@alma-cdk/cross-region-parameter

import {Request} from '../lib/request'; import {Response} from '../lib/response'; import {AWSError} from '../lib/error'; import {Service} from '../lib/service'; import {ServiceConfigurationOptions} from '../lib/service'; import {ConfigBase as Config} from '../lib/config-base'; interface Blob {} declare class LookoutEquipment extends Service { /** * Constructs a service object. This object has one method for each API operation. */ constructor(options?: LookoutEquipment.Types.ClientConfiguration) config: Config & LookoutEquipment.Types.ClientConfiguration; /** * Creates a container for a collection of data being ingested for analysis. The dataset contains the metadata describing where the data is and what the data actually looks like. In other words, it contains the location of the data source, the data schema, and other information. A dataset also contains any tags associated with the ingested data. */ createDataset(params: LookoutEquipment.Types.CreateDatasetRequest, callback?: (err: AWSError, data: LookoutEquipment.Types.CreateDatasetResponse) => void): Request<LookoutEquipment.Types.CreateDatasetResponse, AWSError>; /** * Creates a container for a collection of data being ingested for analysis. The dataset contains the metadata describing where the data is and what the data actually looks like. In other words, it contains the location of the data source, the data schema, and other information. A dataset also contains any tags associated with the ingested data. */ createDataset(callback?: (err: AWSError, data: LookoutEquipment.Types.CreateDatasetResponse) => void): Request<LookoutEquipment.Types.CreateDatasetResponse, AWSError>; /** * Creates a scheduled inference. Scheduling an inference is setting up a continuous real-time inference plan to analyze new measurement data. When setting up the schedule, you provide an S3 bucket location for the input data, assign it a delimiter between separate entries in the data, set an offset delay if desired, and set the frequency of inferencing. You must also provide an S3 bucket location for the output data. */ createInferenceScheduler(params: LookoutEquipment.Types.CreateInferenceSchedulerRequest, callback?: (err: AWSError, data: LookoutEquipment.Types.CreateInferenceSchedulerResponse) => void): Request<LookoutEquipment.Types.CreateInferenceSchedulerResponse, AWSError>; /** * Creates a scheduled inference. Scheduling an inference is setting up a continuous real-time inference plan to analyze new measurement data. When setting up the schedule, you provide an S3 bucket location for the input data, assign it a delimiter between separate entries in the data, set an offset delay if desired, and set the frequency of inferencing. You must also provide an S3 bucket location for the output data. */ createInferenceScheduler(callback?: (err: AWSError, data: LookoutEquipment.Types.CreateInferenceSchedulerResponse) => void): Request<LookoutEquipment.Types.CreateInferenceSchedulerResponse, AWSError>; /** * Creates a label for an event. */ createLabel(params: LookoutEquipment.Types.CreateLabelRequest, callback?: (err: AWSError, data: LookoutEquipment.Types.CreateLabelResponse) => void): Request<LookoutEquipment.Types.CreateLabelResponse, AWSError>; /** * Creates a label for an event. */ createLabel(callback?: (err: AWSError, data: LookoutEquipment.Types.CreateLabelResponse) => void): Request<LookoutEquipment.Types.CreateLabelResponse, AWSError>; /** * Creates a group of labels. */ createLabelGroup(params: LookoutEquipment.Types.CreateLabelGroupRequest, callback?: (err: AWSError, data: LookoutEquipment.Types.CreateLabelGroupResponse) => void): Request<LookoutEquipment.Types.CreateLabelGroupResponse, AWSError>; /** * Creates a group of labels. */ createLabelGroup(callback?: (err: AWSError, data: LookoutEquipment.Types.CreateLabelGroupResponse) => void): Request<LookoutEquipment.Types.CreateLabelGroupResponse, AWSError>; /** * Creates an ML model for data inference. A machine-learning (ML) model is a mathematical model that finds patterns in your data. In Amazon Lookout for Equipment, the model learns the patterns of normal behavior and detects abnormal behavior that could be potential equipment failure (or maintenance events). The models are made by analyzing normal data and abnormalities in machine behavior that have already occurred. Your model is trained using a portion of the data from your dataset and uses that data to learn patterns of normal behavior and abnormal patterns that lead to equipment failure. Another portion of the data is used to evaluate the model's accuracy. */ createModel(params: LookoutEquipment.Types.CreateModelRequest, callback?: (err: AWSError, data: LookoutEquipment.Types.CreateModelResponse) => void): Request<LookoutEquipment.Types.CreateModelResponse, AWSError>; /** * Creates an ML model for data inference. A machine-learning (ML) model is a mathematical model that finds patterns in your data. In Amazon Lookout for Equipment, the model learns the patterns of normal behavior and detects abnormal behavior that could be potential equipment failure (or maintenance events). The models are made by analyzing normal data and abnormalities in machine behavior that have already occurred. Your model is trained using a portion of the data from your dataset and uses that data to learn patterns of normal behavior and abnormal patterns that lead to equipment failure. Another portion of the data is used to evaluate the model's accuracy. */ createModel(callback?: (err: AWSError, data: LookoutEquipment.Types.CreateModelResponse) => void): Request<LookoutEquipment.Types.CreateModelResponse, AWSError>; /** * Deletes a dataset and associated artifacts. The operation will check to see if any inference scheduler or data ingestion job is currently using the dataset, and if there isn't, the dataset, its metadata, and any associated data stored in S3 will be deleted. This does not affect any models that used this dataset for training and evaluation, but does prevent it from being used in the future. */ deleteDataset(params: LookoutEquipment.Types.DeleteDatasetRequest, callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>; /** * Deletes a dataset and associated artifacts. The operation will check to see if any inference scheduler or data ingestion job is currently using the dataset, and if there isn't, the dataset, its metadata, and any associated data stored in S3 will be deleted. This does not affect any models that used this dataset for training and evaluation, but does prevent it from being used in the future. */ deleteDataset(callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>; /** * Deletes an inference scheduler that has been set up. Already processed output results are not affected. */ deleteInferenceScheduler(params: LookoutEquipment.Types.DeleteInferenceSchedulerRequest, callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>; /** * Deletes an inference scheduler that has been set up. Already processed output results are not affected. */ deleteInferenceScheduler(callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>; /** * Deletes a label. */ deleteLabel(params: LookoutEquipment.Types.DeleteLabelRequest, callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>; /** * Deletes a label. */ deleteLabel(callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>; /** * Deletes a group of labels. */ deleteLabelGroup(params: LookoutEquipment.Types.DeleteLabelGroupRequest, callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>; /** * Deletes a group of labels. */ deleteLabelGroup(callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>; /** * Deletes an ML model currently available for Amazon Lookout for Equipment. This will prevent it from being used with an inference scheduler, even one that is already set up. */ deleteModel(params: LookoutEquipment.Types.DeleteModelRequest, callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>; /** * Deletes an ML model currently available for Amazon Lookout for Equipment. This will prevent it from being used with an inference scheduler, even one that is already set up. */ deleteModel(callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>; /** * Provides information on a specific data ingestion job such as creation time, dataset ARN, and status. */ describeDataIngestionJob(params: LookoutEquipment.Types.DescribeDataIngestionJobRequest, callback?: (err: AWSError, data: LookoutEquipment.Types.DescribeDataIngestionJobResponse) => void): Request<LookoutEquipment.Types.DescribeDataIngestionJobResponse, AWSError>; /** * Provides information on a specific data ingestion job such as creation time, dataset ARN, and status. */ describeDataIngestionJob(callback?: (err: AWSError, data: LookoutEquipment.Types.DescribeDataIngestionJobResponse) => void): Request<LookoutEquipment.Types.DescribeDataIngestionJobResponse, AWSError>; /** * Provides a JSON description of the data in each time series dataset, including names, column names, and data types. */ describeDataset(params: LookoutEquipment.Types.DescribeDatasetRequest, callback?: (err: AWSError, data: LookoutEquipment.Types.DescribeDatasetResponse) => void): Request<LookoutEquipment.Types.DescribeDatasetResponse, AWSError>; /** * Provides a JSON description of the data in each time series dataset, including names, column names, and data types. */ describeDataset(callback?: (err: AWSError, data: LookoutEquipment.Types.DescribeDatasetResponse) => void): Request<LookoutEquipment.Types.DescribeDatasetResponse, AWSError>; /** * Specifies information about the inference scheduler being used, including name, model, status, and associated metadata */ describeInferenceScheduler(params: LookoutEquipment.Types.DescribeInferenceSchedulerRequest, callback?: (err: AWSError, data: LookoutEquipment.Types.DescribeInferenceSchedulerResponse) => void): Request<LookoutEquipment.Types.DescribeInferenceSchedulerResponse, AWSError>; /** * Specifies information about the inference scheduler being used, including name, model, status, and associated metadata */ describeInferenceScheduler(callback?: (err: AWSError, data: LookoutEquipment.Types.DescribeInferenceSchedulerResponse) => void): Request<LookoutEquipment.Types.DescribeInferenceSchedulerResponse, AWSError>; /** * Returns the name of the label. */ describeLabel(params: LookoutEquipment.Types.DescribeLabelRequest, callback?: (err: AWSError, data: LookoutEquipment.Types.DescribeLabelResponse) => void): Request<LookoutEquipment.Types.DescribeLabelResponse, AWSError>; /** * Returns the name of the label. */ describeLabel(callback?: (err: AWSError, data: LookoutEquipment.Types.DescribeLabelResponse) => void): Request<LookoutEquipment.Types.DescribeLabelResponse, AWSError>; /** * Returns information about the label group. */ describeLabelGroup(params: LookoutEquipment.Types.DescribeLabelGroupRequest, callback?: (err: AWSError, data: LookoutEquipment.Types.DescribeLabelGroupResponse) => void): Request<LookoutEquipment.Types.DescribeLabelGroupResponse, AWSError>; /** * Returns information about the label group. */ describeLabelGroup(callback?: (err: AWSError, data: LookoutEquipment.Types.DescribeLabelGroupResponse) => void): Request<LookoutEquipment.Types.DescribeLabelGroupResponse, AWSError>; /** * Provides a JSON containing the overall information about a specific ML model, including model name and ARN, dataset, training and evaluation information, status, and so on. */ describeModel(params: LookoutEquipment.Types.DescribeModelRequest, callback?: (err: AWSError, data: LookoutEquipment.Types.DescribeModelResponse) => void): Request<LookoutEquipment.Types.DescribeModelResponse, AWSError>; /** * Provides a JSON containing the overall information about a specific ML model, including model name and ARN, dataset, training and evaluation information, status, and so on. */ describeModel(callback?: (err: AWSError, data: LookoutEquipment.Types.DescribeModelResponse) => void): Request<LookoutEquipment.Types.DescribeModelResponse, AWSError>; /** * Provides a list of all data ingestion jobs, including dataset name and ARN, S3 location of the input data, status, and so on. */ listDataIngestionJobs(params: LookoutEquipment.Types.ListDataIngestionJobsRequest, callback?: (err: AWSError, data: LookoutEquipment.Types.ListDataIngestionJobsResponse) => void): Request<LookoutEquipment.Types.ListDataIngestionJobsResponse, AWSError>; /** * Provides a list of all data ingestion jobs, including dataset name and ARN, S3 location of the input data, status, and so on. */ listDataIngestionJobs(callback?: (err: AWSError, data: LookoutEquipment.Types.ListDataIngestionJobsResponse) => void): Request<LookoutEquipment.Types.ListDataIngestionJobsResponse, AWSError>; /** * Lists all datasets currently available in your account, filtering on the dataset name. */ listDatasets(params: LookoutEquipment.Types.ListDatasetsRequest, callback?: (err: AWSError, data: LookoutEquipment.Types.ListDatasetsResponse) => void): Request<LookoutEquipment.Types.ListDatasetsResponse, AWSError>; /** * Lists all datasets currently available in your account, filtering on the dataset name. */ listDatasets(callback?: (err: AWSError, data: LookoutEquipment.Types.ListDatasetsResponse) => void): Request<LookoutEquipment.Types.ListDatasetsResponse, AWSError>; /** * Lists all inference events that have been found for the specified inference scheduler. */ listInferenceEvents(params: LookoutEquipment.Types.ListInferenceEventsRequest, callback?: (err: AWSError, data: LookoutEquipment.Types.ListInferenceEventsResponse) => void): Request<LookoutEquipment.Types.ListInferenceEventsResponse, AWSError>; /** * Lists all inference events that have been found for the specified inference scheduler. */ listInferenceEvents(callback?: (err: AWSError, data: LookoutEquipment.Types.ListInferenceEventsResponse) => void): Request<LookoutEquipment.Types.ListInferenceEventsResponse, AWSError>; /** * Lists all inference executions that have been performed by the specified inference scheduler. */ listInferenceExecutions(params: LookoutEquipment.Types.ListInferenceExecutionsRequest, callback?: (err: AWSError, data: LookoutEquipment.Types.ListInferenceExecutionsResponse) => void): Request<LookoutEquipment.Types.ListInferenceExecutionsResponse, AWSError>; /** * Lists all inference executions that have been performed by the specified inference scheduler. */ listInferenceExecutions(callback?: (err: AWSError, data: LookoutEquipment.Types.ListInferenceExecutionsResponse) => void): Request<LookoutEquipment.Types.ListInferenceExecutionsResponse, AWSError>; /** * Retrieves a list of all inference schedulers currently available for your account. */ listInferenceSchedulers(params: LookoutEquipment.Types.ListInferenceSchedulersRequest, callback?: (err: AWSError, data: LookoutEquipment.Types.ListInferenceSchedulersResponse) => void): Request<LookoutEquipment.Types.ListInferenceSchedulersResponse, AWSError>; /** * Retrieves a list of all inference schedulers currently available for your account. */ listInferenceSchedulers(callback?: (err: AWSError, data: LookoutEquipment.Types.ListInferenceSchedulersResponse) => void): Request<LookoutEquipment.Types.ListInferenceSchedulersResponse, AWSError>; /** * Returns a list of the label groups. */ listLabelGroups(params: LookoutEquipment.Types.ListLabelGroupsRequest, callback?: (err: AWSError, data: LookoutEquipment.Types.ListLabelGroupsResponse) => void): Request<LookoutEquipment.Types.ListLabelGroupsResponse, AWSError>; /** * Returns a list of the label groups. */ listLabelGroups(callback?: (err: AWSError, data: LookoutEquipment.Types.ListLabelGroupsResponse) => void): Request<LookoutEquipment.Types.ListLabelGroupsResponse, AWSError>; /** * Provides a list of labels. */ listLabels(params: LookoutEquipment.Types.ListLabelsRequest, callback?: (err: AWSError, data: LookoutEquipment.Types.ListLabelsResponse) => void): Request<LookoutEquipment.Types.ListLabelsResponse, AWSError>; /** * Provides a list of labels. */ listLabels(callback?: (err: AWSError, data: LookoutEquipment.Types.ListLabelsResponse) => void): Request<LookoutEquipment.Types.ListLabelsResponse, AWSError>; /** * Generates a list of all models in the account, including model name and ARN, dataset, and status. */ listModels(params: LookoutEquipment.Types.ListModelsRequest, callback?: (err: AWSError, data: LookoutEquipment.Types.ListModelsResponse) => void): Request<LookoutEquipment.Types.ListModelsResponse, AWSError>; /** * Generates a list of all models in the account, including model name and ARN, dataset, and status. */ listModels(callback?: (err: AWSError, data: LookoutEquipment.Types.ListModelsResponse) => void): Request<LookoutEquipment.Types.ListModelsResponse, AWSError>; /** * Lists statistics about the data collected for each of the sensors that have been successfully ingested in the particular dataset. Can also be used to retreive Sensor Statistics for a previous ingestion job. */ listSensorStatistics(params: LookoutEquipment.Types.ListSensorStatisticsRequest, callback?: (err: AWSError, data: LookoutEquipment.Types.ListSensorStatisticsResponse) => void): Request<LookoutEquipment.Types.ListSensorStatisticsResponse, AWSError>; /** * Lists statistics about the data collected for each of the sensors that have been successfully ingested in the particular dataset. Can also be used to retreive Sensor Statistics for a previous ingestion job. */ listSensorStatistics(callback?: (err: AWSError, data: LookoutEquipment.Types.ListSensorStatisticsResponse) => void): Request<LookoutEquipment.Types.ListSensorStatisticsResponse, AWSError>; /** * Lists all the tags for a specified resource, including key and value. */ listTagsForResource(params: LookoutEquipment.Types.ListTagsForResourceRequest, callback?: (err: AWSError, data: LookoutEquipment.Types.ListTagsForResourceResponse) => void): Request<LookoutEquipment.Types.ListTagsForResourceResponse, AWSError>; /** * Lists all the tags for a specified resource, including key and value. */ listTagsForResource(callback?: (err: AWSError, data: LookoutEquipment.Types.ListTagsForResourceResponse) => void): Request<LookoutEquipment.Types.ListTagsForResourceResponse, AWSError>; /** * Starts a data ingestion job. Amazon Lookout for Equipment returns the job status. */ startDataIngestionJob(params: LookoutEquipment.Types.StartDataIngestionJobRequest, callback?: (err: AWSError, data: LookoutEquipment.Types.StartDataIngestionJobResponse) => void): Request<LookoutEquipment.Types.StartDataIngestionJobResponse, AWSError>; /** * Starts a data ingestion job. Amazon Lookout for Equipment returns the job status. */ startDataIngestionJob(callback?: (err: AWSError, data: LookoutEquipment.Types.StartDataIngestionJobResponse) => void): Request<LookoutEquipment.Types.StartDataIngestionJobResponse, AWSError>; /** * Starts an inference scheduler. */ startInferenceScheduler(params: LookoutEquipment.Types.StartInferenceSchedulerRequest, callback?: (err: AWSError, data: LookoutEquipment.Types.StartInferenceSchedulerResponse) => void): Request<LookoutEquipment.Types.StartInferenceSchedulerResponse, AWSError>; /** * Starts an inference scheduler. */ startInferenceScheduler(callback?: (err: AWSError, data: LookoutEquipment.Types.StartInferenceSchedulerResponse) => void): Request<LookoutEquipment.Types.StartInferenceSchedulerResponse, AWSError>; /** * Stops an inference scheduler. */ stopInferenceScheduler(params: LookoutEquipment.Types.StopInferenceSchedulerRequest, callback?: (err: AWSError, data: LookoutEquipment.Types.StopInferenceSchedulerResponse) => void): Request<LookoutEquipment.Types.StopInferenceSchedulerResponse, AWSError>; /** * Stops an inference scheduler. */ stopInferenceScheduler(callback?: (err: AWSError, data: LookoutEquipment.Types.StopInferenceSchedulerResponse) => void): Request<LookoutEquipment.Types.StopInferenceSchedulerResponse, AWSError>; /** * Associates a given tag to a resource in your account. A tag is a key-value pair which can be added to an Amazon Lookout for Equipment resource as metadata. Tags can be used for organizing your resources as well as helping you to search and filter by tag. Multiple tags can be added to a resource, either when you create it, or later. Up to 50 tags can be associated with each resource. */ tagResource(params: LookoutEquipment.Types.TagResourceRequest, callback?: (err: AWSError, data: LookoutEquipment.Types.TagResourceResponse) => void): Request<LookoutEquipment.Types.TagResourceResponse, AWSError>; /** * Associates a given tag to a resource in your account. A tag is a key-value pair which can be added to an Amazon Lookout for Equipment resource as metadata. Tags can be used for organizing your resources as well as helping you to search and filter by tag. Multiple tags can be added to a resource, either when you create it, or later. Up to 50 tags can be associated with each resource. */ tagResource(callback?: (err: AWSError, data: LookoutEquipment.Types.TagResourceResponse) => void): Request<LookoutEquipment.Types.TagResourceResponse, AWSError>; /** * Removes a specific tag from a given resource. The tag is specified by its key. */ untagResource(params: LookoutEquipment.Types.UntagResourceRequest, callback?: (err: AWSError, data: LookoutEquipment.Types.UntagResourceResponse) => void): Request<LookoutEquipment.Types.UntagResourceResponse, AWSError>; /** * Removes a specific tag from a given resource. The tag is specified by its key. */ untagResource(callback?: (err: AWSError, data: LookoutEquipment.Types.UntagResourceResponse) => void): Request<LookoutEquipment.Types.UntagResourceResponse, AWSError>; /** * Updates an inference scheduler. */ updateInferenceScheduler(params: LookoutEquipment.Types.UpdateInferenceSchedulerRequest, callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>; /** * Updates an inference scheduler. */ updateInferenceScheduler(callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>; /** * Updates the label group. */ updateLabelGroup(params: LookoutEquipment.Types.UpdateLabelGroupRequest, callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>; /** * Updates the label group. */ updateLabelGroup(callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>; } declare namespace LookoutEquipment { export type AmazonResourceArn = string; export type Boolean = boolean; export type BoundedLengthString = string; export interface CategoricalValues { /** * Indicates whether there is a potential data issue related to categorical values. */ Status: StatisticalIssueStatus; /** * Indicates the number of categories in the data. */ NumberOfCategory?: Integer; } export type Comments = string; export type ComponentName = string; export type ComponentTimestampDelimiter = string; export interface CountPercent { /** * Indicates the count of occurences of the given statistic. */ Count: Integer; /** * Indicates the percentage of occurances of the given statistic. */ Percentage: Float; } export interface CreateDatasetRequest { /** * The name of the dataset being created. */ DatasetName: DatasetName; /** * A JSON description of the data that is in each time series dataset, including names, column names, and data types. */ DatasetSchema?: DatasetSchema; /** * Provides the identifier of the KMS key used to encrypt dataset data by Amazon Lookout for Equipment. */ ServerSideKmsKeyId?: NameOrArn; /** * A unique identifier for the request. If you do not set the client request token, Amazon Lookout for Equipment generates one. */ ClientToken: IdempotenceToken; /** * Any tags associated with the ingested data described in the dataset. */ Tags?: TagList; } export interface CreateDatasetResponse { /** * The name of the dataset being created. */ DatasetName?: DatasetName; /** * The Amazon Resource Name (ARN) of the dataset being created. */ DatasetArn?: DatasetArn; /** * Indicates the status of the CreateDataset operation. */ Status?: DatasetStatus; } export interface CreateInferenceSchedulerRequest { /** * The name of the previously trained ML model being used to create the inference scheduler. */ ModelName: ModelName; /** * The name of the inference scheduler being created. */ InferenceSchedulerName: InferenceSchedulerName; /** * The interval (in minutes) of planned delay at the start of each inference segment. For example, if inference is set to run every ten minutes, the delay is set to five minutes and the time is 09:08. The inference scheduler will wake up at the configured interval (which, without a delay configured, would be 09:10) plus the additional five minute delay time (so 09:15) to check your Amazon S3 bucket. The delay provides a buffer for you to upload data at the same frequency, so that you don't have to stop and restart the scheduler when uploading new data. For more information, see Understanding the inference process. */ DataDelayOffsetInMinutes?: DataDelayOffsetInMinutes; /** * How often data is uploaded to the source Amazon S3 bucket for the input data. The value chosen is the length of time between data uploads. For instance, if you select 5 minutes, Amazon Lookout for Equipment will upload the real-time data to the source bucket once every 5 minutes. This frequency also determines how often Amazon Lookout for Equipment runs inference on your data. For more information, see Understanding the inference process. */ DataUploadFrequency: DataUploadFrequency; /** * Specifies configuration information for the input data for the inference scheduler, including delimiter, format, and dataset location. */ DataInputConfiguration: InferenceInputConfiguration; /** * Specifies configuration information for the output results for the inference scheduler, including the S3 location for the output. */ DataOutputConfiguration: InferenceOutputConfiguration; /** * The Amazon Resource Name (ARN) of a role with permission to access the data source being used for the inference. */ RoleArn: IamRoleArn; /** * Provides the identifier of the KMS key used to encrypt inference scheduler data by Amazon Lookout for Equipment. */ ServerSideKmsKeyId?: NameOrArn; /** * A unique identifier for the request. If you do not set the client request token, Amazon Lookout for Equipment generates one. */ ClientToken: IdempotenceToken; /** * Any tags associated with the inference scheduler. */ Tags?: TagList; } export interface CreateInferenceSchedulerResponse { /** * The Amazon Resource Name (ARN) of the inference scheduler being created. */ InferenceSchedulerArn?: InferenceSchedulerArn; /** * The name of inference scheduler being created. */ InferenceSchedulerName?: InferenceSchedulerName; /** * Indicates the status of the CreateInferenceScheduler operation. */ Status?: InferenceSchedulerStatus; } export interface CreateLabelGroupRequest { /** * Names a group of labels. Data in this field will be retained for service usage. Follow best practices for the security of your data. */ LabelGroupName: LabelGroupName; /** * The acceptable fault codes (indicating the type of anomaly associated with the label) that can be used with this label group. Data in this field will be retained for service usage. Follow best practices for the security of your data. */ FaultCodes?: FaultCodes; /** * A unique identifier for the request to create a label group. If you do not set the client request token, Lookout for Equipment generates one. */ ClientToken: IdempotenceToken; /** * Tags that provide metadata about the label group you are creating. Data in this field will be retained for service usage. Follow best practices for the security of your data. */ Tags?: TagList; } export interface CreateLabelGroupResponse { /** * The name of the label group that you have created. Data in this field will be retained for service usage. Follow best practices for the security of your data. */ LabelGroupName?: LabelGroupName; /** * The ARN of the label group that you have created. */ LabelGroupArn?: LabelGroupArn; } export interface CreateLabelRequest { /** * The name of a group of labels. Data in this field will be retained for service usage. Follow best practices for the security of your data. */ LabelGroupName: LabelGroupName; /** * The start time of the labeled event. */ StartTime: Timestamp; /** * The end time of the labeled event. */ EndTime: Timestamp; /** * Indicates whether a labeled event represents an anomaly. */ Rating: LabelRating; /** * Provides additional information about the label. The fault code must be defined in the FaultCodes attribute of the label group. Data in this field will be retained for service usage. Follow best practices for the security of your data. */ FaultCode?: FaultCode; /** * Metadata providing additional information about the label. Data in this field will be retained for service usage. Follow best practices for the security of your data. */ Notes?: Comments; /** * Indicates that a label pertains to a particular piece of equipment. Data in this field will be retained for service usage. Follow best practices for the security of your data. */ Equipment?: Equipment; /** * A unique identifier for the request to create a label. If you do not set the client request token, Lookout for Equipment generates one. */ ClientToken: IdempotenceToken; } export interface CreateLabelResponse { /** * The ID of the label that you have created. */ LabelId?: LabelId; } export interface CreateModelRequest { /** * The name for the ML model to be created. */ ModelName: ModelName; /** * The name of the dataset for the ML model being created. */ DatasetName: DatasetIdentifier; /** * The data schema for the ML model being created. */ DatasetSchema?: DatasetSchema; /** * The input configuration for the labels being used for the ML model that's being created. */ LabelsInputConfiguration?: LabelsInputConfiguration; /** * A unique identifier for the request. If you do not set the client request token, Amazon Lookout for Equipment generates one. */ ClientToken: IdempotenceToken; /** * Indicates the time reference in the dataset that should be used to begin the subset of training data for the ML model. */ TrainingDataStartTime?: Timestamp; /** * Indicates the time reference in the dataset that should be used to end the subset of training data for the ML model. */ TrainingDataEndTime?: Timestamp; /** * Indicates the time reference in the dataset that should be used to begin the subset of evaluation data for the ML model. */ EvaluationDataStartTime?: Timestamp; /** * Indicates the time reference in the dataset that should be used to end the subset of evaluation data for the ML model. */ EvaluationDataEndTime?: Timestamp; /** * The Amazon Resource Name (ARN) of a role with permission to access the data source being used to create the ML model. */ RoleArn?: IamRoleArn; /** * The configuration is the TargetSamplingRate, which is the sampling rate of the data after post processing by Amazon Lookout for Equipment. For example, if you provide data that has been collected at a 1 second level and you want the system to resample the data at a 1 minute rate before training, the TargetSamplingRate is 1 minute. When providing a value for the TargetSamplingRate, you must attach the prefix "PT" to the rate you want. The value for a 1 second rate is therefore PT1S, the value for a 15 minute rate is PT15M, and the value for a 1 hour rate is PT1H */ DataPreProcessingConfiguration?: DataPreProcessingConfiguration; /** * Provides the identifier of the KMS key used to encrypt model data by Amazon Lookout for Equipment. */ ServerSideKmsKeyId?: NameOrArn; /** * Any tags associated with the ML model being created. */ Tags?: TagList; /** * Indicates that the asset associated with this sensor has been shut off. As long as this condition is met, Lookout for Equipment will not use data from this asset for training, evaluation, or inference. */ OffCondition?: OffCondition; } export interface CreateModelResponse { /** * The Amazon Resource Name (ARN) of the model being created. */ ModelArn?: ModelArn; /** * Indicates the status of the CreateModel operation. */ Status?: ModelStatus; } export type DataDelayOffsetInMinutes = number; export type DataIngestionJobSummaries = DataIngestionJobSummary[]; export interface DataIngestionJobSummary { /** * Indicates the job ID of the data ingestion job. */ JobId?: IngestionJobId; /** * The name of the dataset used for the data ingestion job. */ DatasetName?: DatasetName; /** * The Amazon Resource Name (ARN) of the dataset used in the data ingestion job. */ DatasetArn?: DatasetArn; /** * Specifies information for the input data for the data inference job, including data Amazon S3 location parameters. */ IngestionInputConfiguration?: IngestionInputConfiguration; /** * Indicates the status of the data ingestion job. */ Status?: IngestionJobStatus; } export interface DataPreProcessingConfiguration { /** * The sampling rate of the data after post processing by Amazon Lookout for Equipment. For example, if you provide data that has been collected at a 1 second level and you want the system to resample the data at a 1 minute rate before training, the TargetSamplingRate is 1 minute. When providing a value for the TargetSamplingRate, you must attach the prefix "PT" to the rate you want. The value for a 1 second rate is therefore PT1S, the value for a 15 minute rate is PT15M, and the value for a 1 hour rate is PT1H */ TargetSamplingRate?: TargetSamplingRate; } export interface DataQualitySummary { /** * Parameter that gives information about insufficient data for sensors in the dataset. This includes information about those sensors that have complete data missing and those with a short date range. */ InsufficientSensorData: InsufficientSensorData; /** * Parameter that gives information about data that is missing over all the sensors in the input data. */ MissingSensorData: MissingSensorData; /** * Parameter that gives information about data that is invalid over all the sensors in the input data. */ InvalidSensorData: InvalidSensorData; /** * Parameter that gives information about unsupported timestamps in the input data. */ UnsupportedTimestamps: UnsupportedTimestamps; /** * Parameter that gives information about duplicate timestamps in the input data. */ DuplicateTimestamps: DuplicateTimestamps; } export type DataSizeInBytes = number; export type DataUploadFrequency = "PT5M"|"PT10M"|"PT15M"|"PT30M"|"PT1H"|string; export type DatasetArn = string; export type DatasetIdentifier = string; export type DatasetName = string; export interface DatasetSchema { /** * */ InlineDataSchema?: InlineDataSchema; } export type DatasetStatus = "CREATED"|"INGESTION_IN_PROGRESS"|"ACTIVE"|string; export type DatasetSummaries = DatasetSummary[]; export interface DatasetSummary { /** * The name of the dataset. */ DatasetName?: DatasetName; /** * The Amazon Resource Name (ARN) of the specified dataset. */ DatasetArn?: DatasetArn; /** * Indicates the status of the dataset. */ Status?: DatasetStatus; /** * The time at which the dataset was created in Amazon Lookout for Equipment. */ CreatedAt?: Timestamp; } export interface DeleteDatasetRequest { /** * The name of the dataset to be deleted. */ DatasetName: DatasetIdentifier; } export interface DeleteInferenceSchedulerRequest { /** * The name of the inference scheduler to be deleted. */ InferenceSchedulerName: InferenceSchedulerIdentifier; } export interface DeleteLabelGroupRequest { /** * The name of the label group that you want to delete. Data in this field will be retained for service usage. Follow best practices for the security of your data. */ LabelGroupName: LabelGroupName; } export interface DeleteLabelRequest { /** * The name of the label group that contains the label that you want to delete. Data in this field will be retained for service usage. Follow best practices for the security of your data. */ LabelGroupName: LabelGroupName; /** * The ID of the label that you want to delete. */ LabelId: LabelId; } export interface DeleteModelRequest { /** * The name of the ML model to be deleted. */ ModelName: ModelName; } export interface DescribeDataIngestionJobRequest { /** * The job ID of the data ingestion job. */ JobId: IngestionJobId; } export interface DescribeDataIngestionJobResponse { /** * Indicates the job ID of the data ingestion job. */ JobId?: IngestionJobId; /** * The Amazon Resource Name (ARN) of the dataset being used in the data ingestion job. */ DatasetArn?: DatasetArn; /** * Specifies the S3 location configuration for the data input for the data ingestion job. */ IngestionInputConfiguration?: IngestionInputConfiguration; /** * The Amazon Resource Name (ARN) of an IAM role with permission to access the data source being ingested. */ RoleArn?: IamRoleArn; /** * The time at which the data ingestion job was created. */ CreatedAt?: Timestamp; /** * Indicates the status of the DataIngestionJob operation. */ Status?: IngestionJobStatus; /** * Specifies the reason for failure when a data ingestion job has failed. */ FailedReason?: BoundedLengthString; /** * Gives statistics about a completed ingestion job. These statistics primarily relate to quantifying incorrect data such as MissingCompleteSensorData, MissingSensorData, UnsupportedDateFormats, InsufficientSensorData, and DuplicateTimeStamps. */ DataQualitySummary?: DataQualitySummary; IngestedFilesSummary?: IngestedFilesSummary; /** * Provides details about status of the ingestion job that is currently in progress. */ StatusDetail?: BoundedLengthString; /** * Indicates the size of the ingested dataset. */ IngestedDataSize?: DataSizeInBytes; /** * Indicates the earliest timestamp corresponding to data that was successfully ingested during this specific ingestion job. */ DataStartTime?: Timestamp; /** * Indicates the latest timestamp corresponding to data that was successfully ingested during this specific ingestion job. */ DataEndTime?: Timestamp; } export interface DescribeDatasetRequest { /** * The name of the dataset to be described. */ DatasetName: DatasetIdentifier; } export interface DescribeDatasetResponse { /** * The name of the dataset being described. */ DatasetName?: DatasetName; /** * The Amazon Resource Name (ARN) of the dataset being described. */ DatasetArn?: DatasetArn; /** * Specifies the time the dataset was created in Lookout for Equipment. */ CreatedAt?: Timestamp; /** * Specifies the time the dataset was last updated, if it was. */ LastUpdatedAt?: Timestamp; /** * Indicates the status of the dataset. */ Status?: DatasetStatus; /** * A JSON description of the data that is in each time series dataset, including names, column names, and data types. */ Schema?: InlineDataSchema; /** * Provides the identifier of the KMS key used to encrypt dataset data by Amazon Lookout for Equipment. */ ServerSideKmsKeyId?: KmsKeyArn; /** * Specifies the S3 location configuration for the data input for the data ingestion job. */ IngestionInputConfiguration?: IngestionInputConfiguration; /** * Gives statistics associated with the given dataset for the latest successful associated ingestion job id. These statistics primarily relate to quantifying incorrect data such as MissingCompleteSensorData, MissingSensorData, UnsupportedDateFormats, InsufficientSensorData, and DuplicateTimeStamps. */ DataQualitySummary?: DataQualitySummary; /** * IngestedFilesSummary associated with the given dataset for the latest successful associated ingestion job id. */ IngestedFilesSummary?: IngestedFilesSummary; /** * The Amazon Resource Name (ARN) of the IAM role that you are using for this the data ingestion job. */ RoleArn?: IamRoleArn; /** * Indicates the earliest timestamp corresponding to data that was successfully ingested during the most recent ingestion of this particular dataset. */ DataStartTime?: Timestamp; /** * Indicates the latest timestamp corresponding to data that was successfully ingested during the most recent ingestion of this particular dataset. */ DataEndTime?: Timestamp; } export interface DescribeInferenceSchedulerRequest { /** * The name of the inference scheduler being described. */ InferenceSchedulerName: InferenceSchedulerIdentifier; } export interface DescribeInferenceSchedulerResponse { /** * The Amazon Resource Name (ARN) of the ML model of the inference scheduler being described. */ ModelArn?: ModelArn; /** * The name of the ML model of the inference scheduler being described. */ ModelName?: ModelName; /** * The name of the inference scheduler being described. */ InferenceSchedulerName?: InferenceSchedulerName; /** * The Amazon Resource Name (ARN) of the inference scheduler being described. */ InferenceSchedulerArn?: InferenceSchedulerArn; /** * Indicates the status of the inference scheduler. */ Status?: InferenceSchedulerStatus; /** * A period of time (in minutes) by which inference on the data is delayed after the data starts. For instance, if you select an offset delay time of five minutes, inference will not begin on the data until the first data measurement after the five minute mark. For example, if five minutes is selected, the inference scheduler will wake up at the configured frequency with the additional five minute delay time to check the customer S3 bucket. The customer can upload data at the same frequency and they don't need to stop and restart the scheduler when uploading new data. */ DataDelayOffsetInMinutes?: DataDelayOffsetInMinutes; /** * Specifies how often data is uploaded to the source S3 bucket for the input data. This value is the length of time between data uploads. For instance, if you select 5 minutes, Amazon Lookout for Equipment will upload the real-time data to the source bucket once every 5 minutes. This frequency also determines how often Amazon Lookout for Equipment starts a scheduled inference on your data. In this example, it starts once every 5 minutes. */ DataUploadFrequency?: DataUploadFrequency; /** * Specifies the time at which the inference scheduler was created. */ CreatedAt?: Timestamp; /** * Specifies the time at which the inference scheduler was last updated, if it was. */ UpdatedAt?: Timestamp; /** * Specifies configuration information for the input data for the inference scheduler, including delimiter, format, and dataset location. */ DataInputConfiguration?: InferenceInputConfiguration; /** * Specifies information for the output results for the inference scheduler, including the output S3 location. */ DataOutputConfiguration?: InferenceOutputConfiguration; /** * The Amazon Resource Name (ARN) of a role with permission to access the data source for the inference scheduler being described. */ RoleArn?: IamRoleArn; /** * Provides the identifier of the KMS key used to encrypt inference scheduler data by Amazon Lookout for Equipment. */ ServerSideKmsKeyId?: KmsKeyArn; /** * Indicates whether the latest execution for the inference scheduler was Anomalous (anomalous events found) or Normal (no anomalous events found). */ LatestInferenceResult?: LatestInferenceResult; } export interface DescribeLabelGroupRequest { /** * Returns the name of the label group. */ LabelGroupName: LabelGroupName; } export interface DescribeLabelGroupResponse { /** * The name of the label group. */ LabelGroupName?: LabelGroupName; /** * The ARN of the label group. */ LabelGroupArn?: LabelGroupArn; /** * Codes indicating the type of anomaly associated with the labels in the lagbel group. */ FaultCodes?: FaultCodes; /** * The time at which the label group was created. */ CreatedAt?: Timestamp; /** * The time at which the label group was updated. */ UpdatedAt?: Timestamp; } export interface DescribeLabelRequest { /** * Returns the name of the group containing the label. */ LabelGroupName: LabelGroupName; /** * Returns the ID of the label. */ LabelId: LabelId; } export interface DescribeLabelResponse { /** * The name of the requested label group. */ LabelGroupName?: LabelGroupName; /** * The ARN of the requested label group. */ LabelGroupArn?: LabelGroupArn; /** * The ID of the requested label. */ LabelId?: LabelId; /** * The start time of the requested label. */ StartTime?: Timestamp; /** * The end time of the requested label. */ EndTime?: Timestamp; /** * Indicates whether a labeled event represents an anomaly. */ Rating?: LabelRating; /** * Indicates the type of anomaly associated with the label. Data in this field will be retained for service usage. Follow best practices for the security of your data. */ FaultCode?: FaultCode; /** * Metadata providing additional information about the label. Data in this field will be retained for service usage. Follow best practices for the security of your data. */ Notes?: Comments; /** * Indicates that a label pertains to a particular piece of equipment. */ Equipment?: Equipment; /** * The time at which the label was created. */ CreatedAt?: Timestamp; } export interface DescribeModelRequest { /** * The name of the ML model to be described. */ ModelName: ModelName; } export interface DescribeModelResponse { /** * The name of the ML model being described. */ ModelName?: ModelName; /** * The Amazon Resource Name (ARN) of the ML model being described. */ ModelArn?: ModelArn; /** * The name of the dataset being used by the ML being described. */ DatasetName?: DatasetName; /** * The Amazon Resouce Name (ARN) of the dataset used to create the ML model being described. */ DatasetArn?: DatasetArn; /** * A JSON description of the data that is in each time series dataset, including names, column names, and data types. */ Schema?: InlineDataSchema; /** * Specifies configuration information about the labels input, including its S3 location. */ LabelsInputConfiguration?: LabelsInputConfiguration; /** * Indicates the time reference in the dataset that was used to begin the subset of training data for the ML model. */ TrainingDataStartTime?: Timestamp; /** * Indicates the time reference in the dataset that was used to end the subset of training data for the ML model. */ TrainingDataEndTime?: Timestamp; /** * Indicates the time reference in the dataset that was used to begin the subset of evaluation data for the ML model. */ EvaluationDataStartTime?: Timestamp;