{
  "format": "dataset-manifest/1",
  "slug": "telecom-model-parameters",
  "version": 1,
  "seed": "telecom-model-parameters:1",
  "spec": {
    "format": "dataset/1",
    "slug": "telecom-model-parameters",
    "name": "Telecom Model Parameters",
    "tagline": "Parameters for telecom model development including materials, design changes, simulations, and user feedback.",
    "description": "Design teams create and refine structures for network components using 3D parametric modeling, continually updating the materials and geometries to meet evolving industry standards. Each model undergoes a variety of design changes, often requiring simulations to evaluate performance outcomes and validate the effectiveness of these adjustments. This iterative development process is critical in the telecom industry, where staying ahead of technological advancements is essential for maintaining competitive advantage. \n\nThe dataset consists of several tables that interrelate to paint a complete picture of the model development lifecycle. The 'models' table serves as the central hub, containing entries for each design being developed. Each model can be associated with various 'materials' that provide the physical properties needed for simulations. As designs progress, 'design_changes' track the modifications made to each model, linking back to the original entries in the 'models' table. Simulated performance results are captured in the 'simulations' table, which connects to both the 'models' and 'design_changes' tables, allowing for a comprehensive understanding of how specific alterations impact overall performance. Additionally, 'model_users' tracks the team members involved in the modeling process, while 'model_feedback' captures insights and evaluations from users, providing a feedback loop that informs future design decisions.\n\nThis dataset answers key questions regarding model performance and user input, offering opportunities to analyze the impact of specific materials on simulation outcomes or to assess how feedback influences design changes. It is particularly useful for practicing data analysis techniques, including join operations and performance analytics, as well as exploring trends in model evolution over time. The data is synthetic, crafted to mimic real-world scenarios while allowing for safe experimentation and learning.",
    "domain": "telecom",
    "shape": "time-series",
    "useCases": [
      "analytics-demo",
      "bi-dashboards"
    ],
    "keywords": [
      "telecom",
      "3D modeling",
      "design changes",
      "simulation",
      "material properties",
      "feedback"
    ],
    "locale": "en",
    "license": "CC0-1.0",
    "version": 1,
    "tables": {
      "models": {
        "description": "Each row represents a unique telecom model, including its specifications and version history. This table serves as the core reference point for all related tables.",
        "rows": 100,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each model."
          },
          "name": {
            "kind": "fullName",
            "description": "The name of the telecom model."
          },
          "version": {
            "kind": "int",
            "min": 1,
            "max": 10,
            "description": "Version number of the model."
          },
          "created_at": {
            "kind": "datetime",
            "from": "2020-01-01",
            "to": "2023-12-31",
            "description": "Creation date of the model.",
            "dayProfile": "business"
          }
        }
      },
      "materials": {
        "description": "This table tracks materials used in various models, linking directly to the models table. Each entry reflects a specific material's properties and usage context.",
        "rows": {
          "per": "models",
          "via": "model_id",
          "min": 1,
          "max": 5,
          "skew": "long-tail"
        },
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each material entry."
          },
          "model_id": {
            "kind": "fk",
            "ref": "models.id",
            "description": "Reference to the model this material belongs to."
          },
          "material_type": {
            "kind": "enum",
            "values": [
              "plastic",
              "metal",
              "composite"
            ],
            "description": "Type of material used."
          },
          "density": {
            "kind": "decimal",
            "min": 1,
            "max": 10,
            "scale": 2,
            "description": "Density of the material in g/cm^3.",
            "nullable": true,
            "when": {
              "column": "material_type",
              "in": [
                "plastic",
                "metal"
              ]
            }
          },
          "cost": {
            "kind": "decimal",
            "min": 10,
            "max": 500,
            "scale": 2,
            "description": "Cost of the material per unit.",
            "nullable": true,
            "when": {
              "column": "material_type",
              "in": [
                "plastic",
                "metal"
              ]
            }
          }
        }
      },
      "design_changes": {
        "description": "Design changes made to models are recorded here. Each entry is linked to a specific model and includes details about the nature of changes.",
        "rows": {
          "per": "models",
          "via": "model_id",
          "min": 1,
          "max": 10,
          "skew": "long-tail"
        },
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each design change."
          },
          "model_id": {
            "kind": "fk",
            "ref": "models.id",
            "description": "Reference to the model that was changed."
          },
          "change_description": {
            "kind": "sentence",
            "description": "Description of the design change.",
            "nullable": true
          },
          "changed_at": {
            "kind": "datetime",
            "from": "2020-01-01",
            "to": "2023-12-31",
            "description": "Date when the change was made.",
            "dayProfile": "business"
          }
        }
      },
      "simulations": {
        "description": "Simulation results for each model, assessing performance metrics based on design parameters. Each simulation is linked to a model entry.",
        "rows": {
          "per": "models",
          "via": "model_id",
          "min": 1,
          "max": 5,
          "skew": "long-tail"
        },
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each simulation result."
          },
          "model_id": {
            "kind": "fk",
            "ref": "models.id",
            "description": "Reference to the model being simulated."
          },
          "performance_metric": {
            "kind": "decimal",
            "min": 0,
            "max": 100,
            "scale": 2,
            "description": "Performance score based on simulation."
          },
          "simulated_at": {
            "kind": "datetime",
            "from": "2020-01-01",
            "to": "2023-12-31",
            "description": "Date when the simulation was conducted.",
            "dayProfile": "waking"
          }
        }
      },
      "model_users": {
        "description": "Users involved in the development and refinement of the models. Each entry is linked to a model, capturing user roles and interactions.",
        "rows": {
          "per": "models",
          "via": "model_id",
          "min": 1,
          "max": 5,
          "skew": "long-tail"
        },
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each user entry."
          },
          "model_id": {
            "kind": "fk",
            "ref": "models.id",
            "description": "Reference to the model associated with the user."
          },
          "user_role": {
            "kind": "enum",
            "values": [
              "designer",
              "engineer",
              "analyst"
            ],
            "description": "Role of the user in the modeling process."
          },
          "user_name": {
            "kind": "fullName",
            "description": "Name of the user.",
            "nullable": true,
            "when": {
              "column": "user_role",
              "in": [
                "designer",
                "engineer"
              ]
            }
          }
        }
      },
      "model_feedback": {
        "description": "Feedback collected from users regarding the models. This table links feedback directly to the model it pertains to, helping to capture iterative improvements.",
        "rows": {
          "per": "models",
          "via": "model_id",
          "min": 1,
          "max": 5,
          "skew": "long-tail"
        },
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each feedback entry."
          },
          "model_id": {
            "kind": "fk",
            "ref": "models.id",
            "description": "Reference to the model receiving feedback."
          },
          "feedback": {
            "kind": "paragraph",
            "description": "The feedback comments provided by users.",
            "nullable": true
          },
          "submitted_at": {
            "kind": "datetime",
            "from": "2020-01-01",
            "to": "2023-12-31",
            "description": "Date when the feedback was submitted.",
            "dayProfile": "waking"
          }
        }
      }
    },
    "structure": "multi-table",
    "traits": [
      "timestamped",
      "categorical",
      "nullable"
    ],
    "analysisQuestions": [
      "What are the most commonly used materials in model designs?",
      "How do design changes correlate with performance metrics in simulations?",
      "What user roles provide the most feedback on model improvements?",
      "Which models see the most frequent design changes and why might that be?",
      "How does the cost of materials impact the overall design changes?"
    ],
    "suggestedQueries": [
      {
        "title": "Join Models with Materials",
        "sql": "SELECT models.name, materials.material_type FROM models JOIN materials ON models.id = materials.model_id;",
        "dialect": "postgres"
      },
      {
        "title": "Count Design Changes by Model",
        "sql": "SELECT model_id, COUNT(*) FROM design_changes GROUP BY model_id;",
        "dialect": "postgres"
      },
      {
        "title": "Average Performance by Material Type",
        "sql": "SELECT materials.material_type, AVG(simulations.performance_metric) FROM simulations JOIN materials ON simulations.model_id = materials.model_id GROUP BY materials.material_type;",
        "dialect": "postgres"
      },
      {
        "title": "User Feedback Over Time",
        "sql": "SELECT submitted_at, COUNT(*) FROM model_feedback GROUP BY submitted_at ORDER BY submitted_at;",
        "dialect": "postgres"
      },
      {
        "title": "Changes and Feedback Correlation",
        "sql": "SELECT design_changes.change_description, AVG(model_feedback.feedback) FROM design_changes JOIN model_feedback ON design_changes.model_id = model_feedback.model_id GROUP BY design_changes.change_description;",
        "dialect": "postgres"
      }
    ],
    "usageScenarios": [
      {
        "title": "Telecom Design Teams",
        "body": "Design engineers can utilize this dataset to analyze how different materials impact the performance of telecom models. By examining the relationships between design changes and simulation results, they can make informed decisions on material selections and modifications."
      },
      {
        "title": "Business Intelligence Analysts",
        "body": "BI analysts can leverage the data to create dashboards that visualize model performance trends over time. By integrating feedback and usage patterns, they can provide insights on user satisfaction and areas for improvement in model designs."
      },
      {
        "title": "Quality Assurance Teams",
        "body": "QA teams can analyze the dataset to identify patterns in design changes and their impacts on model performance. This allows for the identification of best practices and potential pitfalls in the modeling process."
      },
      {
        "title": "Training and Development",
        "body": "Training coordinators can use this dataset for hands-on workshops on data analysis and visualization. Participants can practice SQL queries and dashboard creation, enhancing their skills in handling telecom model data."
      }
    ]
  },
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    "design_changes": 412,
    "materials": 232,
    "model_feedback": 215,
    "model_users": 213,
    "simulations": 224
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