dtSearch

dtSearch icon

Relativity's dtSearch engine provides advanced search functionality such as proximity, stemming, and fuzzy searches across any field type. It also supports the use of Boolean operators and custom noise word lists and the basic searching features available in keyword searches. After building your dtSearch index, the Dictionary search option becomes available.

Note: Relativity partitions a single index into smaller indexes, called sub-indexes, which multiple workers build simultaneously. This increases performance by spreading out the work over a configurable number of agents. When you perform a search, Relativity runs your query on the smaller indexes in parallel. The application then federates and returns your results. For more details, see the Ask The Expert Training content: Searching: Best Practices for dtSearch Builds.

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Using dtSearch indexes

There are roughly three million files relevant to a case you are working on, including emails, email attachments, invoices, and technical manuals related to construction practices and material handling. It is early in the case, and you need to gain an understanding of the data set. You also need to retrieve certain text related to five substances that you know are prevalent in this data, as an employee from the construction company mentioned these specifically in an email to you. To do this, you need to be able to perform proximity, stemming, and fuzzy searches on your data set. So you create a new dtSearch index.

You call the index Hazardous Materials dtSearch so that you can identify it in the Search drop-down menu on the Documents list. You might also create an Analytics index for this case with a similar name, so make sure to differentiate them clearly. For the Searchable set field, you select a saved search that you have already created called Hazardous Materials searchable set, which has documents to which you have already applied keywords related to the substances mentioned in your client's email.

Because many of the invoices and emails in your data set contain references to various purchases of building materials made by various departments in the construction company you are helping to represent, you set the Auto-recognize date, email, and credit card numbers field to Yes.

You leave all other fields at their default settings and save the index. You then build and activate the index so that you can select it in the Search drop-down menu.

When you select the index and search your document set on it, you run proximity searches to see how close terms relating to hazardous substances occur to the names of the building materials that may or may not contain them. The searches you run include the following:

  • lead W/10 paint
  • lead W/10 plumbing pipes
  • lead W/10 connectors
  • lead W/10 solder
  • asbestos W/10 insulation and
  • asbestos W/10 pipe coverings
  • asphalt W/10 sealant
  • asphalt W/10 adhesives
  • radioactive W/10 fluorescent lamps
  • radioactive W/10 smoke detectors

As you keep running these proximity searches, you get down to a small group of intriguing emails between a prospective buyer, your client, and a prospective seller. This may prove that the seller had knowledge of the fact that those building materials were potentially dangerous when they were negotiating a price with your client. This discovery turns out to be crucial to the case.