

Structured analytics operations analyze text to identify the similarities and differences between the documents in a set.
Using structured analytics, you can quickly assess and organize a large, unfamiliar set of documents. On the Structured Analytics Set tab, you can run structured data operations to shorten your review time, improve coding consistency, optimize batch set creation, and improve your Analytics indexes.
See these related pages:
Read a structured analytics scenario
As a system admin tasked with organizing and assessing one of the largest data sets you've worked with for a pending lawsuit against your client, you find a substantial portion of your data set includes emails and email attachments. To save time and accomplish the task of organizing and assessing the large data set for review, you create and run a new structured analytics set using the email threading operation to do the following:
After running your structured analytics set with the email threading operation, you first review the summary report to assess your results at a high level, and then you create a new email threading document view for the purpose of viewing and analyzing your email threading results to identify non-duplicate inclusive emails for review.
It may be helpful to note the following differences between structured analytics and conceptual analytics, as one method may be better suited for your present needs than the other.
Structured analytics | Conceptual analytics |
---|---|
Takes word order into consideration | Leverages Latent Semantic Indexing (LSI), a mathematical approach to indexing documents |
Doesn’t require an index (requires a set) | Requires an Analytics Index |
Enables the grouping of documents that are not necessarily conceptually similar, but that have similar content | Uses co-occurrences of words and semantic relationships between concepts |
Takes into account the placement of words and looks to see if new changes or words were added to a document | Doesn't use word order |
Structured analytics includes the following distinct operations:
Note: The results of email threading decrease in accuracy if email messages contain headers in unsupported languages.
See the Supported languages matrix for a complete list of languages that the language identification operation can detect.
Note: The repeated content filter can be applied to the Analytics index . Repeated content filters are no longer linked to the Analytics profile.
The following table summarizes the primary benefits of each operation.
Operation | Optimizes batch set creation | Improves coding consistency | Optimizes quality of Analytics indexes | Speeds up review |
---|---|---|---|---|
Email threading | √ | √ | √ | |
Name normalization | √ | √ | √ | |
Textual near duplicate identification | √ | √ | √ | |
Language identification | √ | √ | ||
Repeated content identification | √ | √ |
Note: You can change the structured analytics set operations after you’ve run a set. Once you successfully run an operation and want to run another, return to your set and deselect the operation you previously ran and select the new operation. Then, save and run your structured analytics set.
To use structured analytics within Relativity, you must have the Analytics application installed in your workspace. Installing the application will create an Indexing & Analytics tab, along with several fields that allow structured analytics to become operational. Due to the addition of several relational fields, we recommend installing the application during a low activity time via the Applications Library admin tab.
Once you've installed the application to at least one workspace, you must also add the Structured Analytics Manager and Structured Analytics Worker agents to your environment.
Note: Relativity template workspaces already have the Analytics application installed by default.
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