109c",
      "rows": null,
      "contentType": "application/typescript; charset=utf-8"
    },
    {
      "file": "django/fixtures.json",
      "format": "django-fixture",
      "table": null,
      "key": "datasets/business-process-simulation-software/v2/django/fixtures.json",
      "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/business-process-simulation-software/v2/django/fixtures.json",
      "bytes": 243551,
      "sha256": "10457f3fadf392b9b01f2b2e37ee1c2ff831d28b0585e7bf9201f1baa7cea2bb",
      "rows": null,
      "contentType": "application/json; charset=utf-8"
    }
  ],
  "publishedAt": 1788766258227,
  "updatedAt": 1788777707213
}