Monitoring a Review Center queue
The Review Center dashboard provides a centralized location to track, manage, and edit all Review Center queues. In addition, you can track reviewer coding decisions through a variety of methods.
See these related pages:
- Creating a Review Center queue
- Reviewing documents using Review Center
- Review Center security permissions
After creating a queue, navigate to the Review Center tab. This tab contains a dashboard showing all queues, their statistics, and controls related to queue progress.
The Review Center dashboard contains the following sections.
The queue tab strip contains a tab for each queue that has been created. To make the dashboard show details for a queue, click on its name in the tab strip.
Below the queue name, each queue shows its status. The possible statuses are:
Not Started—the queue has not been prepared or started.
Preparing—the queue is refreshing the saved search for the first time. If it is a prioritized review queue, this also trains the classifier.
Prepared—the queue has finished preparing for the first time, but it has not been started. It may or may not have a reviewer group assigned.
Starting—the admin has started the queue, and the queue is becoming active for reviewers. During this phase, the queue also refreshes the saved search and retrains the classifier if needed.
Active—the queue has started, and reviewers can start reviewing.
Paused—the admin has paused the queue.
Canceling—the admin has canceled the process of starting or refreshing the queue.
Complete—the admin has marked the queue as complete, and it is no longer available to reviewers.
Errored—an error occurred. When this happens, the error details will appear in a banner at the top of the dashboard.
Ready for Validation—a linked validation queue has been created, but not started.
In addition, if any of the statuses have the word "Validation" added to them (such as "Validation Paused"), this means the status applies to a linked validation queue. For more information, see Review validation.
At the right of the strip, the Add Queue button lets you quickly create new queues. For instructions, see Creating a new queue from a template.
If you have a large number of queues, you can filter them according to their assigned labels in the Queue Label field.
To filter the queue tab strip:
Click into the search bar above the queue tab strip.
A drop-down list of labels appears.
Select the labels you want to filter by. You can also type in the search bar to narrow the list, then press Enter to select or deselect a label.
Close the list using the arrow at the right end of the bar.
The queue tab strip now only shows queues whose labels are listed in the search bar. If several labels are listed, queues that match any one of them will appear.
The queue tab filters only apply to the tab strip. They do not affect any of the charts or statistics on the rest of the page.
The Queue Summary section shows the reviewer group, saved search, coding fields, and controls for actions such as pausing or refreshing the queue. The "<X> Active" statistic under the reviewer group shows how many reviewers currently have documents checked out to them. Additionally, clicking on the saved search name or the coding field name takes you to that saved search or field.
To view all settings for the current queue, click on the arrow symbol on the left side. This expands the Queue Summary panel and shows the detailed setting list.
In order for a queue to function, Review Center has to run the saved search, check for any outside-coded documents, and perform other actions. If it is a prioritized review queue, it also needs to periodically retrain the classifier. This collection of actions is referred to as refreshing the queue.
Depending on your settings, the refresh button may say several things:
Prepare Only—appears when the queue has not been started. This runs the saved search and trains the classifier for the first time, but it does not start the queue. Alternately, you can click Prepare and Start to perform both actions together.
Note: Preparing a new queue in advance makes the Start Review action take only a few seconds. This can be helpful if your data source is very large or if you have a complicated saved search. For example, you might prepare a new queue overnight, then start it in the morning.
Refresh Queue—appears during a review that does not use auto-refresh. Clicking this refreshes the queue.
Auto Refresh—appears during a review that uses auto-refresh. Clicking this starts an immediate refresh of the queue. For more information, see Auto-refreshing the queue.
After you click the button, a Cancel option appears.
If you edit a queue's settings when the queue is partway through refreshing, the refresh will automatically cancel. Any edits that affect the queue refresh will take effect during the next refresh.
If Queue Refresh is set to On in the queue settings, the queue will automatically refresh when a certain percentage of documents have had coding changes. These refreshes only happen after the queue has been started, and you can change this setting at any time.
The amount of document coding that triggers a refresh increases every time. After each refresh, there will need to be slightly more documents coded than the amount that triggered the last refresh.
If you need to trigger an immediate refresh, click on the words Auto Refresh to trigger an additional manual refresh. For example, if new documents have been added to the saved search, you can click this to add them to the queue quickly instead of waiting until the next auto-refresh.
While the queue is auto-refreshing, a Cancel option appears. If you cancel the current auto-refresh, the queue will still try to auto-refresh again later.
Reviewers can still review documents in an active queue while it refreshes. Clicking the refresh button, running an auto-refresh, or canceling a refresh makes no difference to reviewer access.
Similarly, if the queue was paused before the refresh, it will stay unavailable. Active queues stay active, and paused queues stay paused.
The Start Review button makes the queue available for review. If the queue has never been prepared before, it will say Prepare and Start. This also runs the saved search and trains the classifier for the first time.
After the queue has finished starting, the symbol beside this option changes to a pause button. Clicking this pauses the queue and stops reviewers from checking out more documents.
A few things are required before starting a queue:
You must have a reviewer group assigned.
For a prioritized review queue, you must code at least two non-empty documents in your data source before preparing or starting the queue: one with the positive choice on your review field, and one with the negative choice. This gives the AI classifier the information it needs to start making its predictions. The more documents you code in advance, the better the starting predictions will be.
To edit the queue or perform other less-frequent actions, click on the three-dot menu on the right.
The menu options are:
Edit—opens a modal to edit any of the queue settings. For field descriptions, see Creating a Review Center queue.
Release Documents—releases all documents that are checked out by reviewers. If a reviewer falls inactive and does not review the last few documents in a queue, this frees up those documents for reassignment.
Note: If you release documents while a reviewer is actively reviewing, that person will be able to finish coding, but their documents may get checked out by another reviewer at the same time. To prevent this, ask any active reviewers to exit and re-enter the queue after you click the link.
Set up Validation—opens the options to create a review validation queue. For more information, see Review validation.
Mark as Complete—sets the queue's status to Complete and moves it to the far right of the queue tab strip. This also removes the queue from the Review Queues tab, and reviewers can no longer access it. After the queue has been marked Complete, this option changes to Re-enable. Clicking this sets the queue's status to Not Started and returns it to the Review Queues tab.
The Review Progress section shows statistics for the current queue's progress.
The statistics are:
Total Docs—the total number of documents currently in the queue's data source. To be counted, the queue must have been prepared or refreshed after the documents were added or removed. The "100%" in smaller print underneath it indicates that this is the total document set.
Docs Coded—the number of documents in the data source that either have a value in the review field, or have been skipped. This includes documents coded outside the queue. The smaller percentage underneath it shows the percentage of Docs Coded divided by Total Docs.
<Positive Choice>—the number of documents coded with the positive choice on the review field. This includes documents coded outside the queue. The smaller percentage underneath it shows the percentage of <Positive Choice> divided by Docs Coded.
<Negative Choice>—the number of documents coded with the negative choice on the review field. This includes documents coded outside the queue. The smaller percentage underneath it shows the percentage of <Negative Choice> divided by Docs Coded.
Neutral—the number of documents coded with a neutral choice on the review field. This includes documents coded outside the queue. The smaller percentage underneath it shows the percentage of Neutral documents divided by all Docs Coded.
Relevance Rate—the total percentage of documents coded positive. This is calculated by counting the number of documents coded positive, then dividing it by the total number of coded, non-skipped documents. The bold percentage shows the relevance rate including documents coded either inside or outside of the queue, while the smaller percentage underneath it shows the relevance rate only for documents coded inside the queue.
Uncoded—the number of documents in the data source with no value in the review field. This includes documents that were skipped or had their coding decision removed. The smaller percentage underneath it shows the percentage of Uncoded documents divided by Total Docs.
Skipped—the number of documents that were skipped within the queue. The smaller percentage underneath it shows the percentage of Skipped documents divided by all Uncoded documents.
Predicted <Positive Choice> (Prioritized Review only)—the number of documents in the data source with no review field value and a relevance rank greater than or equal to 50.00. The smaller percentage underneath it shows the percentage of Predicted <Positive Choice> documents divided by all Uncoded documents.
Predicted <Negative Choice> (Prioritized Review only)—the number of documents in the data source with no review field value and a relevance rank less than 50.00. The smaller percentage underneath it shows the percentage of Predicted <Negative Choice> documents divided by all Uncoded documents.
The dashboard includes two visualization panels. Both panels have the same options for charts and tables to show, which lets you choose which visualization to show on which panel, in any order.
To navigate the visualization panel:
To select a different visualization, click the blue arrow () next to the visualization's name. This opens a drop-down menu with all other visualizations.
To switch from the chart view to the table view, click the Chart drop-down in the upper right corner and select Table. This shows a table with the same information as the selected chart. The table can also be downloaded as a .csv file.
To zoom in or out on a chart, hover the cursor over it and scroll. All charts reset to their default zoom when you reload the page.
Some charts and tables are available for any type of queue. These include:
The Coding Progress tab shows the count of documents that have been coded in the queue over time. Data is reported in 15-minute increments.
The Relevance Rate tab shows the relevance rate over time. This can be shown overall or by user.
Each solid data point represents 100 documents, and a hollow data point represents any remainder. For example, if 201 documents have been coded, there will be 3 points: 2 solid points for each set of 100, and 1 hollow point for the final document.
Other details about the data points include:
If you have more than one data point in a 15 minute increment, the chart shows them as two points on a vertical line. This can happen if many reviewers are coding quickly.
The Date field for a data point is the date the last document in the set of 100 was logged.
For prioritized review queues, the relevancy rate usually declines over time. However, the relevance rate may spike if lots of new documents are added to the queue or if the definition of relevance changes during review. For saved search queues, the shape of the relevancy rate graph varies depending on the saved search being used.
The Review Speed tab shows the number of documents coded per hour. Data is reported in 15-minute increments.
The Review Speed data can be shown overall or by user. When it's set to show all reviewers, the line chart shows a weighted average of the review speeds of the reviewers. It does not report their aggregate review speed.
The Queue History tab shows the state of the queue at every previous refresh. This is shown only as a table, not a chart.
The columns vary depending on the queue type. For saved search queues, it also depends on whether positive and negative choices are selected for the review field.
Possible columns include:
Refresh Start Time
Refresh End Time
Total Items—the total number of documents in the data source.
Refresh Type—this can be either Auto or Manual.
Coded <Positive Choice> (optional for saved search queues)
Coded <Negative Choice> (optional for saved search queues)
Uncoded Predicted <Positive Choice> (prioritized review queues only)
Uncoded Predicted <Negative Choice> (prioritized review queues only)
All document counts show the number of documents in that category at the Refresh End Time.
The Rank Distribution chart is available for prioritized review queues. This chart helps you compare the model's predictions to reviewer's actual coding decisions. It shows the number of documents at each rank, from 0 to 100, color-coded by the reviewers' coding decisions on those documents.
A low relevance rank means that the model predicts that the document is more likely to be coded negative, and a high relevance rank means that the model predicts the document is more likely to be coded positive.
If you zoom out on the Rank Distribution chart, you may see documents with ranks below zero. These are documents that could not be classified. For more information, see Understanding document ranks.
The Reviewed Documents table shows which reviewer coded each document, how long the reviewer took, and how it was coded.
For saved search queues, the columns depend on whether a review field is set, as well as if positive and negative choices are selected.
Possible columns include:
Control Number—the control number of the document.
Reviewer—the assigned reviewer's name.
Coded Time—the check-in time for the document. If the document is still checked out, this is blank.
Coding Duration—how much time passed between the document being checked out to the reviewer and checked back in. This is reported in hours, minutes, and seconds (HH:MM:SS).
Queue Coding Decision (optional for saved search queues)—how the document was coded when the reviewer checked it back in. If the document was skipped, this is blank.
<Review Field Name> (optional for saved search queues)—the current coding designation of the document.
Queues can be edited or deleted from the Review Library tab.
To delete a queue:
Navigate to the Review Library tab.
Click on the queue you want to delete.
A confirmation pop-up will appear.
Click Delete again.
After the process completes, you will return to the main Review Library tab.
Deleting a queue does not remove any of the coding decisions or rank values that have been assigned to the documents.
Note: If you delete a main queue that has a validation queue linked to it, it also deletes the validation queue. For more information on validation queues, see Review validation.
If a required field or object that a queue relies on is deleted or moved, this puts the queue into a warning state. Any queue preparation or auto-refresh stops, and a message appears at the top of the Review Center tab directing you to the field or object that needs to be fixed. Your reviewers also see a warning at the top of the Review Queue page telling them which queue is misconfigured and that they should alert their administrator.
When this happens, we recommend pausing the queue and checking its settings. For example, if the saved search was deleted, you may need to link the queue to a new saved search. If a required field was deleted, you may need to create a new one.
If you have checked the queue's settings and still see warnings, contact Product Support.
During prioritized review, the AI classifier assigns a rank to each document. These ranks are stored in the Rank Output field, and they determine the order in which reviewers will see documents.
Most document ranks range from 0 to 100. The higher the score, the stronger the prediction that the document will be coded on the positive choice. The AI classifier recalculates ranks every time the queue refreshes, and the highest-ranking documents are served up to reviewers.
- Active Learning and Review Center use similar ranking systems, but the classifiers are not the same. If you use both tools to classify the same document, it will receive separate scores. These scores can be very different depending on circumstances.
- In order to improve efficiency and performance, Relativity reserves the right to update the prioritized review queue's AI classifier during software upgrades. Although we work hard to minimize disruptions, these upgrades can cause minor differences in document ranking. As a result, administrators may occasionally see minor variations in document ranks after a queue is refreshed, even without any new document coding.
If the classifier cannot classify a document, it will assign the document a value below zero. These values are:
An error occurred while processing the data through the classifier.
|The extracted text field is empty. If you see this rank, consider making a saved search queue to review these documents separately.
|The document's extracted text field is larger than the limit of 600 KB. If you see this rank, we recommend filtering out large documents from your saved search to improve the performance of the classifier.
You can view coding decisions made by each reviewer in the Reviewed Documents table. For more information, see Reviewed Documents table.
Alternatively, you can also use the following methods.
The Field Tree helps you get a quick overview of document coding decisions. It does not show which reviewer made each decision.
To view coding decisions using the Field Tree:
Navigate to the Documents tab.
In the browser panel, click on the tag symbol to open the Field Tree.
Scroll to the folder labeled Review Center and expand it.
Click on your queue.
Click on the Reviewed tag to view coded documents or the Skipped tag to view skipped documents.
If you rename or delete a queue, this renames or deletes the matching Field Tree tags also.
The Track Document Field Edits by Reviewer application lets you see which reviewer made each coding decision. You can set up the application individually for each of your queues.
Install the application using the instructions from Track document field edits by reviewer.
When configuring the application:
Put your Reviewed On and Reviewed By fields into a saved search or view for monitoring.
Set your queue's review field as the Field to Monitor.
For most use cases, set Track Initial Change Only? to Yes. This sets it to track the first reviewer of the document, instead of overwriting the Reviewed On and Reviewed By fields every time a user edits the document.
If you set up the application after starting your queue, you can still see previous coding decisions by following the steps under Populating Historical Records.
Review Center templates and queues are Relativity Dynamic Objects (RDOs), which typically can be moved across workspaces or instances with Relativity Integration Points and Relativity Desktop Client. However, because of the complexity of an active queue, we do not support moving active queues. Doing so could damage your Review Center environment.
We do support moving queue templates across workspaces or instances using Relativity Integration Points and Relativity Desktop Client. This process is safe for your environment.