Search
(Indexed using OpenSearch)
Embedded Files
Location: Search Menu
General
Using the OpenSearch search engine, the Lexbe eDiscovery Platform (LEP) offers comprehensive, multi-index search methods to search for specific documents across an entire case using various search options.
LEP offers search results generated from Optical Character Recognition (OCR), which captures image-based text, as well as native extraction, meaning text that is excluded from print and/or hidden/embedded text. Going a step further, LEP offers an additional index to search across translated documents, using a multi-lingual search index.
For shared functionality between Browse and Search see Browse/Search for more information.
Search Bar
The Search bar is located at the top of the Browse page. Simply enter a search term into the Search bar and hit Enter or click SEARCH.
The search results will display on the Browse page beneath the search bar.
To expand the search results to include full email families, click the Paperclip icon.
To clear the current search (or search/filter combination) click RESET beside the Search bar.
To run a search over a subset of data instead of the entire document universe, you can set a filter before you search. See Browse/Search for information on how to set filters.
Noise Words
Noise words do not exist in OpenSearch. As such, all words are indexed.
Boolean Searches
LEP supports advanced Boolean searches. A Boolean search consists of a group of words or phrases linked by connectors such as AND and OR that indicate relationship or logic. Supported Boolean operators are listed below with examples. Please note, Boolean operators must be capitalized when used.
AND
Example Search Term: Apple AND Pear
Expected Results: Returns documents that only contain both of the words Apple AND Pear.
OR
Example Search Term: Apple OR Pear
Expected Results: Returns documents that contain either of the words Apple OR Pear.
**A single space can be used in place of the OR operator. In this example, the search term would read: Apple Pear
NOT or AND NOT
Example Search Term: Apple NOT Pear
Example Search Term: Apple AND NOT Pear
Expected Results: Returns documents that contain the word Apple but not Pear.
Parentheses
To use more than one connector, use parentheses to indicate the precise search criteria. For example, apple AND pear OR orange juice could mean (apple AND pear) OR orange, or it could mean apple AND (pear OR orange).
Phrase Search
To search for a phrase, put the phrase in quotation marks.
Example Search Term: "Apple Pear"
Expected Results: Returns documents that contain the exact phrase Apple Pear.
Wildcard Searches
Single and multi character wildcard searches are supported in LEP when searching document content or metadata. Examples of some supported wildcard searches are included below.
Single Character Wildcard: ?
Example Search Term: Appl?
Expected Results: Documents containing Apply or Apple
Example Search Term: F?x
Expected Results: Documents containing Fix, Fox, Fax, etc.
Multi Character Wildcard: *
Example Search Term: Appl*
Expected Results: Documents containing Apply, Apples, Application, Appliance, etc.
Example Search Term: Fa*
Expected Results: Documents containing Fax, Faxes, Fade, Fad, Fail, etc.
Fuzzy Searches
Fuzzy searching will find a word even if it is slightly misspelled. For example, a fuzzy search for "liti3ation" will find "litigation." Fuzzy searching can be useful when searching text that may contain typographical errors or for text that has been scanned using optical character recognition (OCR). More specifically, Fuzzy searching finds all terms with a maximum of one change, where a change is the insertion, deletion or substitution of a single character, or transposition of two adjacent characters.
Fuzzy Search Character: ~
Example Search Term: Brwn~
Expected Results: Documents containing Brown, Brawn, etc.
Example Search Term: Erica~
Expected Results: Documents containing Erika, Ericka, etc.
Example Search Term: Hide~
Expected Results: Documents containing Hides, Hider, Hid, etc.
Proximity Searches
Word Distance
A proximity search allows users to specify a maximum distance between words and/or phrases in a document. OpenSearch counts the words between the specified terms, so in the example the quick brown fox ran, the term quick is within two of ran.
Search Term vs. Relevance of Search Hits
Proximity searches in LEP search for the terms in either order, so in the example "fox quick"~5, even though fox appears first in the search term, documents where quick appears before fox will also be returned. The closer the text is to the order specified in the search term, the more relevant that document is considered to be. When compared to the above example, the phrase "quick fox" would be considered more relevant than "quick brown fox".
Formatting
As demonstrated above, proximity searches must be enclosed in quotes, followed by the tilde, and then the desired distance represented numerically. Please note, that when using phrases within a proximity search, the phrase must be enclosed in both quotes and parenthesis to be properly formatted.
Currently, LEP supports the following types of proximity searches:
Words
Example Search Term: "quick fox"~2
Expected Results: Returns documents where the words quick and fox appear within two words of each other.
Words with Wildcards
Example Search Term: "fox* quick"~10
Expected Results: Returns documents where the words fox, foxes, foxy, etc. appear within ten words of quick.
Words and Phrases
Example Search Term: "fox ("quick brown")"~3
Expected Results: Returns documents where the word fox appears within three words of the phrase "quick brown."
Words with Wildcards and Phrases
Example Search Term: "("quick brown") fox*"~5
Expected Results: Returns documents where the phrase "quick brown" appears within five words of fox, foxes, foxy, etc.
Words with Wildcards, Phrases, and Boolean Connectors
Example Search Term: "fox* (red OR "quick brown")"~5 AND hunt
Expected Results: Returns documents where the word fox, foxes, foxy, etc. appears within five words of red OR the phrase "quick brown", AND hunt is also present.
Nested Proximity Searches
Example Search Term: "("quick fox*"~2) red"~5
Expected Results: Returns documents where quick and fox, foxes, foxy, etc. appear within two words of each other, and they also appear within five words of red.
Metadata Searches
In addition to being able to filter on metadata fields, LEP also supports the searching of certain metadata fields via the Search page. The specific fields are as follows:
Metadata Fields Search Syntax
To metadata.to:
From metadata.from:
Cc metadata.cc:
Bcc metadata.bcc:
Subject metadata.subject:
Author metadata.author:
Doc Type metadata.docType:
Doc Title metadata.docTitle:
Doc Original Title metadata.docOriginalTitle:
Last Modified By metadata.lastModifiedBy:
Master Date metadata.masterDate:
All Metadata Fields metadata.\*:
Search Examples
Search Term: "congratulations promotion"~10 AND metadata.docType:Email
Results: Returns documents where the terms congratulations and promotion appear within ten words of eachother, and where the Doc Type is Email.
Search Term: metadata.from:"Kate Symes" AND metadata.to:djenergy@dowjones.com
Results: Returns documents where the "From" field contains Kate Symes and the "To" field contains djenergy@dowjones.com
Search Term: metadata.\*:("djenergy@dowjones.com" OR "kate Symes")
Results: Returns documents where any of the metadata fields contain Kate Symes or djenergy@dowjones.com
Searching a Date or Date Range
To search a date range, enter the range as shown below. Users must (i) use a hyphen as the separator, (ii) enter the date(s) in YYYY-MM-DD format, and (iii) capitalize the word "TO."
Specific Date
Example Search Term: metadata.masterDate:2019-01-25
Expected Results: Returns all documents with a Master Date of 1/25/2019.
Full Date Range
Example Search Term: metadata.masterDate:[2019-01-25 TO 2020-11-13]
Expected Results: Returns all documents with a Master Date of 1/25/2019, through and including, 11/13/2020.
Partial Date Range
Example Search Term: metadata.masterDate:[2019 TO 2020]
Expected Results: Returns all documents with a Master Date of 1/1/2019, through and including, 1/1/2020.
Search Quick Links (Shared Function)
When a user runs a search, the search term is automatically saved as link under the "Recent" subsection of the Search Quick Links menu. Clicking on a link will automatically run the associated search term. By default, the searches are titled Recent Search YYYY-DD-MM, and are sorted in alphabetical order within their respective subsections.
Search Quick Links are divided into the below three subsections:
Shared: Links under this subsection are available to all users who have access to the case. Users can share as many Search Quick Links as they'd like.
Pinned: Pinned Search Quick Links are private and only available to the user who created them.
Recent: This subsection displays the five most recent searches that you've run. As a result of the default naming convention, the most recent search will always appear at the top of the list until the quick links are renamed.
Editing a Search Quick Link
Renaming
Click Edit
Place the cursor on the line of the Search Quick Link you wish to rename, and rename as desired.
Click OK to save, or click Cancel to close the window without saving your changes.
Pinning , Sharing, Deleting
Click Edit
Click the vertical ellipses next to the desired search link
Select the desired action (Share, Pin, or Delete)
Click OK to save, or click Cancel to close the window without saving your changes.
Search Quick Links Menu
Seeing Search Hits in the Document Viewer
Once the search results are displayed on the Browse page, click one of the hyperlinked document titles to enter the document viewer.
When accessing the Document Viewer from the search results, the search "Hits" references for the document will display in the bottom right of the Coding tab. To go to the specific page in the document where the term appears, click the referenced Page number. See image to the right.
Layouts and Layout Quick Links (Shared Function)
The Search page also allows users to save preferred field layouts and views. See Browse/Search for more information.
ESI Culling
The process of culling (reducing or filtering) electronically stored information (ESI) is designed to defensibly reduce a large collection of documents to a smaller set of potentially responsive documents requiring further review. Depending on how documents were collected and other factors, culling can reduce data sets by 90% or more (if broadly collected) and thereby significantly reduce eDiscovery costs. Culling can be done by Custodian, by date range, and by text. See ESI Culling for more information.
Lexbe Professional Services is available to assist with any culling needs or keyword searching. Contact your sales consultant for a quote, or email support@lexbe.zohodesk.com to speak to a Professional Services representative about any questions related to searching or culling.
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