Reference

• Analytic Catalog Entry Model
• Analytic Validation Result Model
• Analytic Deployment Request Model
• Analytic Deployment Result Model
• Analytic Execution Result Model
• Asynchronous Execution Status Response Model

Analytic Catalog Entry Model

Analytic Validation Result Model

Analytic Deployment Request Model

Analytic Deployment Result Model

Analytic Execution Result Model

Asynchronous Execution Status Response Model

The deployed analytic URI format is https://<analytic id>.analytics.<Predix platform domain>.

The parts of the analytic URI are determined as follows.

• "analytic id" is the id returned by the Analytics Catalog service when an analytic entry is created.
• "Predix platform domain" is the sub string of the Analytics Catalog service URI starting at ".run". For example, if the analytics catalog service URI is predix-analytics-catalog-release.run.aws-usw02-pr.ice.predix.io, then the Predix platform domain is run.aws-usw02-pr.ice.predix.io.

For example, if your "analytic id" is 09718078-95e7-4b60-b74a-152838f03b41 and the Analytics Catalog service URI is predix-analytics-catalog-release.run.aws-usw02-pr.ice.predix.io, then the analytic URI is https://09718078-95e7-4b60-b74a-152838f03b41.analytics.run.aws-usw02-pr.ice.predix.io.

Required Structures

The following table summarizes the required major structures in each BPMN 2.0 XML file created as an orchestration workflow file.

The serviceTask Naming Conventions

Each analytic in the orchestration is represented as a serviceTask element in the orchestration workflow file.

name="<analytic_id>::<analytic_name>::<analytic_version>"

where <analytic_id>, <analytic_name>, and <analytic_version> match the ID, name, and version assigned when the analytic was added to the catalog.

• Overview
• Type: AnalyticTemplate
• Type: DataPortDefinition
• Template Examples

Overview

The analytic template name (hierarchical, descriptive) describes the data format type you are working with. Descriptive data format is input and output data that is flat in structure but has self-describing attributes such as the data type and data size.

Each analytic has one template that is stored in the Analytics Catalog. The template is a JSON structure. For more information about JSON terminology, see http://www.json.org.

Type: AnalyticTemplate

This is the overall structure of a descriptive analytic template.

{
    "name" : <String>,
    "description" : <String>,
    "packageName" : <String>,
    "codeLanguage" : <String>,
    "version" : <String>,
    "author" : <String>,
    "inputDataPortDefinitions" : [<List of DataPortDefinition>],
    "constantDataPortDefinitions" : [<List of DataPortDefinition>],
    "outputDataPortDefinitions" : [<List of DataPortDefinition>]
}

Type: DataPortDefinition

The inputDataPortDefinitions, constantDataPortDefinitions and outputDataPortDefinitions elements of the analytic template contain arrays of DataPortDefinition that specify the inputs, constants and outputs of the analytic.

{
    "timeSeriesName" : <String>,
    "name" : <String>,
    "description" : <String>,
    "dataType" : <String>,
    "variable" : <Boolean>,
    "arrayDimensions" : <Integer>,
    "validators" : [<List of String>]
}

See the following table for a description of the elements in a DataPortDefinition.

Template Examples

Analytic templates are used at runtime in conjunction with Port-to-Field Map configurations to control the runtime engine's handling of the analytic input and output. See Samples of Descriptive Analytic Templates and Port-to-Field Maps.

• Sample: Analytic Adder with Double Array Ports
• Sample: Analytic Adder with Timeseries and Constant Ports
• Sample: Analytic Adder with Variable Timeseries Ports
• Sample: Analytic Adder with Multi-Dimensional Timeseries Ports

Sample: Analytic Adder with Double Array Ports

The following is a sample JSON input.

{
  "internalRecords": [],
  "records": [
    {
      "type": "DOUBLE",
      "name": "numberArray1",
      "data": [
        5.0,
        6.0,
        7.0,
        8.0,
        9.0,
        10.0
      ],
      "arraySizes": [
        6
      ]
    },
    {
      "type": "DOUBLE",
      "name": "numberArray2",
      "data": [
        500.0,
        600.0,
        700.0,
        800.0,
        900.0,
        1000.0
      ],
      "arraySizes": [
        6
      ]
    }
  ],
  "timeSeriesRecords": []
}

The following is a sample JSON output.

{
  "records": [
    {
      "type": "DOUBLE",
      "name": "results",
      "data": [
        505.0,
        606.0,
        707.0,
        808.0,
        909.0,
        1010.0
      ],
      "arraySizes": [
        6
      ]
    }
  ]
}

The following is a sample descriptive analytic template.

{
  "name": "DoubleArrayAdder",
  "description": "",
  "packageName": "",
  "codeLanguage": "JAVA",
  "version": "1.0.0",
  "author": "Predix Analytics team",
  "inputDataPortDefinitions": [
    {
      "name": "numberArray1",
      "description": "",
      "dataType": "Double",
      "valueRequired": true,
      "variable": false,
      "arrayDimensions": 1
    },
    {
      "name": "numberArray2",
      "description": "",
      "dataType": "Double",
      "valueRequired": true,
      "variable": false,
      "arrayDimensions": 1
    }
  ],
  "outputDataPortDefinitions": [
    {
      "name": "results",
      "description": "",
      "dataType": "Double",
      "valueRequired": false,
      "variable": false,
      "arrayDimensions": 1
    }
  ]
}

The following is a sample port-to-field map.

{
  "analyticName": "DoubleArrayAdder",
  "analyticVersion": "1.0",
  "orchestrationStepId": "sid-10001",
  "iterations": [
    {
      "inputMaps": [
        {
          "valueSourceType": "DATA_CONNECTOR",
          "fullyQualifiedPortName": "numberArray1",
          "fieldId": "KW",
          "queryCriteria": {
            "start": 1455733669605,
            "end": 1455733669610
          },
          "engUnit": "kw",
          "required": true,
          "dataSourceId": "Predix Time Series"
        },
        {
          "valueSourceType": "DATA_CONNECTOR",
          "fullyQualifiedPortName": "numberArray2",
          "fieldId": "vibration",
          "queryCriteria": {
            "start": 1455733669605,
            "end": 1455733669610
          },
          "engUnit": "hertz",
          "required": true,
          "dataSourceId": "Predix Time Series"
        }
      ],
      "outputMaps": [
        {
          "fullyQualifiedPortName": "results",
          "fieldId": "bearing temperature final",
          "engUnit": "Celsius",
          "dataSourceId": "Predix Time Series"
        }
      ]
    }
  ]
}

Sample: Analytic Adder with Timeseries and Constant Ports

Scenario: An analytic requires a time series table and one constant, where the time series input table has two fixed columns; numberArray1 and numberArray2. Sample analytic templates and port-to-field maps to support this scenario follow.

The following is a sample JSON input.

{
    "internalRecords": [],
    "records": [
        {
            "type": "DOUBLE",
            "name": "constant1",
            "data": [
                55.0
            ],
            "arraySizes": [
                1
            ]
        }
    ],
    "timeSeriesRecords": [
        {
            "name": "time_series",
            "timestamps": [
                "2016-02-17T18:27:49.601",
                "2016-02-17T18:27:49.602",
                "2016-02-17T18:27:49.603"
            ],
            "data": [
                {
                    "type": "DOUBLE",
                    "name": "numberArray1",
                    "data": [
                        1.0,
                        2.0,
                        3.0
                    ],
                    "arraySizes": [
                        3
                    ]
                },
                {
                    "type": "DOUBLE",
                    "name": "numberArray2",
                    "data": [
                        100.0,
                        200.0,
                        300.0
                    ],
                    "arraySizes": [
                        3
                    ]
                }
            ]
        }
    ]
}

The following is a sample analytic output.

{
  "internalRecords": [],
  "records": [],
  "timeSeriesRecords": [
    {
      "name": "time_series",
      "timestamps": [
        "2016-02-17T18:27:49.601",
        "2016-02-17T18:27:49.602",
        "2016-02-17T18:27:49.603"
      ],
      "data": [
        {
          "type": "DOUBLE",
          "name": "results",
          "data": [
            101.0,
            202.0,
            303.0
          ],
          "arraySizes": [
            3
          ]
        }
      ]
    }
  ]
}

The following is a sample descriptive analytic template.

{
  "name": "TimeseriesAdder",
  "description": "",
  "packageName": "",
  "codeLanguage": "JAVA",
  "version": "1.0.0",
  "author": "Predix Analytics team",
  "inputDataPortDefinitions": [
    {
      "name": "time_series",
      "description": "",
      "dataType": "TimeSeries",
      "valueRequired": false,
      "variable": false,
      "arrayDimensions": 0
    },
    {
      "timeSeriesName": "time_series",
      "name": "numberArray1",
      "description": "",
      "dataType": "Double",
      "valueRequired": true,
      "variable": false,
      "arrayDimensions": 1
    },
    {
      "timeSeriesName": "time_series",
      "name": "numberArray2",
      "description": "",
      "dataType": "Double",
      "valueRequired": true,
      "variable": false,
      "arrayDimensions": 1
    }
  ],
  "constantDataPortDefinitions": [
    {
      "name": "constant1",
      "description": "",
      "dataType": "Double",
      "valueRequired": true,
      "variable": false,
      "arrayDimensions": 0
    }
  ],
  "outputDataPortDefinitions": [
    {
      "name": "time_series",
      "description": "",
      "dataType": "TimeSeries",
      "valueRequired": false,
      "variable": false,
      "arrayDimensions": 0
    },
    {
      "timeSeriesName": "time_series",
      "name": "results",
      "description": "",
      "dataType": "Double",
      "valueRequired": false,
      "variable": false,
      "arrayDimensions": 1
    }
  ]
}

The following is a sample port-to-field map.

{
  "analyticName": "timeseries-adder",
  "analyticVersion": "1.0",
  "orchestrationStepId": "sid-10001",
  "iterations": [
    {
      "inputMaps": [
        {
          "valueSourceType": "DATA_CONNECTOR",
          "fullyQualifiedPortName": "time_series.numberArray1",
          "fieldId": "KW",
          "queryCriteria": {
            "start": 1455733669605,
            "end": 1455733669610
          },
          "engUnit": "kw",
          "required": true,
          "dataSourceId": "Predix Time Series"
        },
        {
          "valueSourceType": "DATA_CONNECTOR",
          "fullyQualifiedPortName": "time_series.numberArray2",
          "fieldId": "vibration",
          "queryCriteria": {
            "start": 1455733669605,
            "end": 1455733669610
          },
          "engUnit": "hertz",
          "required": true,
          "dataSourceId": "Predix Time Series"
        },
        {
          "valueSourceType": "DATA_CONNECTOR",
          "fullyQualifiedPortName": "constant1",
          "fieldId": "engineType",
          "queryCriteria": {
            "columns": [
              "data_value"
            ],
            "table": "sensor_data",
            "conditions": [
              {
                "key": "asset_id",
                "value":"${ASSET_ID}",
                "valueType": "string",
                "relation": " = "
              },
              {
                "key": "field_id",
                "value": "engineType",
                "valueType": "string",
                "relation": " = "
              }
            ]
          },
          "required": true,
          "dataSourceId": "Custom Data Source"
        }
      ],
      "outputMaps": [
        {
          "fullyQualifiedPortName": "time_series.results",
          "fieldId": "bearing temperature final",
          "engUnit": "Celsius",
          "dataSourceId": "Predix Time Series"
        }
      ]
    }
  ]
}

Sample: Analytic Adder with Variable Timeseries Ports

The following is a sample JSON input.

{
    "internalRecords": [],
    "records": [],
    "timeSeriesRecords": [
        {
            "name": "time_series",
            "timestamps": [
                "2016-02-17T18:27:49.601",
                "2016-02-17T18:27:49.602",
                "2016-02-17T18:27:49.603"
            ],
            "data": [
                {
                    "type": "DOUBLE",
                    "name": "temperature_2",
                    "data": [
                        1.0,
                        2.0,
                        3.0
                    ],
                    "arraySizes": [
                        3
                    ]
                },
                {
                    "type": "DOUBLE",
                    "name": "temperature_5",
                    "data": [
                        5.0,
                        6.0,
                        7.0
                    ],
                    "arraySizes": [
                        3
                    ]
                },
                {
                    "type": "DOUBLE",
                    "name": "vibration_2",
                    "data": [
                        100.0,
                        200.0,
                        300.0
                    ],
                    "arraySizes": [
                        3
                    ]
                },
                {
                    "type": "DOUBLE",
                    "name": "vibration_5",
                    "data": [
                        500.0,
                        600.0,
                        700.0
                    ],
                    "arraySizes": [
                        3
                    ]
                }
            ]
        }
    ]
}

The following is a sample JSON output.

{
    "internalRecords": [],
    "records": [],
    "timeSeriesRecords": [
        {
            "name": "time_series",
            "timestamps": [
                "2016-02-17T18:27:49.601",
                "2016-02-17T18:27:49.602",
                "2016-02-17T18:27:49.603"
            ],
            "data": [
                {
                    "type": "DOUBLE",
                    "name": "results_2",
                    "data": [
                        101.0,
                        202.0,
                        303.0
                    ],
                    "arraySizes": [
                        3
                    ]
                },
                {
                    "type": "DOUBLE",
                    "name": "results_5",
                    "data": [
                        505.0,
                        606.0,
                        707.0
                    ],
                    "arraySizes": [
                        3
                    ]
                }
            ]
        }
    ]
}

The following is a sample descriptive analytic template.

{
    "name": "TimeseriesAdder",
    "description": "",
    "packageName": "",
    "codeLanguage": "JAVA",
    "version": "1.0.0",
    "author": "Predix Analytics team",
    "inputDataPortDefinitions": [
        {
            "name": "time_series",
            "description": "",
            "dataType": "TimeSeries",
            "valueRequired": false,
            "variable": false,
            "arrayDimensions": 0
        },
        {
            "timeSeriesName": "time_series",
            "name": "temperature",
            "description": "",
            "dataType": "Double",
            "valueRequired": true,
            "variable": true,
            "arrayDimensions": 1
        },
        {
            "timeSeriesName": "time_series",
            "name": "vibration",
            "description": "",
            "dataType": "Double",
            "valueRequired": true,
            "variable": true,
            "arrayDimensions": 1
        }
    ],
    "outputDataPortDefinitions": [
        {
            "name": "time_series",
            "description": "",
            "dataType": "TimeSeries",
            "valueRequired": false,
            "variable": false,
            "arrayDimensions": 0
        },
        {
            "timeSeriesName": "time_series",
            "name": "results",
            "description": "",
            "dataType": "Double",
            "valueRequired": false,
            "variable": true,
            "arrayDimensions": 1
        }
    ]
}

The following is a sample port-to-field map.

{
    "analyticName": "timeseries-adder",
    "analyticVersion": "1.0",
    "orchestrationStepId": "sid-10001",
    "iterations": [
        {
            "inputMaps": [
                {
                    "valueSourceType": "DATA_CONNECTOR",
                    "fullyQualifiedPortName": "time_series.temperature_2",
                    "fieldId": "temp.left",
                    "queryCriteria": {
                        "start": 1455733669601,
                        "end": 1455733669603
                    },
                    "required": true,
                    "dataSourceId": "Predix Time Series"
                },
                {
                    "valueSourceType": "DATA_CONNECTOR",
                    "fullyQualifiedPortName": "time_series.temperature_5",
                    "fieldId": "temp.right",
                    "queryCriteria": {
                        "start": 1455733669601,
                        "end": 1455733669603
                    },
                    "required": true,
                    "dataSourceId": "Predix Time Series"
                },
                {
                    "valueSourceType": "DATA_CONNECTOR",
                    "fullyQualifiedPortName": "time_series.vibration_2",
                    "fieldId": "vibration.left",
                    "queryCriteria": {
                        "start": 1455733669601,
                        "end": 1455733669603
                    },
                    "required": true,
                    "dataSourceId": "Predix Time Series"
                },
                {
                    "valueSourceType": "DATA_CONNECTOR",
                    "fullyQualifiedPortName": "time_series.vibration_5",
                    "fieldId": "vibration.right",
                    "queryCriteria": {
                        "start": 1455733669601,
                        "end": 1455733669603
                    },
                    "required": true,
                    "dataSourceId": "Predix Time Series"
                }
            ],
            "outputMaps": [
                {
                    "fullyQualifiedPortName": "time_series.results_2",
                    "fieldId": "results.left",
                    "dataSourceId": "Predix Time Series"
                }
                {
                    "fullyQualifiedPortName": "time_series.results_5",
                    "fieldId": "results.right",
                    "dataSourceId": "Predix Time Series"
                }
            ]
        }
    ]
}

Sample: Analytic Adder with Multi-Dimensional Timeseries Ports

The following is a sample JSON input.

{
    "internalRecords": [],
    "records": [],
    "timeSeriesRecords": [
        {
            "name": "time_series",
            "timestamps": [
                "2016-02-17T18:27:49.601",
                "2016-02-17T18:27:49.602",
                "2016-02-17T18:27:49.603",
                "2016-03-17T18:27:49.601",
                "2016-03-17T18:27:49.602",
                "2016-03-17T18:27:49.603"
            ],
            "data": [
                {
                    "type": "DOUBLE",
                    "name": "temperature",
                    "data": [
                        1.0,
                        2.0,
                        3.0,
                        5.0,
                        6.0,
                        7.0
                    ],
                    "arraySizes": [
                        2,
                        3
                    ]
                },
                {
                    "type": "DOUBLE",
                    "name": "vibration",
                    "data": [
                        100.0,
                        200.0,
                        300.0,
                        500.0,
                        600.0,
                        700.0
                    ],
                    "arraySizes": [
                        2,
                        3
                    ]
                }
            ]
        }
    ]
}

The following is a sample analytic output.

{
    "internalRecords": [],
    "records": [],
    "timeSeriesRecords": [
        {
            "name": "time_series",
            "timestamps": [
                "2016-02-17T18:27:49.601",
                "2016-02-17T18:27:49.602",
                "2016-02-17T18:27:49.603",
                "2016-03-17T18:27:49.601",
                "2016-03-17T18:27:49.602",
                "2016-03-17T18:27:49.603"
            ],
            "data": [
                {
                    "type": "DOUBLE",
                    "name": "results",
                    "data": [
                        101.0,
                        202.0,
                        303.0,
                        505.0,
                        606.0,
                        707.0
                    ],
                    "arraySizes": [
                        2,
                        3
                    ]
                }
            ]
        }
    ]
}

The following is a sample descriptive analytic template.

{
    "name": "TimeseriesAdder",
    "description": "",
    "packageName": "",
    "codeLanguage": "JAVA",
    "version": "1.0.0",
    "author": "Predix Analytics team",
    "inputDataPortDefinitions": [
        {
            "name": "time_series",
            "description": "",
            "dataType": "TimeSeries",
            "valueRequired": false,
            "variable": false,
            "arrayDimensions": 0
        },
        {
            "timeSeriesName": "time_series",
            "name": "temperature",
            "description": "",
            "dataType": "Double",
            "valueRequired": true,
            "variable": false,
            "arrayDimensions": 2
        },
        {
            "timeSeriesName": "time_series",
            "name": "vibration",
            "description": "",
            "dataType": "Double",
            "valueRequired": true,
            "variable": false,
            "arrayDimensions": 2
        }
    ],
    "outputDataPortDefinitions": [
        {
            "name": "time_series",
            "description": "",
            "dataType": "TimeSeries",
            "valueRequired": false,
            "variable": false,
            "arrayDimensions": 0
        },
        {
            "timeSeriesName": "time_series",
            "name": "results",
            "description": "",
            "dataType": "Double",
            "valueRequired": false,
            "variable": false,
            "arrayDimensions": 2
        }
    ]
}

The following is a sample port-to-field map.

{
    "analyticName": "timeseries-adder",
    "analyticVersion": "1.0",
    "orchestrationStepId": "sid-10001",
    "iterations": [
        {
            "inputMaps": [
                {
                    "valueSourceType": "DATA_CONNECTOR",
                    "fullyQualifiedPortName": "time_series.temperature[0]",
                    "fieldId": "temperature.previous",
                    "queryCriteria": {
                        "start": 1455733669601,
                        "end": 1455733669603
                    },
                    "required": true,
                    "dataSourceId": "Predix Time Series"
                },
                {
                    "valueSourceType": "DATA_CONNECTOR",
                    "fullyQualifiedPortName": "time_series.temperature[1]",
                    "fieldId": "temperature.current",
                    "queryCriteria": {
                        "start": 1458239269601,
                        "end": 1458239269603
                    },
                    "required": true,
                    "dataSourceId": "Predix Time Series"
                },
                {
                    "valueSourceType": "DATA_CONNECTOR",
                    "fullyQualifiedPortName": "time_series.vibration[0]",
                    "fieldId": "vibration.previous",
                    "queryCriteria": {
                        "start": 1455733669601,
                        "end": 1455733669603
                    },
                    "required": true,
                    "dataSourceId": "Predix Time Series"
                },
                {
                    "valueSourceType": "DATA_CONNECTOR",
                    "fullyQualifiedPortName": "time_series.vibration[1]",
                    "fieldId": "vibration.current",
                    "queryCriteria": {
                        "start": 1458239269601,
                        "end": 1458239269603
                    },
                    "required": true,
                    "dataSourceId": "Predix Time Series"
                }
            ],
            "outputMaps": [
                {
                    "fullyQualifiedPortName": "time_series.results[0]",
                    "fieldId": "results.previous",
                    "dataSourceId": "Predix Time Series"
                },
                {
                    "fullyQualifiedPortName": "time_series.results[1]",
                    "fieldId": "results.current",
                    "dataSourceId": "Predix Time Series"
                }
            ]
        }
    ]
}

• Overview
• Type: AnalyticTemplate
• Type: PortDefinition
• Type: FieldPort
• Type: TimeseriesArrayPort
• Type: CompositePort
• Type: InputModel
• Template Examples

Overview

The analytic template name (hierarchical, descriptive) describes the data format type you are working with. Hierarchical data format is input and output data that can be arbitrarily nested.

Each analytic has one template that is stored in the Analytics Catalog. The template is a JSON structure. For more information about JSON terminology, see http://www.json.org.

Type: AnalyticTemplate

This is the overall structure of an hierarchical analytic template.

{
    "analyticName":<string>,
    "analyticVersion":<string>,
    "comment":[<string>],
    "inputPortDefinitions":[<List of PortDefinition>],
    "inputModelDefinitions":[<List of InputModel>],
    "outputPortDefinitions":[<List of PortDefinition>]
}

See the following table for a description of the elements in an AnalyticTemplate.

Type: PortDefinition

The inputPortDefinitions and outputPortDefinitions elements of the analytic template contain arrays of PortDefinitions that specify the inputs and outputs of the analytic. There are several types of PortDefinitions, but they all have the following elements in common.

{
    "comment":[<string>],
    "portName":<string>,
    "portType":<string>,
    "variable":<boolean>
}

See the following table for a description of the elements in a PortDefinition.

Type: FieldPort

A FieldPort is a type of PortDefinition that holds the actual data values to be passed to/from the analytic. A FieldPort defines a leaf node in the analytic's JSON structure.

{
    "comment":[<string>],
    "portName":<string>,
    "portType":<string>,
    "variable":<boolean>,
    "engUnit":<string>,
    "dataType":<string>,
    "required":<boolean>,
    "timestampShared":<string>
}

Extends from PortDefinition.

FieldPort definitions correspond to "<port name>": <value determined at runtime (see Port-to-Field Map description)> pairs into the JSON structure.

In addition to the common elements of a PortDefinition, a FieldPort contains the following elements.

Type: TimeseriesArrayPort

A TimeseriesArrayPort is a type of PortDefinition that is used to define a JSON structure that represents a time series data table. This is the overall structure of a TimeseriesArrayPort.

{
    "comment": [<string>],
    "portName": <string>,
    "portType": <string>,
    "variable": <boolean>,
    "columns": [<List of FieldPorts>]
}

Extends from PortDefinition.

In addition to the common elements of a PortDefinition, a TimeseriesArrayPort holds an array of FieldPorts called columns.

Field: columns

Contains FieldPort column definitions where each FieldPort column is an array data type. The generated JSON structure will contain one JSON array for each FieldPort column and an additional 'time_stamp' array object containing the timestamps of the table values, as the example shows below.

Note: All columns in the time series array will share the same time stamps. The quality attribute from Time Series is not supported.

{
    “<timeseries array port name>” : {
        “time_stamp” : [<timestamp values associated with field port values>],
        [<list of JSON objects for the field port definitions>]
    }
}

Note: The time series values for each of the field port definitions must contain the same set of time stamps. If they do not, the platform will trigger an error.

Type: CompositePort

A CompositePort is a type of PortDefinition that contains other PortDefinitions. This is the overall structure of a CompositePort.

{
    "comment":[<string>],
    "portName":<string>,
    "portType":<string>,
    "variable":<boolean>,
    "childrenPorts":[<List of PortDefinition>]
}

Extends from: PortDefinition.

In addition to the common elements of a PortDefinition, a CompositePort holds an array of childrenPorts.

Field: childrenPorts

Contains a list of inner (children) PortDefinitions. When the variable flag is false, the generated JSON object (corresponding to the composite port) will contain each child port as a direct child node of the composite port, following this pattern.

{
    “<composite port name>”: {
        <JSON structure from the Port-to-Field Map for the first child port definition>, 
		
        ...
		
        <JSON structure from the Port-to-Field Map for the last child port definition>
    } 
}

When the variable flag is true, the generated JSON object (corresponding to the composite port) will wrap the child nodes in an array under the composite port, following this pattern.

{
    "<composite port name>" : [
        {
            <JSON structure from Port-to-Field Map for the first child>,
            
            ...,
            
            <JSON structure from Port-to-Field Map for the last child>,        
        },
        
        ...,
        
        {
            <JSON structure from Port-to-Field Map for the first child>,
            
            ...,
            
            <JSON structure from Port-to-Field Map for the last child>,        
        }
    ]
}

Type: InputModel

An InputModel is a named byte array that the analytic uses in its processing. The values for the model are managed as part of an orchestration configuration and are associated with an asset context.

{ "modelPortName": <string>,
 "comments" : [<string>] }

See the following table for a description of the elements in an InputModel.

Template Examples

Analytic templates are used at runtime in conjunction with port-to-field map configurations to control the runtime engine's handling of the analytic input and output. See Samples of Hierarchical Analytic Templates and Port-to-Field Maps.

• Sample: DemoAdder Analytic
• Sample: Analytic Requiring a Time Series Table, Input Models, and Constants
• Sample: Analytic Requiring a Time Series Table With Variable Time Stamps
• Sample: Array of Arrays Model
• Sample: Multiple Iterations of Same Analytic
• Sample: Analytic Requiring Predix Time Series Data Using an Array of Tags
• Sample: Analytic Requiring Predix Time Series Data Using Variable Input Ports
• Sample: Reusing a Port-to-Field Map When Providing Data Is Optional

Sample: DemoAdder Analytic

The following is a sample JSON input.

{
  "number1": <latest value from temperature sensor1>,
  "number2": -55
}

The following is a sample JSON output.

{"result" : <result from analytic>}

The following is a sample hierarchical analytic template.

{
  "analyticName": "add2Numbers",
  "analyticVersion": "V1.0",
  "inputPortDefinitions": [
    {
      "portType": "FIELD",
      "portName": "number1",
      "variable": false,
      "required": true
    },
    {
      "portType": "FIELD",
      "portName": "number2",
      "variable": false,
      "required": true
    }
  ],
  "outputPortDefinitions": [
    {
      "portType": "FIELD",
      "portName": "result",
      "variable": false,
      "required": true
    }
  ]
}

The following is a sample port-to-field map.

{
    "analyticName": "add2Numbers",
    "analyticVersion": "V1.0",
    "orchestrationStepId": "adjust temp by ambient",
    "iterations": [
        {
            "inputMaps": [
                {
                    "valueSourceType": "DATA_CONNECTOR",
                    "fullyQualifiedPortName": "number1",
                    "fieldId": "temperature sensor1",
                    "queryCriteria": {"start": 1464989425852, "end": 1464989425852},
                    "dataSourceId": "PredixTimeSeries"
                },
                {
                    "valueSourceType": "CONSTANT",
                    "fullyQualifiedPortName": "number2",
                    "value": -55
                }
            ],
            "outputMaps": [
                {
                    "fullyQualifiedPortName": "result",
                    "fieldId": "adjusted temp",
                    "dataSourceId": "PredixTimeSeries"
                }
            ]
        }
    ]
}

Sample: Analytic Requiring a Time Series Table, Input Models, and Constants

Scenario: An analytic requires a time series table and two constants, where the time series input table has three fixed columns; column1, column2, and column3. Sample hierarchical analytic templates and port-to-field maps to support this scenario follow.

The following is a sample JSON input.

{
  "inputData": {
 "data": {
        "timeseries": {
            "time_stamp": [
                000003242,
                000004242,
                000005242
            ],
            "column1": [
                10.0,
                20.0,
                30.0
            ],
            "column2": [
                1.0,
                2.0,
                3.0
            ],
            "column3": [
                0.1,
                0.2,
                0.3
            ]
        },
        "constants": {
            "constant1": 55,
            "constant2": 100
        }
    }

     },
  "inputModel": [
    {
      "modelPortName": "test-model-port-1",
      "isModelEmbedded": "true",
      "modelLib": "anVuay1tb2RlbC1iaW5hcnktMQ=="
    },
    {
      "modelPortName": "test-model-port-2",
      "isModelEmbedded": "true",
      "modelLib": "janVuay1tb2RlbC1iaW5hcnktMg=="
    }
  ]
}

{
    "data": {
        "timeseries": {
            "time_stamp": [
                000003242,
                000004242,
                000005242
            ],
            "column1": [
                10.0,
                20.0,
                30.0
            ],
            "column2": [
                1.0,
                2.0,
                3.0
            ],
            "column3": [
                0.1,
                0.2,
                0.3
            ]
        },
        "constants": {
            "constant1": 55,
            "constant2": 100
        }
    }
}

The following is a sample hierarchical analytic template.

{
    "analyticName": "analyticWith3ColumnsAnd2Constants",
    "analyticVersion": "V1.0",
    "inputPortDefinitions": [
        {
            "portType": "COMPOSITE",
            "portName": "data",
            "childrenPorts": [
                {
                    "portName": "timeseries",
                    "portType": "TIMESERIES_ARRAY",
                    "variable": false,
                    "columns": [
                        {
                            "portName": "column1",
                            "portType": "FIELD",
                            "variable": false,
                            "dataType": "DOUBLE_ARRAY",
                            "required": true
                        },
                        {
                            "portName": "column2",
                            "portType": "FIELD",
                            "variable": false,
                            "dataType": "DOUBLE_ARRAY",
                            "required": true
                        },
                        {
                            "portName": "column3",
                            "portType": "FIELD",
                            "variable": false,
                            "dataType": "DOUBLE_ARRAY",
                            "required": true
                        }
                    ]
                },
                {
                    "portType": "COMPOSITE",
                    "portName": "constants",
                    "childrenPorts": [
                        {
                            "portType": "FIELD",
                            "portName": "constant1",
                            "variable": false,
                            "dataSourceId": "PredixTimeSeries",
                            "required": true
                        },
                        {
                            "portType": "FIELD",
                            "portName": "constant2",
                            "variable": false,
                            "dataSourceId": "PredixTimeSeries",
                            "required": true
                        }
                    ]
                }
            ]
        }
    ],
,
"inputModelDefinitions": [
  {
    "modelPortName": "test-model-port-1"
  },
  {
    "modelPortName": "test-model-port-2"
  }
],

    "outputPortDefinitions" : [
        {
            "portType": "FIELD",
            "portName": "results",
            "variable": false,
            "dataSourceId": "PredixTimeSeries"
        }
    ]
}

The following is a sample port-to-field map.

{
    "analyticName": "timeseriesAnalyticSampleWith3ColumnsAnd2Constants",
    "analyticVersion": "V1.0",
    "orchestrationStepId": "adjust temp by ambient",
    "iterations": [
        {
            "inputMaps": [
                {
                    "valueSourceType": "DATA_CONNECTOR",
                    "fullyQualifiedPortName": "data.timeseries.column1",
                    "fieldId": "temperature sensor",
                    "queryCriteria": {"start": 000003242, "end": 000005242},
                    "dataSourceId": "PredixTimeSeries"
                },
                {
                    "valueSourceType": "DATA_CONNECTOR",
                    "fullyQualifiedPortName": "data.timeseries.column2",
                    "fieldId": "vibration sensor",
                    "queryCriteria": {"start": 000003242, "end": 000005242},
                    "dataSourceId": "PredixTimeSeries"
                },
                {
                    "valueSourceType": "DATA_CONNECTOR",
                    "fullyQualifiedPortName": "data.timeseries.column3",
                    "fieldId": "pressure sensor",
                    "queryCriteria": {"start": 000003242, "end": 000005242},
                    "dataSourceId": "PredixTimeSeries"
                },
                {
                    "valueSourceType": "CONSTANT",
                    "fullyQualifiedPortName": "data.constants.constant1",
                    "value": -55
                },
                {
                    "valueSourceType": "CONSTANT",
                    "fullyQualifiedPortName": "data.constants.constant2",
                    "value": 100
                }
            ],
"inputModelMaps": [
  {
    "modelPortName": "test-model-port-1",
    "modelName": "test-model-1",
    "modelVersion": "v1"
  },
  {
    "modelPortName": "test-model-port-2",
    "modelName": "test-model-2",
    "modelVersion": "v1"
  }
],
            "outputMaps": [
                {
                    "fullyQualifiedPortName": "result",
                    "fieldId": "xxxField",
                    "dataSourceId": "PredixTimeSeries"
                }
            ]
        }
    ]
}

Sample: Analytic Requiring a Time Series Table With Variable Time Stamps

Scenario: An analytic requires a time series table and two constants, where the time series input table has three fixed columns; column1, column2, and column3 where time stamps are different. Sample hierarchical analytic templates and port-to-field maps to support this scenario follow.

The following is a sample JSON input.

{
  "time_series_temperature": {
    "temperature": [
      100
    ],
    "time_stamp": [
      "1455733669601"
    ]
  },
  "time_series_pressure": {
    "pressure": [
      57
    ],
    "time_stamp": [
      "1455733669605"
    ]
  },
  "time_series_vibration": {
    "vibration": [
      14
    ],
    "time_stamp": [
      "1455733669609"
    ]
  }
}

The following is a sample hierarchical analytic template.

{
  "analyticName": "analytic-name",
  "analyticVersion": "1.0",
  "inputPortDefinitions": [
    {
      "portName": "time_series_temperature",
      "portType": "TIMESERIES_ARRAY",
      "required": true,
      "variable": false,
      "columns": [
        {
          "portName": "temperature",
          "portType": "FIELD",
          "variable": false,
          "dataType": "DOUBLE_ARRAY",
          "required": true
        }
      ]
    },
    {
      "portName": "time_series_pressure",
      "portType": "TIMESERIES_ARRAY",
      "required": true,
      "variable": false,
      "columns": [
        {
          "portName": "pressure",
          "portType": "FIELD",
          "variable": false,
          "dataType": "DOUBLE_ARRAY",
          "required": true
        }
      ]
    },
    {
      "portName": "time_series_vibration",
      "portType": "TIMESERIES_ARRAY",
      "required": true,
      "variable": false,
      "columns": [
        {
          "portName": "vibration",
          "portType": "FIELD",
          "variable": false,
          "dataType": "DOUBLE_ARRAY",
          "required": true
        }
      ]
    }
  ]
}

The following is a sample port-to-field map.

{
  "analyticName": "analytic-name",
  "analyticVersion": "1.0",
  "orchestrationStepId": "sid-10001",
  "iterations": [
    {
      "inputMaps": [
        {
          "valueSourceType": "DATA_CONNECTOR",
          "fullyQualifiedPortName": "time_series_temperature.temperature",
          "fieldId": "field-7071",
          "queryCriteria": {
            "start": 1455733669601,
            "end": 1455733669610
          },
          "engUnit": "Celsius",
          "required": true,
          "dataSourceId": "Predix Time Series"
        },
        {
          "valueSourceType": "DATA_CONNECTOR",
          "fullyQualifiedPortName": "time_series_pressure.pressure",
          "fieldId": "field-2934",
          "queryCriteria": {
            "start": 1455733669601,
            "end": 1455733669610
          },
          "engUnit": "Celsius",
          "required": true,
          "dataSourceId": "Predix Time Series"
        },
        {
          "valueSourceType": "DATA_CONNECTOR",
          "fullyQualifiedPortName": "time_series_vibration.vibration",
          "fieldId": "field-9342",
          "queryCriteria": {
            "start": 1455733669601,
            "end": 1455733669610
          },
          "engUnit": "Celsius",
          "required": true,
          "dataSourceId": "Predix Time Series"
        }
      ],
      "outputMaps": [
        {
          "fullyQualifiedPortName": "data.result",
          "fieldId": "field-7432",
          "engUnit": "Celsius",
          "dataSourceId": "Predix Time Series"
        }
      ]
    }
  ]
}

Sample: Array of Arrays Model

This example shows how you can create an hierarchical analytic template that represents JSON input data as an array of arrays when starting with input data similar to the following:

"Target_data": [
    [-1.3902596851290876, -6.765816812041471, -4.76418388526832, -1.2112080900902589, -1.7569610822677713], 
    [9.7817179641162, -1.145749275218344, 6.533089409154036, 6.549142463394793, -5.168794582523],
    [-0.44253227413493945, -6.43586397529881, -3.291613230961093, 1.2406735399676179, -1.4659275851421456],
    [0.01589916825431556, -4.695552518057381, -5.284477375577028, -1.9252859877753656, -2.865932498974862]....

Start by marking the analytic input port as a variable port in the analytic template.

{
  "analyticName": "analytic-with-variable-input-port",
  "analyticVersion": "1.0",
  "inputPortDefinitions": [
    {
      "portType": "FIELD",
      "portName": "Target_data",
      "variable": true,
      "dataType": "DOUBLE_ARRAY",
      "required": true
    }
  ],
  "outputPortDefinitions": [
    {
      "portName": "output",
      "portType": "FIELD",
      "variable": false,
      "dataType": "DOUBLE_ARRAY",
      "required": true
    }
  ]
}

The following is a sample port-to-field map.

{
  "analyticName": "analytic-with-variable-input-port",
  "analyticVersion": "1.0",
  "orchestrationStepId": "sid-10001",
  "iterations": [
    {
      "inputMaps": [
        {
          "valueSourceType": "DATA_CONNECTOR",
          "fullyQualifiedPortName": "Target_data.0",
          "fieldId": "KW",
          "queryCriteria": {
            "start": 1455733669605,
            "end": 1455733669610
          },
          "engUnit": "kw",
          "required": true,
          "dataSourceId": "Predix Time Series"
        },
        {
          "valueSourceType": "DATA_CONNECTOR",
          "fullyQualifiedPortName": "Target_data.1",
          "fieldId": "vibration",
          "queryCriteria": {
            "start": 1455733669605,
            "end": 1455733669610
          },
          "engUnit": "hertz",
          "required": true,
          "dataSourceId": "Predix Time Series"
        }
      ],
      "outputMaps": [
        {
          "fullyQualifiedPortName": "output",
          "fieldId": "bearing temperature final",
          "engUnit": "Celsius",
          "dataSourceId": "Predix Time Series"
        }
      ]
    }
  ]
}

When the orchestration is run, the data input will come from two time series tags. The data input will look as follows.

{
  "Target_data": [
    [
      5,
      6,
      7,
      8,
      9,
      10
    ],
    [
      500,
      600,
      700,
      800,
      900,
      1000
    ]
  ]
}

You can add more data array to the top level array by defining Target_data.2, Target_data.3, and so on.

Sample: Multiple Iterations of Same Analytic

This example shows how you can run multiple iterations of the same analytic in an orchestration execution. The following is a sample port-to-field map.

{
  "analyticName": "demo-timeseries-adder",
  "analyticVersion": "V1",
  "orchestrationStepId": "sid-10001",
  "iterations": [
    {
      "id": "0",
      "name": " First Iteration",
      "inputMaps": [
        {
          "valueSourceType": "DATA_CONNECTOR",
          "fullyQualifiedPortName": "data.time_series.numberArray1",
          "fieldId": "temperature sensor",
          "queryCriteria": {
            "start": 0,
            "end": -1
          },
          "dataSourceId": "PredixTimeSeries"
        },
        {
          "valueSourceType": "DATA_CONNECTOR",
          "fullyQualifiedPortName": "data.time_series.numberArray2",
          "fieldId": "vibration sensor",
          "queryCriteria": {
            "start": 0,
            "end": -1
          },
          "dataSourceId": "PredixTimeSeries"
        }
      ],
      "outputMaps": [
        {
          "fullyQualifiedPortName": "data.time_series.sum",
          "fieldId": "demo sum",
          "dataSourceId": "PredixTimeSeries"
        }
      ]
    },
    {
      "id": "1",
      "name": " Second Iteration",
      "inputMaps": [
        {
          "valueSourceType": "DATA_CONNECTOR",
          "fullyQualifiedPortName": "data.time_series.numberArray1",
          "fieldId": "temperature sensor",
          "queryCriteria": {
            "start": 0,
            "end": -1
          },
          "dataSourceId": "PredixTimeSeries"
        },
        {
          "valueSourceType": "DATA_CONNECTOR",
          "fullyQualifiedPortName": "data.time_series.numberArray2",
          "fieldId": "vibration sensor",
          "queryCriteria": {
            "start": 0,
            "end": -1
          },
          "dataSourceId": "PredixTimeSeries"
        }
      ],
      "outputMaps": [
        {
          "fullyQualifiedPortName": "data.time_series.sum",
          "fieldId": "demo sum",
          "dataSourceId": "PredixTimeSeries"
        }
      ]
    }
  ]
}

Sample: Analytic Requiring Predix Time Series Data Using an Array of Tags

This example shows how you can run an analytic that uses Predix Time Series data that is an array of tags. For steps to run the orchestration request, see afs-orchestration-execution.html#task_9d270d7f-4d4a-4e07-9239-f121d17f1c8d.

The following is a sample hierarchical analytic template.

{
  "analyticName": "java-timeseries-demo-adder",
  "analyticVersion": "1.0",
  "inputPortDefinitions": [
    {
      "portType": "COMPOSITE",
      "portName": "data",
      "variable": false,
      "childrenPorts": [
        {
          "portName": "time_series_1",
          "portType": "TIMESERIES_ARRAY",
          "required": true,
          "variable": false,
          "columns": [
            {
              "portName": "doubleArray",
              "portType": "FIELD",
              "variable": true,
              "dataType": "DOUBLE_ARRAY",
              "required": true
            }
          ]
        },
        {
          "portName": "time_series_2",
          "portType": "TIMESERIES_ARRAY",
          "required": true,
          "variable": false,
          "columns": [
            {
              "portName": "integerArray",
              "portType": "FIELD",
              "variable": true,
              "dataType": "INTEGER_ARRAY",
              "required": true
            }
          ]
        }
      ]
    }
  ],
  "outputPortDefinitions": [
    {
      "portName": "data",
      "portType": "COMPOSITE",
      "required": true,
      "variable": false,
      "childrenPorts": [
        {
          "portName": "time_series",
          "portType": "TIMESERIES_ARRAY",
          "required": true,
          "variable": false,
          "columns": [
            {
              "portName": "sum",
              "portType": "FIELD",
              "variable": false,
              "dataType": "DOUBLE_ARRAY",
              "required": true
            }
          ]
        }
      ]
    }
  ]
}

The following is a sample port-to-field map.

{
  "analyticName": "4-input-timeseries-adder",
  "analyticVersion": "1.0",
  "orchestrationStepId": "sid-10001",
  "iterations": [
    {
      "inputMaps": [
        {
          "valueSourceType": "DATA_CONNECTOR",
          "fullyQualifiedPortName": "data.time_series_1.doubleArray",
          "fieldId": "doubleArrayFieldId",
          "queryCriteria": {
            "start": 1455733669601,
            "end": 1455733669603
          },
          "engUnit": "hertz",
          "required": true,
          "variable": true,
          "dataSourceId": "Predix Time Series"
        },
        {
          "valueSourceType": "DATA_CONNECTOR",
          "fullyQualifiedPortName": "data.time_series_2.integerArray",
          "fieldId": "integerArrayFieldId",
          "queryCriteria": {
            "start": 1455733669901,
            "end": 1455733669904
          },
          "engUnit": "kw",
          "required": true,
          "variable": true,
          "dataSourceId": "Predix Time Series"
        }
      ],
      "outputMaps": [
        {
          "fullyQualifiedPortName": "data.time_series.sum",
          "fieldId": "bearing temperature final",
          "engUnit": "Celsius",
          "dataSourceId": "Predix Time Series"
        }
      ]
    }
  ]
}

The array of tags is passed in through the orchestrationConfigurationID definition as shown in the following example.

{
  "orchestrationConfigurationId": "3a5cd1da-5b6e-47fd-b74d-9e75e959810f",
  "assetDataFieldsMap": {
    "doubleArrayFieldId": [
      "timerseries_tag1",
      "timerseries_tag2",
      "timerseries_tag3"
    ],
    "integerArrayFieldId": [
      "timerseries_tag4",
      "timerseries_tag5"
    ]
  },
  "dataSource": []
}

The following shows a sample of generated input with data.

{
  "inputData": {
    "data": {
      "time_series_1": {
        "time_stamp": [
          1455733669601,
          1455733669602,
          1455733669603
        ],
        "doubleArray": [
          [
            500,
            600,
            700
          ],
          [
            5000,
            6000,
            7000
          ],
          [
            50000,
            60000,
            70000
          ]
        ]
      },
      "time_series_2": {
        "time_stamp": [
          1455733669901,
          1455733669902,
          1455733669903,
          1455733669904
        ],
        "integerArray": [
          [
            105,
            205,
            305,
            405
          ],
          [
            15,
            25,
            35,
            45
          ]
        ]
      }
    }
  }
}

Sample: Analytic Requiring Predix Time Series Data Using Variable Input Ports

This example shows how you can run an analytic that uses data having separate timestamps for each indexed block of generated data from Predix Time Series (variable input port).

The following is a sample hierarchical template format with variable ports.

{
  "analyticName": "sample-hirerachical-analytic-template-with-two-variable-ports",
  "analyticVersion": "1.0",
  "inputPortDefinitions": [
    {
      "portType": "COMPOSITE",
      "portName": "data",
      "variable": false,
      "childrenPorts": [
        {
          "portName": "time_series_1",
          "portType": "TIMESERIES_ARRAY",
          "required": true,
          "variable": false,
          "columns": [
            {
              "portName": "doubleArray",
              "portType": "FIELD",
              "variable": true,
              "timestampShared": false,
              "dataType": "DOUBLE_ARRAY",
              "required": true
            }
          ]
        },
        {
          "portName": "time_series_2",
          "portType": "TIMESERIES_ARRAY",
          "required": true,
          "variable": false,
          "columns": [
            {
              "portName": "integerArray",
              "portType": "FIELD",
              "variable": true,
              "timestampShared": false,
              "dataType": "INTEGER_ARRAY",
              "required": true
            }
          ]
        }
      ]
    }
  ],
  "outputPortDefinitions": [
    {
      "portName": "data",
      "portType": "COMPOSITE",
      "required": true,
      "variable": false,
      "childrenPorts": [
        {
          "portName": "time_series",
          "portType": "TIMESERIES_ARRAY",
          "required": true,
          "variable": false,
          "columns": [
            {
              "portName": "sum",
              "portType": "FIELD",
              "variable": false,
              "dataType": "DOUBLE_ARRAY",
              "required": true
            }
          ]
        }
      ]
    }
  ]
}

The following is a sample port-to-field map showing that separate timestamps for each block of data is needed.

{
  "analyticName": "sample-port-to-field-map-with-two-variable-ports",
  "analyticVersion": "1.0",
  "orchestrationStepId": "sid-10001",
  "iterations": [
    {
      "inputMaps": [
        {
          "valueSourceType": "DATA_CONNECTOR",
          "fullyQualifiedPortName": "data.time_series_1.doubleArray.0",
          "fieldId": "vibration1",
          "queryCriteria": {
            "start": 1455733669601,
            "end": 1455733669603
          },
          "engUnit": "hertz",
          "required": true,
          "dataSourceId": "Predix Time Series"
        },
        {
          "valueSourceType": "DATA_CONNECTOR",
          "fullyQualifiedPortName": "data.time_series_1.doubleArray.1",
          "fieldId": "vibration2",
          "queryCriteria": {
            "start": 1455733669701,
            "end": 1455733669705
          },
          "engUnit": "hertz",
          "required": true,
          "dataSourceId": "Predix Time Series"
        },
        {
          "valueSourceType": "DATA_CONNECTOR",
          "fullyQualifiedPortName": "data.time_series_1.doubleArray.2",
          "fieldId": "vibration3",
          "queryCriteria": {
            "start": 1455733669801,
            "end": 1455733669802
          },
          "engUnit": "hertz",
          "required": true,
          "dataSourceId": "Predix Time Series"
        },
        {
          "valueSourceType": "DATA_CONNECTOR",
          "fullyQualifiedPortName": "data.time_series_2.integerArray.0",
          "fieldId": "kw1",
          "queryCriteria": {
            "start": 1455733669601,
            "end": 1455733669603
          },
          "engUnit": "kw",
          "required": true,
          "dataSourceId": "Predix Time Series"
        },
        {
          "valueSourceType": "DATA_CONNECTOR",
          "fullyQualifiedPortName": "data.time_series_2.integerArray.1",
          "fieldId": "kw2",
          "queryCriteria": {
            "start": 1455733669701,
            "end": 1455733669705
          },
          "engUnit": "kw",
          "required": true,
          "dataSourceId": "Predix Time Series"
        }
      ],
      "outputMaps": [
        {
          "fullyQualifiedPortName": "data.time_series.sum",
          "fieldId": "bearing temperature final",
          "engUnit": "Celsius",
          "dataSourceId": "Predix Time Series"
        }
      ]
    }
  ]
}

The following is sample generated analytic input for a hierarchical template showing separate timestamps side-by-side with values.

{
  "data": {
    "time_series_1": {
      "doubleArray": [
        {
          "time_stamp": [
            1455733669601,
            1455733669602,
            1455733669603
          ],
          "values": [
            500,
            600,
            700
          ]
        },
        {
          "time_stamp": [
            1455733669701,
            1455733669702,
            1455733669703,
            1455733669704,
            1455733669705
          ],
          "values": [
            50,
            60,
            70,
            80,
            90
          ]
        },
        {
          "time_stamp": [
            1455733669801,
            1455733669802
          ],
          "values": [
            1000,
            2000
          ]
        }
      ]
    },
    "time_series_2": {
      "integerArray": [
        {
          "time_stamp": [
            1455733669101,
            1455733669102,
            1455733669103,
            1455733669104
          ],
          "values": [
            105,
            205,
            305,
            405
          ]
        },
        {
          "time_stamp": [
            1455733669101,
            1455733669202,
            1455733669203,
            1455733669204,
            1455733669205,
            1455733669206
          ],
          "values": [
            15,
            25,
            35,
            45,
            55,
            65
          ]
        }
      ]
    }
  }
}

When creating the orchestration execution request payload, pass the array type of data in assetDataFieldsMap for the fieldId. For example,

{
  "orchestrationConfigurationId": "3a5cd1da-5b6e-47fd-b74d-9e75e959810f",
  "assetDataFieldsMap": {
    "doubleArrayFieldId": [
      "timerseries_tag1",
      "timerseries_tag2",
      "timerseries_tag3"
    ],
    "integerArrayFieldId": [
      "timerseries_tag4",
      "timerseries_tag5"
    ]
  },
  "dataSource": []
}

The following is sample generate input for two timeseries variable ports.

{
  "inputData": {
    "data": {
      "time_series_1": {
        "time_stamp": [
          1455733669601,
          1455733669602,
          1455733669603
        ],
        "doubleArray": [
          [
            500,
            600,
            700
          ],
          [
            5000,
            6000,
            7000,
            8000,
            9000
          ],
          [
            50000,
            60000
          ]
        ]
      },
      "time_series_2": {
        "time_stamp": [
          1455733669101,
          1455733669102,
          1455733669103,
          1455733669104
        ],
        "integerArray": [
          [
            105,
            205,
            305,
            405
          ],
          [
            15,
            25,
            35,
            45,
            55,
            65
          ]
        ]
      }
    }
  }
}

Sample: Reusing a Port-to-Field Map When Providing Data Is Optional

Scenario: You may want to use the same port-to-field map for multiple deployments but need to vary which ports will have data mapped for certain iterations. In the InputMaps definition, you can set the required field to false to indicate that data is optional. When this port is marked optional (required: false) and data is not provided during execution, it will be ignored and execution will not be failed for missing data. The following examples show how you can achieve this.

The following is a sample hierarchical analytic template

{
  "analyticName": "java-timeseries-demo-adder",
  "analyticVersion": "1.0",
  "inputPortDefinitions": [
    {
      "portType": "COMPOSITE",
      "portName": "data",
      "variable": false,
      "childrenPorts": [
        {
          "portName": "time_series",
          "portType": "TIMESERIES_ARRAY",
          "required": true,
          "variable": false,
          "columns": [
            {
              "portName": "numberArray1",
              "portType": "FIELD",
              "variable": false,
              "dataType": "DOUBLE_ARRAY",
              "required": true
            },
            {
              "portName": "numberArray2",
              "portType": "FIELD",
              "variable": false,
              "dataType": "DOUBLE_ARRAY",
              "required": true
            },
            {
              "portName": "numberArray3",
              "portType": "FIELD",
              "variable": false,
              "dataType": "DOUBLE_ARRAY",
              "required": false
            }
          ]
        }
      ]
    }
  ],
  "outputPortDefinitions": [
    {
      "portName": "data",
      "portType": "COMPOSITE",
      "required": true,
      "variable": false,
      "childrenPorts": [
        {
          "portName": "time_series",
          "portType": "TIMESERIES_ARRAY",
          "required": true,
          "variable": false,
          "columns": [
            {
              "portName": "sum",
              "portType": "FIELD",
              "variable": false,
              "dataType": "DOUBLE_ARRAY",
              "required": true
            },
            {
              "portName": "diff",
              "portType": "FIELD",
              "variable": false,
              "dataType": "DOUBLE_ARRAY",
              "required": false
            }
          ]
        }
      ]
    }
  ]
}

The following is a sample descriptive analytic template.

{
  "name": "TimeseriesAdder",
  "description": "",
  "packageName": "",
  "codeLanguage": "JAVA",
  "version": "1.0.0",
  "author": "Predix Analytics team",
  "inputDataPortDefinitions": [
    {
      "name": "time_series",
      "description": "",
      "dataType": "TimeSeries",
      "valueRequired": true,
      "variable": true,
      "arrayDimensions": 0
    },
    {
      "timeSeriesName": "time_series",
      "name": "numberArray1",
      "description": "",
      "dataType": "Double",
      "valueRequired": true,
      "variable": true,
      "validators": [
        "NotNull()",
        "IsNumber()"
      ],
      "arrayDimensions": 1
    },
    {
      "timeSeriesName": "time_series",
      "name": "numberArray2",
      "description": "",
      "dataType": "Double",
      "valueRequired": true,
      "variable": true,
      "validators": [
        "NotNull()",
        "IsNumber()"
      ],
      "arrayDimensions": 1
    },
    {
      "timeSeriesName": "time_series",
      "name": "numberArray3",
      "description": "",
      "dataType": "Double",
      "valueRequired": false,
      "variable": true,
      "validators": [
        "NotNull()",
        "IsNumber()"
      ],
      "arrayDimensions": 1
    }
  ],
  "constantDataPortDefinitions": [
    {
      "timeSeriesName": "time_series",
      "name": "sum",
      "description": "",
      "dataType": "Custom",
      "valueRequired": true,
      "variable": true,
      "arrayDimensions": 1
    },
    {
      "timeSeriesName": "time_series",
      "name": "diff",
      "description": "",
      "dataType": "Boolean",
      "valueRequired": false,
      "variable": true,
      "validators": [
        "NotNull()",
        "IsNumber()"
      ],
      "arrayDimensions": 1
    }
  ]
}

The following is a sample port-to-field map.

{
  "analyticName": "4-input-timeseries-adder",
  "analyticVersion": "1.0",
  "orchestrationStepId": "sid-10001",
  "iterations": [
    {
      "inputMaps": [
        {
          "valueSourceType": "DATA_CONNECTOR",
          "fullyQualifiedPortName": "data.time_series.numberArray1",
          "fieldId": "KW",
          "queryCriteria": {
            "start": 1455733669605,
            "end": 1455733669610
          },
          "engUnit": "kw",
          "required": true,
          "dataSourceId": "Predix Time Series"
        },
        {
          "valueSourceType": "DATA_CONNECTOR",
          "fullyQualifiedPortName": "data.time_series.numberArray2",
          "fieldId": "vibration",
          "queryCriteria": {
            "start": 1455733669605,
            "end": 1455733669610
          },
          "engUnit": "hertz",
          "required": true,
          "dataSourceId": "Predix Time Series"
        },
        {
          "valueSourceType": "DATA_CONNECTOR",
          "fullyQualifiedPortName": "data.time_series.numberArray3",
          "fieldId": "vibration",
          "queryCriteria": {
            "start": 1455733669605,
            "end": 1455733669610
          },
          "engUnit": "hertz",
          "required": false,
          "dataSourceId": "Predix Time Series"
        }
      ],
      "outputMaps": [
        {
          "fullyQualifiedPortName": "data.time_series.sum",
          "fieldId": "bearing temperature final",
          "engUnit": "Celsius",
          "required": true,
          "dataSourceId": "Predix Time Series"
        },
        {
          "fullyQualifiedPortName": "data.time_series.diff",
          "fieldId": "windspeed final",
          "engUnit": "km",
          "required": false,
          "dataSourceId": "Predix Time Series"
        }
      ]
    }
  ]
}

Sample generated input with data for optional (required: false) field.

{
    "data": {
      "time_series": {
        "time_stamp": [
          1455733669605,
          1455733669606,
          1455733669607,
          1455733669608,
          1455733669609,
          1455733669610
        ],
        "numberArray1": [
          5.0,
          6.0,
          7.0,
          8.0,
          9.0,
          10.0
        ],
        "numberArray2": [
          500.0,
          600.0,
          700.0,
          800.0,
          900.0,
          1000.0
        ],
       "numberArray3": [
          50.0,
          60.0,
          70.0,
          80.0,
          90.0,
          100.0
        ]
      }
    }
}

Sample generated input without data for optional (required: false) field.

{
    "data": {
      "time_series": {
        "time_stamp": [
          1455733669605,
          1455733669606,
          1455733669607,
          1455733669608,
          1455733669609,
          1455733669610
        ],
        "numberArray1": [
          5.0,
          6.0,
          7.0,
          8.0,
          9.0,
          10.0
        ],
        "numberArray2": [
          500.0,
          600.0,
          700.0,
          800.0,
          900.0,
          1000.0
        ]
      }
    }
}

The port-to-field map is itself a JSON structure. The JSON objects in this structure are summarized in the following tables.

• Type: PortToFieldMap
• Type: Iteration
• Type: PortMap
• Type: InputConstantMap
• Type: InputDataConnectorMap
• Type: OutputDataConnectorMap
• Type: ModelPortMap

Type: PortToFieldMap

This is the overall structure of a port-to-field map.

{
    "analyticName":<string>,
    "analyticVersion":<string>,
    "comment":<string>,
    "orchestrationStepId":<string>,
    "iterations":[<list of Iterations>]
}

See the following table for a description of the elements in a PortToFieldMap.

Type: Iteration

An iteration defines the set of input/output PortMaps for an execution of the analytic. Fill in the "inputMaps" array with the list of PortMaps that define how to get the input value and fill in the "outputMaps" with the list of OutputDataConnectorMaps that define where to write the output values.

This is the overall structure of an iteration object.

{
    "inputMaps":[<list of PortMaps>],
    "outputMaps":[<list of OutputDataConnectorMaps>],
    "inputModelMaps":[<list of ModelPortMaps>],
    "id":<string>,
    "name":<string>
}

See the following table for a description of the elements in an Iteration.

Type: PortMap

Maps an object in the JSON input structure to a constant value or a field in the data source.

{
    "valueSourceType":<string>,
    "fullyQualifiedPortName":<string>
}

See the following table for a description of the common elements in a PortMap.

Type: InputConstantMap

An InputConstantMap is a type of PortMap to be used when the data to be provided to the analytic can be stored directly within the port-to-field map itself. This is the overall structure of an input constant map.

{
    "valueSourceType":<string>,
    "fullyQualifiedPortName":<string>,
    "value":<string>
}

Extends from: PortMap.

In addition to the common elements of a PortMap, an InputConstantMap holds the value of a constant field:

Field: value

Contains the value to be passed to the constant field. This can be a numeric value (without quotes) or a string value (with quotes).

A constant input value can be parametrized and passed in at runtime using the customAttributes field of OrchestrationExecutionRequest. For example:

{
    "valueSourceType":CONSTANT,
    "fullyQualifiedPortName":"data.abc",
    "value":"${custom.CONSTANT_VALUE}"
}

Type: InputDataConnectorMap

An InputDataConnectorMap is a type of PortMap to be used when the data to be provided to the analytic needs to be retrieved from the data source at runtime by the Analytics Data Connector. This is the overall structure of an input data connector map.

{
    "valueSourceType":<string>,
    "fullyQualifiedPortName":<string>,
    "fieldId":<string>,
    "dataSourceId":<string>,
    "queryCriteria":<string>,
    "engUnit":<string>,
    "variable":<boolean>,
    "tagNameQuery":<string>
}

Extends from: PortMap.

In addition to the common elements of a PortMap, an InputDataConnectorMap contains the following entries.

Type: OutputDataConnectorMap

An OutputDataConnectorMap is used to identify the destination of the output data generated by the analytic at runtime. This is the overall structure of an output data connector map.

{
    "fullyQualifiedPortName":<string>,
    "fieldId":<string>,
    "dataSourceId":<string>,
    "tagNameQuery":<string>
}

An OutputDataConnectorMap contains the following entries.

Type: ModelPortMap

A ModelPortMap is used to retrieve the specified model from the Runtime service. This model is then passed to the analytic at runtime by the Analytics Data Connector. This is the overall structure of a model port map.

{
    "modelPortName": <string>,
    "modelName": <string>,
    "modelVersion": <string>
}

See the following table for a description of the elements in a ModelPortMap.

• Pre-Defined System Attributes
• Use Latest Data Point
• Use Parametrized Variables

Pre-Defined System Attributes

Use Latest Data Point

Retrieve the latest datapoint from Predix Time Series service (instead of specifying tag ids) by omitting the start and end time in the port-to-field map's queryCriteria field. The following example shows how.

{
  "analyticName": "timeseriesAnalyticSampleWith3ColumnsAnd2Constants",
  "analyticVersion": "V1.0",
  "orchestrationStepId": "adjust temp by ambient",
  "iterations": [
    {
      "inputMaps": [
        {
          "valueSourceType": "DATA_CONNECTOR",
          "fullyQualifiedPortName": "data.otherData.multipleTimeseries.0.MTSColumn1.0",
          "fieldId": "pressure sensor1",
          "queryCriteria": {
            "tags": [{
              "limit": 7,
              "order": "asc",
              "aggregations": [{
              ........
            }]
          },
          "required": true,
          "dataSourceId": "Predix Time Series"
        },
        {
          "valueSourceType": "DATA_CONNECTOR",
          "fullyQualifiedPortName": "data.otherData.multipleTimeseries.0.MTSColumn1.1",
          "fieldId": "pressure sensor1",
          "queryCriteria": {},
          "dataSourceId": "Predix Time Series"
        },
        ......

Use Parametrized Variables

You can use parametrized variables for resolving at orchestration runtime in the queryCriteria field. The following port-to-field map example shows how to do this in InputDataConnectorMap. The data must be consistent with Predix Time Series /datapoints request format.

{
  "analyticName": "4-input-timeseries-adder",
  "analyticVersion": "1.0",
  "orchestrationStepId": "sid-10001",
  "iterations": [
    {
      "inputMaps": [
        {
          "valueSourceType": "DATA_CONNECTOR",
          "fullyQualifiedPortName": "data.time_series.numberArray1",
          "fieldId": "KW",
          "queryCriteria": {
            "start": "${custom.KW.START_TIME}",
            "end": "${custom.KW.END_TIME}",
            "tags": [{
              "limit": 1000,
              "order": "desc",
              "aggregations": [{
                "type": "${.custom.AGGREGATION_TYPE}",
                "interval": "${custom.INTERVAL}"
              }],
              "groups": [{
                "name": "${ATTRIBUTE_1_KEY}",
                "attributes": [
                  "${context.ASSET_ID}_${context.FIELD_ID}_${custom.KW.ATTRIBUTE_1_VALUE_1}",
                  "${context.ASSET_ID}_${context.FIELD_ID}_${custom.KW.ATTRIBUTE_1_VALUE_2}"
                ]
              }]
            }]
          },
.......
        {
          "valueSourceType": "DATA_CONNECTOR",
          "fullyQualifiedPortName": "data.time_series.numberArray1",
          "fieldId": "vibration",
          "queryCriteria": {
            "start": "${custom.vibration.START_TIME}",
            "end": "${custom.vibration.END_TIME}",
            "tags": [{
              "limit": 1000,
              "order": "desc",
              "aggregations": [{
                "type": "${custom.AGGREGATION_TYPE}",
                "interval": "${custom.INTERVAL}"
              }],
              "groups": [{
                "name": "${ATTRIBUTE_1_KEY}",
                "attributes": [
                  "${context.ASSET_ID}_${context.FIELD_ID}_${custom.vibration.ATTRIBUTE_1_VALUE_1}",
                  "${context.ASSET_ID}_${context.FIELD_ID}_${custom.vibration.ATTRIBUTE_1_VALUE_2}"
                ]
              }]
            }]
          },

When defining the OutputDataConnectorMap, note the following restrictions.

• Output only supports key-value pairs of String datatype.
• You must represent the Predix Time Series Web Socket write API structure.
• messageId field is mandatory when writing to Time Series. Either provide hard coded value or a parametrized variable substituted at orchestration runtime.
• If queryCriteria field is not specified for the output port, a system generated messageId and default attribute "source"="orchestration" key-value pair is added when writing to time series

The following is a sample queryCriteria in OutputDataConnectorMap.

........
 "outputMaps": [
  {
    "fullyQualifiedPortName": "data.time_series.sum",
    "fieldId": "bearing temperature final",
    "engUnit": "Celsius",
    "dataSourceId": "Predix Time Series",
    "queryCriteria" :     {
      "messageId": "${custom.MESSAGE_ID}",
      "body": [
       {
         "attributes": {
          "${custom.ATTRIBUTE_1_KEY}" : "${custom.ASSET_ID}_${custom.FIELD_ID}_${custom.bearing temperature final.ATTRIBUTE_1_VALUE_1}",
          "${custom.ATTRIBUTE_2_KEY}" : "${context.ASSET_ID}_${context.FIELD_ID}_${custom.bearing temperature final.ATTRIBUTE_1_VALUE_2}"
 
          }
        }
      ]
    }
.......

Provide block dynamic parametrized variables in queryCriteria field in InputDataConnectorMap as follows.

{
  "analyticName": "4-input-timeseries-adder",
  "analyticVersion": "1.0",
  "orchestrationStepId": "sid-10001",
  "iterations": [
    {
      "inputMaps": [
        {
          "valueSourceType": "DATA_CONNECTOR",
          "fullyQualifiedPortName": "data.time_series.numberArray1",
          "fieldId": "KW",
          "queryCriteria": "${custom.QUERY_CRITERIA1}",
.......

Provide block dynamic parametrized variables in queryCriteria field in OutputDataConnectorMap as follows.

........
 "outputMaps": [
  {
    "fullyQualifiedPortName": "data.time_series.sum",
    "fieldId": "bearing temperature final",
    "engUnit": "Celsius",
    "dataSourceId": "Predix Time Series",
    "queryCriteria" :  "${custom.QUERY_CRITERIA2}"
.......

A Java-based reference implementation with PostgreSQL DB support is available. Use Reference Data Connector for PostgreSQL as a starting point to develop a Custom Data Connector service that uses a PostgreSQL data source.

• Data Format
• Type: DataRequest
• Type: DataResponse
• Type: Field
• Type: OrchestrationExecutionContext
• Type: ErrorResponse
• Sample AnalyticReadDataRequest
• Sample AnalyticReadDataResponse
• Sample AnalyticDataReadResponse with Error
• Sample AnalyticDataWriteRequest
• Sample AnalyticDataWriteResponse
• Sample AnalyticDataWriteResponse with Error

Data Format

This is the overall data format:

 [ [ <EpochInMs>, <Measurement Value>, <Quality> ] ] 

Where:

• <EpochInMs> — Epoch Time in milliseconds
• <Measurement Value> — Measurement data value
• <Quality> — Quality (Optional)

See the following table for the expected format for all the data types.

Payloads are expected as follows.

Type: DataRequest

This is the overall structure of a DataRequest object.

{
  "field": List<Field>,
  "customAttributes": Object,
  "systemAttributes": Object,
  "orchestrationExecutionContext": OrchestrationExecutionContext,
  "dataSourceId": String
}

See the following table for a description of the elements in a DataRequest.

Type: DataResponse

This is the overall structure of a DataResponse object.

{
  "field": List<Field>,
  "orchestrationExecutionContext": OrchestrationExecutionContext,
  "errorResponse": ErrorResponse,
  "dataSourceId": String
}

See the following table for a description of the elements in a DataResponse object.

Type: Field

This is the overall structure of a Field object.

{
  "fieldId": String,
  "fullyQualifiedPortName": String,
  "dataType": String,
  "engUnit": String,
  "data": Object,
  "queryCriteria": Object,
  "errorResponse": ErrorResponse
}

See the following table for a description of the elements in a Field object.

Type: OrchestrationExecutionContext

This is the overall structure of an OrchestrationExecutionContext object.

{
  "assetId": String,
  "orchestrationConfigurationID": String,
  "orchestrationExecutionRequestID": String,
  "analyticId": String,
  "analyticName": String,
  "analyticVersion": String,
  "analyticExecutionRequestID": String
}

See the following table for a description of the elements in an OrchestrationExecutionContext object.

Type: ErrorResponse

This is the overall structure of an ErrorResponse object.

{
  "code": String,
  "severity": String,
  "detail": String,
  "message": String
}

See the following table for a description of the elements in an ErrorResponse object.

Sample AnalyticReadDataRequest

{
    "field": [
        {
            "fieldId": "KW",
            "fullyQualifiedPortName": "data.time_series.numberArray1",
            "dataType": "DOUBLE_ARRAY",
            "engUnit": "kw",
            "data": [],
            "queryCriteria": {
                "columns": [
                    "recorded_at",
                    "data_value"
                ],
                "table": "sensor_data",
                "conditions": [
                    {
                        "key": "asset_id",
                        "value": "${ASSET_ID}",
                        "valueType": "string",
                        "relation": " = "
                    },
                    {
                        "key": "recorded_at",
                        "value": "current_timestamp",
                        "valueType": "none",
                        "relation": " < "
                    },
                    {
                        "key": "field_id",
                        "value": "KW",
                        "valueType": "string",
                        "relation": " = "
                    }
                ]
            },
            "errorResponse": null
        },
        {
            "fieldId": "vibration",
            "fullyQualifiedPortName": "data.time_series.numberArray2",
            "dataType": "DOUBLE_ARRAY",
            "engUnit": "hertz",
            "data": [],
            "queryCriteria": {
                "columns": [
                    "recorded_at",
                    "data_value"
                ],
                "table": "sensor_data",
                "conditions": [
                    {
                        "key": "asset_id",
                        "value": "${ASSET_ID}",
                        "valueType": "string",
                        "relation": " = "
                    },
                    {
                        "key": "recorded_at",
                        "value": "current_timestamp",
                        "valueType": "none",
                        "relation": " < "
                    },
                    {
                        "key": "field_id",
                        "value": "vibration",
                        "valueType": "string",
                        "relation": " = "
                    }
                ]
            },
            "errorResponse": null
        }
    ],
    "customAttributes": {
        "IS_GENERIC_SCHEMA": "TRUE"
    },
    "systemAttributes": null,
    "orchestrationExecutionContext": {
        "assetId": "/assets/32-90effe42-eb21-4611-b734-83f707d89d7a",
        "orchestrationConfigurationID": "e06982f5-f446-46d2-bc21-9fdf64c111ab",
        "orchestrationExecutionRequestID": "8f990980-b208-4418-99b1-f46d26cd4530",
        "analyticId": "b9e999a1-8931-459d-8d34-2f76c2b9fd95",
        "analyticName": null,
        "analyticVersion": null,
        "analyticExecutionRequestID": null
    },
    "dataSourceId": "Postgres Reference External Data Connector"
}

Sample AnalyticReadDataResponse

{
    "field": [
        {
            "fieldId": "KW",
            "fullyQualifiedPortName": "data.time_series.numberArray1",
            "dataType": "DOUBLE_ARRAY",
            "engUnit": "kw",
            "data": [
                [
                    1473366334967,
                    144.33
                ],
                [
                    1473366334968,
                    244.33
                ],
                [
                    1473366334969,
                    344.33
                ]
            ],
            "queryCriteria": {
                "columns": [
                    "recorded_at",
                    "data_value"
                ],
                "table": "sensor_data",
                "conditions": [
                    {
                        "key": "asset_id",
                        "value": "${ASSET_ID}",
                        "valueType": "string",
                        "relation": " = "
                    },
                    {
                        "key": "recorded_at",
                        "value": "current_timestamp",
                        "valueType": "none",
                        "relation": " < "
                    },
                    {
                        "key": "field_id",
                        "value": "KW",
                        "valueType": "string",
                        "relation": " = "
                    }
                ]
            }
        },
        {
            "fieldId": "vibration",
            "fullyQualifiedPortName": "data.time_series.numberArray2",
            "dataType": "DOUBLE_ARRAY",
            "engUnit": "hertz",
            "data": [
                [
                    1473366334967,
                    1244.33
                ],
                [
                    1473366334968,
                    2244.33
                ],
                [
                    1473366334969,
                    3244.33
                ]
            ],
            "queryCriteria": {
                "columns": [
                    "recorded_at",
                    "data_value"
                ],
                "table": "sensor_data",
                "conditions": [
                    {
                        "key": "asset_id",
                        "value": "${ASSET_ID}",
                        "valueType": "string",
                        "relation": " = "
                    },
                    {
                        "key": "recorded_at",
                        "value": "current_timestamp",
                        "valueType": "none",
                        "relation": " < "
                    },
                    {
                        "key": "field_id",
                        "value": "vibration",
                        "valueType": "string",
                        "relation": " = "
                    }
                ]
            }
        }
    ],
    "orchestrationExecutionContext": {
        "assetId": "/assets/32-90effe42-eb21-4611-b734-83f707d89d7a",
        "orchestrationConfigurationID": "e06982f5-f446-46d2-bc21-9fdf64c111ab",
        "orchestrationExecutionRequestID": "8f990980-b208-4418-99b1-f46d26cd4530",
        "analyticId": "b9e999a1-8931-459d-8d34-2f76c2b9fd95"
    }
}

Sample AnalyticDataReadResponse with Error

{
    "field": [
        {
            "fieldId": "KW",
            "fullyQualifiedPortName": "data.time_series.numberArray1",
            "dataType": "DOUBLE_ARRAY",
            "engUnit": "kw",
            "data": [],
            "queryCriteria": {
                "columns": [
                    "recorded_at",
                    "value"
                ],
                "table": "sensor_data",
                "conditions": [
                    {
                        "key": "asset_id",
                        "value": "${ASSET_ID}",
                        "valueType": "string",
                        "relation": " = "
                    },
                    {
                        "key": "recorded_at",
                        "value": "current_timestamp",
                        "valueType": "none",
                        "relation": " < "
                    },
                    {
                        "key": "field_id",
                        "value": "KW",
                        "valueType": "string",
                        "relation": " = "
                    }
                ]
            },
            "errorResponse": {
                "code": "FIELD_EXCEPTION",
                "message": "Unable to retrieve field KW. StatementCallback; bad SQL grammar [select recorded_at, value from sensor_data where  asset_id  =  '/assets/32-3c686c25-2f57-4f13-8cf0-04bc6bb26866' and  recorded_at  <  current_timestamp and  field_id  =  'KW']; nested exception is org.postgresql.util.PSQLException: ERROR: column \"value\" does not exist\n  Position: 21",
                "parameters": []
            }
        },
        {
            "fieldId": "vibration",
            "fullyQualifiedPortName": "data.time_series.numberArray2",
            "dataType": "DOUBLE_ARRAY",
            "engUnit": "hertz",
            "data": [
                [
                    1473366334967,
                    1244.33
                ],
                [
                    1473366334968,
                    2244.33
                ],
                [
                    1473366334969,
                    3244.33
                ]
            ],
            "queryCriteria": {
                "columns": [
                    "recorded_at",
                    "data_value"
                ],
                "table": "sensor_data",
                "conditions": [
                    {
                        "key": "asset_id",
                        "value": "${ASSET_ID}",
                        "valueType": "string",
                        "relation": " = "
                    },
                    {
                        "key": "recorded_at",
                        "value": "current_timestamp",
                        "valueType": "none",
                        "relation": " < "
                    },
                    {
                        "key": "field_id",
                        "value": "vibration",
                        "valueType": "string",
                        "relation": " = "
                    }
                ]
            }
        }
    ],
    "orchestrationExecutionContext": {
        "assetId": "/assets/32-3c686c25-2f57-4f13-8cf0-04bc6bb26866",
        "orchestrationConfigurationID": "6dfae6f5-8be1-4f90-b567-0970a34f2c53",
        "orchestrationExecutionRequestID": "accab63f-da95-4ac4-8120-fcfca109011d",
        "analyticId": "b9e999a1-8931-459d-8d34-2f76c2b9fd95"
    }
}

Sample AnalyticDataWriteRequest

{
    "field": [
        {
            "fieldId": "bearing temperature",
            "fullyQualifiedPortName": "data.time_series.sum",
            "dataType": "DOUBLE_ARRAY",
            "engUnit": "Celsius",
            "data": [
                [
                    1473366334967,
                    1388.6599999999999,
                    "2"
                ],
                [
                    1473366334968,
                    2488.66,
                    "2"
                ],
                [
                    1473366334969,
                    3588.66,
                    "2"
                ]
            ],
            "queryCriteria": {
                "columns": [
                    "field_id",
                    "recorded_at",
                    "data_value"
                ],
                "table": "sensor_data"
            },
            "errorResponse": null
        }
    ],
    "customAttributes": {
        "IS_GENERIC_SCHEMA": "TRUE"
    },
    "systemAttributes": null,
    "orchestrationExecutionContext": {
        "assetId": "/assets/32-90effe42-eb21-4611-b734-83f707d89d7a",
        "orchestrationConfigurationID": "e06982f5-f446-46d2-bc21-9fdf64c111ab",
        "orchestrationExecutionRequestID": "8f990980-b208-4418-99b1-f46d26cd4530",
        "analyticId": null,
        "analyticName": null,
        "analyticVersion": null,
        "analyticExecutionRequestID": null
    },
    "dataSourceId": "Postgres Reference External Data Connector"
}

Sample AnalyticDataWriteResponse

{
    "field": [
        {
            "fieldId": "bearing temperature",
            "fullyQualifiedPortName": "data.time_series.sum",
            "dataType": "DOUBLE_ARRAY",
            "engUnit": "Celsius",
            "data": [
                [
                    1473366334967,
                    1388.6599999999999,
                    "2"
                ],
                [
                    1473366334968,
                    2488.66,
                    "2"
                ],
                [
                    1473366334969,
                    3588.66,
                    "2"
                ]
            ],
            "queryCriteria": {
                "columns": [
                    "field_id",
                    "recorded_at",
                    "data_value"
                ],
                "table": "sensor_data"
            }
        }
    ],
    "orchestrationExecutionContext": {
        "assetId": "/assets/32-90effe42-eb21-4611-b734-83f707d89d7a",
        "orchestrationConfigurationID": "e06982f5-f446-46d2-bc21-9fdf64c111ab",
        "orchestrationExecutionRequestID": "8f990980-b208-4418-99b1-f46d26cd4530"
    }
}

Sample AnalyticDataWriteResponse with Error

  "field": [
    {
      "fieldId": "bearing temperature",
      "fullyQualifiedPortName": "data.time_series.sum",
      "dataType": "DOUBLE_ARRAY",
      "engUnit": "Celsius",
      "data": [
        [
          1473366334967,
          1388.6599999999999,
          "2"
        ],
        [
          1473366334968,
          2488.66,
          "2"
        ],
        [
          1473366334969,
          3588.66,
          "2"
        ]
      ],
      "queryCriteria": {
        "columns": [
          "field_id",
          "recorded_at",
          "value"
        ],
        "table": "sensor_data"
      },
      "errorResponse": {
        "code": "FIELD_EXCEPTION",
        "message": "Unable to update field bearing temperature. StatementCallback; bad SQL grammar [ insert into sensor_data(asset_id, field_id, recorded_at, value) values( '/assets/37-a57b74c1-28e1-44a5-b59f-7456411a7ab5', 'bearing temperature', to_timestamp(1473366334967::double precision/1000), 1388.6599999999999 );  insert into sensor_data(asset_id, field_id, recorded_at, value) values( '/assets/37-a57b74c1-28e1-44a5-b59f-7456411a7ab5', 'bearing temperature', to_timestamp(1473366334968::double precision/1000), 2488.66 );  insert into sensor_data(asset_id, field_id, recorded_at, value) values( '/assets/37-a57b74c1-28e1-44a5-b59f-7456411a7ab5', 'bearing temperature', to_timestamp(1473366334969::double precision/1000), 3588.66 )]; nested exception is java.sql.BatchUpdateException: Batch entry 0  insert into sensor_data(asset_id, field_id, recorded_at, value) values( '/assets/37-a57b74c1-28e1-44a5-b59f-7456411a7ab5', 'bearing temperature', to_timestamp(1473366334967::double precision/1000), 1388.6599999999999 ) was aborted.  Call getNextException to see the cause.",
        "parameters": []
      }
    }
  ],
  "orchestrationExecutionContext": {
    "assetId": "/assets/37-a57b74c1-28e1-44a5-b59f-7456411a7ab5",
    "orchestrationConfigurationID": "eecc3b4a-274a-4fba-804a-c2d47f85e9f8",
    "orchestrationExecutionRequestID": "a418c284-774a-4080-8c59-b2afd5d0cc27"
  }
}

The following table summarizes data conversion from Predix Time Series to analytic input.

The following table summarizes the data type conversion from analytic output from the analytic template’s input/output data types to Predix Time Series data types.

The following models are used when calling the Orchestration Configuration Service.

• Orchestration Configuration Entry Model
• Orchestration Artifact Model
• Orchestration Execution (v2) Model
• Asset Group Model
• Trained Model

Orchestration Configuration Entry Model

Orchestration Artifact Model

Orchestration Execution (v2) Model

Asset Group Model

Trained Model

For more information about these special characters, please see the CronTrigger Tutorial in Quartz documentation at http://www.quartz-scheduler.org/documentation/quartz-2.x/tutorials/crontrigger.

For more information about available values for Java time zoneid, please see Java documentation at http://docs.oracle.com/javase/7/docs/api/java/util/TimeZone.html.