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Version: 10.2.4

Reporting API

Reporting API enables the following:

  • Building custom dashboards
  • Using a BI tool against the data warehouse provided by WorkFusion
  • Integrating data seamlessly into your data warehouse from WorkFusion's one

The following schema represents all data available for analysis via Reporting API. These include the operational metrics of Control Tower, Workspace, AutoML, RPA, Bot Execution, and Infrastructure. You can leverage the automation metrics without any additional calculation efforts.

note

For a comprehensive overview of the WorkFusion Analytics capabilities, we recommend taking the Analytics for Business Users in Automation Academy.

Connect to Reporting API​

To start using Reporting API, connect it to WorkFusion Analytics Desktop. Mind the following prerequisites:

  • The connection to Analytics Desktop requires a license. For details, refer to the Analytics | Licensing topic.
  • The connection employs the Microsoft SQL Server database. The default port is 1433. If you use a non-default one, specify it in the following format: <server name>, <port number>.
  • Default database name: workfusion.
  • Default schema name: dm.
  • Default username for SQL credentials: rapi.

To connect Reporting API to Analytics Desktop, follow the instructions below:

  1. Start WorkFusion Analytics Desktop. In the Connect section, click More..., and select Microsoft SQL Server.

  2. In the Server field, specify the Analytics server you want to connect to.

  3. In the Database field, enter the default database name.

  4. Enter your Username and Password. Select Require SSL if needed.

  5. (Optional) Select Initial SQL to specify an SQL command to run at the beginning of every connection. For example, this can be a command to refresh an extract upon opening a workbook, sign in to WorkFusion Analytics Server, or publish to WorkFusion Analytics Server.

  6. Click Sign In.

Select dataset​

After signing in, you are on the Data Source page. Perform the following steps:

  1. Select one or more Tables to match the goals of your analysis, relying on the Reporting API reference. Use the search box to browse for specific names, if needed.

  2. Drag the selected Table to the canvas on the right.

    Alternatively, set up custom SQL queries. For details, refer to the Tableau official documentation.

  3. Click the Sheet tab to proceed with building a report.

    On the Data Source page, you can also accomplish the following:

    • Change or add an initial SQL.

    • Connect an additional Data Source.

Explore Reporting API reference​

TableOrderFieldDescriptionValue
auto_ml_document1IdPrimary key for the table.Auto-generated integer number
auto_ml_document2Process Execution IdForeign key to the process execution table.
auto_ml_document3Execution typeShows the document processing details:
  • Bot assisted. Means that the model extracted not all fields.
  • Fully automated. Means that only a model processed the document.
  • Fully manual. Means that the document was handled manually without a model.
  • Expired. Means that the Manual Task for information extraction is expired. So, the related documents are to be excluded from calculations.
auto_ml_document4Is AutomatedShows whether it was only model that extracted all fields in the document.0=no, 1=yes
auto_ml_document5GoldContains the number of fields to be extracted—FP and TN summed up.Integer
auto_ml_document6ExtractedContains the number of extracted fields—TP and FP summed up.Integer
auto_ml_document7ML Processing TimeShows the processing time of the <bold>automation-extract</bold> plugin (post to the ML service + ML extract and so on). <bold>Note</bold>: the <bold>automation-extract</bold> plugin is deprecated.Milliseconds
auto_ml_document8Manual processing timeShows the time to process the document manually.Milliseconds
auto_ml_field1IdPrimary key for the table.Auto-generated integer number
auto_ml_field2Document IdForeign key to the document table.
auto_ml_field3Group NameContains several fields—the equivalent of group answers.Can be null.
auto_ml_field4NameField name—an element in the document to be processed by the AutoML model.
auto_ml_field5Gold ValueContains the correct field value provided in advance or during AutoQC.Can be null.
auto_ml_field6Extracted ValueMeans the correct value for the field.Can be null.
auto_ml_field7TPTrue Positive (correct). The model should have extracted the value and has done so.0 or 1
auto_ml_field8TNTrue Negative (correct). The model should have not extracted the value and has done so.0 or 1
auto_ml_field9FPFalse Positive (mistake). No value is supposed to be extracted, but the model has extracted something.0 or 1
auto_ml_field10FNFalse Negative (mistake). The model should have extracted the value but has not done so.0 or 1
auto_ml_field11ConfidenceA dependent variable representing the estimated accuracy for the given threshold returned by the model.0 or 1
auto_ml_field12ScoreNormalized score, meaning an independent variable returned by the model. The variable represents the threshold model confidence score for a given field rounded to the nth decimal point.0 or 1
auto_ml_field13Result typeStores the field extraction result calculated based on the following conditions:
  • If the FP+FN amount is greater than 0, the stored value is Extracted with Errors.
  • If the total(FP)>0, the result is Extracted with Errors.
  • If the total(FN)>0, it is Failed to Extract.
  • If the total(TP)>0, it is Correctly Extracted.
Otherwise, the result type is Should not be Extracted.
  • Extracted with Errors
  • Failed to Extract
  • Correctly Extracted
  • Should not be Extracted
auto_ml_field14Report DateThe field is populated when the record containing the data for AutoML processing goes through the Analytics process step. Format: YYYY/mm/dd hh:mm:ss.
auto_ml_field2Process IdForeign key to the process table.
process_execution_ml2Baseline hoursShows the total expected time to complete a task as if all fields were extracted manually.Hours
process_execution_ml3Saved hoursShows the difference between the expected time and actual manual effort.Hours
process_execution_ml4Manual effortShows the actual time spent on completing a task: how long it took to manually extract those fields that the model failed to extract.Hours
process_execution_ml5Manual work reductionShows how much time AutoML models save relative to the expected task completion time.From 0 to 100%
process_execution_ml6STP RateShows the rate of documents processed by bots only.From 0 to 100%
process_execution_mlProcess execution IdForeign key to the process_execution table.

Build report​

To generate a report based on the selected dataset, drag-and-drop rows, columns, and values on the pivot table. Select the most appropriate visualization.

For detailed instructions on building reports, refer to the Tableau video tutorials (compatible with 10.x IA Cloud versions).

Publish report​

For instructions on publishing the resulting report to the Analytics Server, refer to the Deploy custom dashboards guide.

To check the publishing results, go to Control Tower. On the CT main menu, select Analytics > Custom and make sure the built reports are available there.

::: tip see demo For an example, refer to the Deploy custom dashboards guide. :::