Analytics indexes

Unlike traditional searching methods like dtSearch, Analytics is an entirely mathematical approach to indexing documents. It does not use any outside word lists, such as dictionaries or thesauri, and it is not limited to a specific set of languages. Unlike textual indexing, word order is not a factor.

The basis of conceptual analytics and active learning is an Analytics index. There are two types of indexes:

• Conceptual—uses Latent Semantic Indexing (LSI) to discover concepts between documents. This indexing process is based solely on term co-occurrence. The language, concepts, and relationships are defined entirely by the contents of your documents and learned by the index. For more information, see Analytics and Latent Semantic Indexing (LSI).
• Classification—uses coded examples to build a Support Vector Machine (SVM) to predict a document's relevance. This index is used solely by the Active Learning application. Classification indexes learn how terms are related to categories based on the contents of your documents and coding decisions made within the Active Learning project. For more information, see Analytics and Support Vector Machine learning (SVM).

Note: The searchable set and training set used for Analytics index creation are now referred to as the data source and training data source. See Creating an Analytics index.

You can run the following Analytics operations on documents indexed by a conceptual index:

• Analytics categorization sets
• Clustering
• Concept searching
• Keyword expansion
• Find similar documents

Analytics and Latent Semantic Indexing (LSI)

Analytics and Support Vector Machine learning (SVM)

Creating an Analytics index

Analytics uses only the documents you provide to make a search index. Because no outside word lists are used, you must create saved searches to dictate which documents are used to build the index. However, if you want to limit search results to certain document groups or have more than one language in the document set, multiple indexes might give you better results.

Note: Permissions for the Search Index object must be kept in sync with permissions on the Analytics Index object. Refer to the Analytics Index object description in the Workspace permissions.

Conceptual index

Classification index

Securing an Analytics index

If you want to apply item-level or workspace-level security to an Analytics index, you must secure both the Analytics Index object and the Search Index object for that particular index.

Restricting a group from viewing an Analytics Index does not restrict them from searching on the index unless access to the corresponding Search Index is also restricted.

Note: If you are applying item-level security from the Search Indexes tab, you may need to create a new view and add the security field to the view.

Data source and training data source considerations

Index and document size limits

Index sizes are limited by default, and some large documents are excluded from indexing as follows.

Index size limits

By default, indexes are limited by the following parameters:

• Indexes can include up to 12 million documents each.

• Conceptual indexes can reach up to 60 GB in size.

These limits can be changed by the administrator. For help with adjusting index size limits, contact Relativity Support.

Document size limits

Analytics indexes automatically suppress documents larger than 30 MB before sending them to the Analytics engine. Suppressed large documents will appear in the Document Exceptions. You can also view suppressed documents from the Document list by using the Excluded from Training and Excluded from Searchable Set choices on the Analytics Index Document field.

Analytics index console operations

Once you save the Analytics index, the Analytics index console appears. From the Analytics index console, you can perform the following operations:

• Populating the index
• Monitoring population/build status
• Retrying exceptions
• Viewing conceptual index document exceptions
• Showing population statistics
• Showing index statistics

Note: Population statistics and index statistics are only available for conceptual indexes.

Populating the index

To populate the Analytics index on the full set of documents, click Run on the Analytics Index console, then choose Full from the modal that appears. This adds all documents from the data source and training data source to the ready-to-index list. Document “preprocessing” also occurs to clean up text. This includes the following:

• Numbers and symbols are ignored.
• All words are made lowercase.
• Filters found under Advanced Settings are applied. fFr example, email header filter.

Once population is complete, the index builds.

Note: If you have access to SQL, you can change the priority of any Analytics index-related job, index build, population, or others, by changing the value on the Priority column in the ContentAnalystIndexJob database table for that index. This column is null by default, null being the lowest priority. The higher you make the number in the Priority column, the higher priority that job becomes. When you change the priority of a job while another job is in progress, Analytics does not stop the in-progress job. Instead, the job will finish before starting on the new highest priority.

Canceling population

While the index is populating, the following console option becomes available:

• Cancel—cancels a full or incremental population. Canceling population requires you to perform a full population later. After you click this button, any document with a status of Populated is indexed. After the indexing of those documents is complete, population stops, leaving an unusable partial index. To repair the index, perform a Full Population to purge the existing data. You can also delete the index from Relativity entirely.

Incremental population

Once population is complete, you have the option to populate incrementally to account for new or removed documents from the data source and training data source on the ready-to-index list. To perform an incremental build, click Run on the console, then choose Incremental from the modal that appears. See Incremental population considerations for conceptual indexes for more information.

Notes:
• If, after building your index, you want to add any documents that were previously excluded from training back into the training data source document pool, you must disable the Optimize training set field on the index and perform another full population. An incremental population does not re-introduce these previously excluded documents.
• Incremental population automatically triggers a rebuild of the classification index.

Building the index

Once population is complete, the index will build automatically. During this phase, training data source documents and Latent Semantic Indexing (LSI) are used to build the concept space based on the relationships between words and documents. Data source documents are mapped into the concept space, and noise words, very common words, are filtered from the index to improve quality.

Please note that the index is unavailable for searching during this phase.

Monitoring population/build status

You can monitor the progress of any Analytics index process with the progress panel at the top of the layout.

Population and index building occurs in the following stages, which will appear within the progress panel:

• Step 0 of 3 – Waiting – Indexing Job in Queue
• Step 1 of 3 – Populating
  • Constructing Population Table
  • Populating
• Step 2 of 3 – Building
  • Preparing to build

  • Building

    • Starting
    • Copying item data
    • Feature weighting
    • Computing correlations
    • Initializing vector spaces
    • Updating searchable items
    • Optimizing vector space queries
    • Finalizing
• Step 3 of 3 – Activating
  • Preparing to Enable Queries
  • Enabling Queries
  • Activating

Document breakdown fields

The following fields appear in the Document Breakdown section:

• Data Source—the number of indexed data source documents.
• Training Data Source—the number of indexed training data source documents.

Note: If an Analytics index goes unused for 15 days, it is automatically disabled to conserve server resources. It then has a status of Inactive and is not available for use until it is activated again. This setting is determined by the MaxAnalyticsIndexIdleDaysentry in the Instance setting table. The default value for this entry can be edited to change the number of maximum idle days for an index.

Activating the index

Building a conceptual index automatically activates it. This makes the index available for users by adding the index to the search drop-down menu on the Documents tab and to the right-click menu in the viewer. All active indexes are searchable.

Deactivating the index

Once a conceptual index is activated, you have the option of deactivating it.

You may need to deactivate an index for the following reasons:

• You need to shut the index down so it doesn't continue using RAM.
• The index is inactive but you don't want to completely remove it.

To deactivate an index, click Deactivate Index on the console. A yellow banner will appear at the top of the console.

To reactivate the index, click Reactivate Index on the banner.

Note: If you deactivate an index, you can't run concept searches against the index and keyword expansion becomes unavailable on the index.

Retrying exceptions

If exceptions occur while populating or building a classification index, you have the option of retrying them from the console. To do this, click Retry Exceptions.

If exceptions occur while populating or building a conceptual index, the system will retry them automatically.

Retrying exceptions attempts to populate the index again.

Note: You can only populate one index at a time. If you submit more than one index for population, they'll be processed in order of submission by default.

Viewing conceptual index document exceptions

When errored documents are removed from population in a conceptual index, they appear on the index console in the Document Exceptions panel. This panel only appears when exceptions exist.

The panel includes the following fields:

• ArtifactID—the artifact ID of the document that received the error.
• Message—the system-generated message accompanying the error.
• Status—the current state of the errored document. The possible values are:
  • Removed From Index—indicates that the errored document was removed from the index.
  • Included in Index—indicates that the errored document was included in the index because you did not select the option to remove it.
• Date Removed—the date and time at which the errored document was removed from the index.

Showing population statistics

To see a list of population statistics, click Show Population Statistics.

Note: Population statistics are only available for conceptual indexes.

This option is available immediately after you save the index, but all rows in this window display a value of 0 until population is started.

This displays a list of population statistics that includes the following fields:

• Status—the state of the documents included in the index. This contains the following values:
  • Pending—documents waiting to be included in either population or index build.
  • Processing—documents currently in the process of being populated or indexed.
  • Processed—documents that have finished being populated or indexed.
  • Error—documents that encountered exceptions in either population or index build.
  • Excluded—excluded documents that were removed from the index as per the Optimize training set field setting or by removing documents in error.
  • Total—the total number of documents in the index, including errored documents.
• Training Set—documents designated for the training data source that are currently in one of the statuses listed in the Status field.
• Searchable Set—documents designated for the data source that are currently in one of the statuses listed in the Status field.

Showing index statistics

To see an in-depth set of index details, click Show Index Statistics. This information can be helpful when investigating issues with your index.

Note: Index statistics are only available for conceptual indexes.

Clicking this displays a view with the following fields:

• Build Completed Date—the date and time at which the index was built.
• Item Last Added Date—the date and time at which the most recent item was added.
• Dimensions—the number of concept space dimensions specified by the Analytics profile used for this index.
• Integrated dtSearch Enabled—whether or not dtSearch was used to assist document validation.
• Index ID—the automatically generated ID created with a new index.
• Unique Words in the Index—the total number of words in all documents in the training data source, excluding duplicates. If a word occurs in multiple documents or multiple times in the same document, it's only counted once.
• Searchable Documents—the number of documents in the data source, determined by the saved search you selected in the Data Source field when creating the index.
• Training Documents—the number of documents in the training data source, determined by the saved search you selected for the Training Data Source field when creating the index. The normal range is two-thirds of the data source up to five million documents, after which it is half of the data source. If this value is outside that range, you receive a note next to the value.
• Unique Words per Document—the total number of words, excluding duplicates, per document in the training data source. The normal range is 0.80-10.00. If this field shows a value lower or higher than this range, a note appears next to the value. If your data set has many long technical manuals, this number may be higher for your index. However, a high value might also indicate a problem with the data, such as poor quality OCR.
• Average Document Size in Words—the average number of words in each document in the training data source. The normal range is 120-200. If this field displays a value lower or higher than this range, you receive a note next to the value. If the data contains many very short emails, or errors in the extracted text field, the number might be smaller than usual. If the saved search did not return long text fields, you may also see a value below the normal range. If it contains long documents, the number could be higher than usual. If this number is extremely low, under 10, it is likely the data sources for the index were set up incorrectly.

Best practices for updating a conceptual index

There may be times when you need to update your index. Depending on the update you’re making, you can save time by running an incremental population or only running a build. The following table outlines various workflows for different index updates.

Workflow	Index update
Adding new documents that: Introduce new concepts Make up more than 10% - 30% of your document population	Add documents to both the data source and training data source. Click Run, then select Incremental.
Adding new documents that: Don’t introduce new concepts Make up less than 10% - 30% of your document population	Add documents to the data source only. Click Run, then select Incremental.
Removing documents from the data source or training data source	Remove documents from the data source or training data source. Click Run, then select Incremental.
Updating noise words	Update noise words. Click Run, then select Full.
Updating extracted text For example, updating poor quality OCR text.	Update extracted text. Click Run, then select Full.
Updating filters For example, email header, repeated content.	Update filters. Click Run, then select Full.

Incremental population considerations for conceptual indexes

Incremental populations do not necessarily force Analytics to go through every stage of an index build.

When managing or updating indexes with new documents, consider the following guidelines:

• Quantity—if your index has 1 million records and you're adding 100,000 more, those documents could potentially teach a substantial amount of new information to your index. In this instance, you would update both the data source and training data source. However, if you were only adding 5,000 documents, there are not likely a lot of new concepts in relation to the rest your index. You would most likely only need to add these new documents to your data source.
• Subject matter—if the newly imported data is drastically different from the existing data, you need to train on it. If the new data is similar in nature and subject matter, then it would likely be safe to only add it to the data source.

You can run an incremental population to add or remove documents from your data source and training data source. This results in an index taking substantially less time to build, and therefore less downtime.

To perform an incremental population, click Run on the console, then choose Incremental from the modal that appears. This checks for changes in both the data source and training data source and updates the index to match.

If extracted text has changed, you have updated the noise words, or you have applied different filters, you must run a full population.

Linking repeated content filters to a conceptual index

Repeated content filters can be linked to an Analytics index either automatically, using the top filters chosen by the system, or by manually selecting individual filters. These linked filters will only apply to the currently open Analytics conceptual index; they will not be applied to structured analytics sets. These can only be linked to conceptual indexes, not classification indexes.

The recommended maximum number of linked repeated content filters per index is 1,000. This includes both manually and automatically linked filters.

Automatically linking repeated content filters

By default, when a conceptual index runs, it will automatically link the top 200 repeated content filters to the index. These are chosen by multiplying the number of occurrences times word count, then selecting the top 200 in descending order.

The following settings apply when automatically linking repeated content filters:

• The default number of linked filters is 200, and this number is controlled by the Repeated content filters to link field on the index creation screen. You can change this to any number from 0 to 1000, inclusive, when you create the index. For more information, see Index creation fields.

• If a repeated content filter has the Ready to index field set to No, it will be excluded from linking to the index, even if it would otherwise be counted as a top filter.

• If a repeated content filter has the Ready to index field set to Yes, it will be included as an automatically linked filter, even if it is not a top filter according to the calculation. It will count towards the number of filters set in the Repeated content filters to link field. For example, if the Repeated content filters to link field is set to 200, and there are 5 low-ranked filters which have Ready to index set to Yes, the index will link those 5 plus the top 195 filters for a total of 200.

• The linking process takes place every time the conceptual index runs. This means that if some repeated content filters are deleted, or if the Ready to index field values change, these changes will be reflected after the next index re-run.

If a conceptual index has both manually and automatically linked filters attached, the manually linked ones will not be changed by the index re-runs and will remain linked. They also do not count towards the number in the Repeated content filters to link field.

Manually linking repeated content filters

Use the Repeated Content Filters section on an Analytics index layout to manually link repeated content filters when the Analytics index is not open in Edit mode.

To manually link one or more existing repeated content filters to an Analytics index, perform the following steps:

1. Click on the Repeated Content Filters tab in the bottom panel of the console.
2. Click Link.
3. Find and select the repeated content filters to link to the profile. If you tagged the Ready to index field with Yes on filters you want to apply, filter for Ready to index = Yes to easily find your predetermined filters.
4. Click Apply.

For more information on repeated content and regular expression filters, see Repeated content filters.