Set Long Text Field Size mass operation

Relativity has a mass operation application, "Set Long Text Field Size" which you can use to calculate the size of long text field. A decimal field must be set up to hold the value that this mass op generates.

The text size will be listed in kilobytes - KB. This mass operation works like others - you need to check off the items you want to process, or check all items in a set. It will be possible to edit the documents in this set for the text field size even if you only have read only permissions to the documents.

A new form will appear. On this form will be a drop down menu of the existing long text fields. Another drop down menu will give you access to all decimal fields that have been set up.

Running Name Normalization in Structured Analytics

To run name normalization in Relativity to identify email aliases, follow these steps:

1. Under Indexing & Analytics, create a new Structured Analytics set.

2. Enter a name and prefix for the set.

3. The document set to analyze should include all emails in the workspace. Documents with more than 30 MB of extracted text will be automatically excluded.

4. Check off the box for the 'name normalization' operation.

5. A profile must be selected which has each of the email header fields mapped to it.

6. Note that you can choose to set the radial dial for the email header fields to 'No' and just run name normalization on the extracted text.

7. Save the set. In the console on the right, you'll have the option to run the structured analytics set. You will be prompted to either run on the full set or just newly added documents. In a new set, all documents will be analyzed no matter what option is selected at this point.

8. Tonight it only took Relativity about four minutes to find more than 2900 email aliases in a set of about 1600 emails.

Name Normalization

Structured Analytics in Relativity allows you to run name normalization in order to identify aliases of email addresses and the groups those emails addresses are from.

Name normalization will parse aliases in email headers by semi-colons. It will look for names listed with email addresses in these familiar formats:

"Lincoln, Abraham" <abraham.lincoln@whitehouse.gov>

'Lincoln, Abraham' <abraham.lincoln@whitehouse.gov>

Lincoln, Abraham <abraham.lincoln@whitehouse.gov>

'Lincoln, Abraham' [abraham.lincoln@whitehouse.gov]

Lincoln, Abraham [abraham.lincoln@whitehouse.gov]

So, in any one of these examples, Relativity will associate three aliases with the same entity. E.g.,

1. "Lincoln, Abraham" <abraham.lincoln@whitehouse.gov>

2. Lincoln, Abraham

3. abraham.lincoln@whitehouse.gov

Relativity will join email addresses listing an identical name to the entities that have already been set up for different email addresses. So:

"Lincoln, Abraham" <abraham.lincoln@illinois.com>

. . . will be joined to the same entity as "Lincoln, Abraham" <abraham.lincoln@whitehouse.gov>.

Relativity will also perform segment matching to help associate email aliases with one another. Segment matching reviews emails sent on the same date, with the same body to see if email addresses in the header fields can be joined to the same entity.

Relativity recommends using a separate structure analytics set for name normalization.