This documentation supports the 20.02 version of BMC Helix Business Workflows.

To view the documentation for the current version, select 20.08 from the Product version menu.

Working with full-text search

In BMC Helix Business Workflows, as a case agent, you can search for different indexed entities like knowledge articles, related cases, similar cases, recommended case templates, and so on. 

The following table describes the process that occurs when you perform a full-text search:

DescriptionSystem Component

The search engine performs the search by using the keywords from the summary described for the case. Search component workflows are triggered that build a search qualification and raise a query to BMC Helix Business Workflows full-text search engine.

For knowledge articles, the full-text search displays only the published articles.

BMC Helix Business Workflows

BMC Helix Business Workflows full-text search engine is invoked to perform the search.

BMC Helix Innovation Studio full-text search engine

The search engine performs the search by using an Index that gets generated based on the indexing of various entities like knowledge articles, related cases, similar cases, recommended case templates, and so on. The search engine uses its own Relevance Algorithm to order the search results.

BMC Helix Innovation Studio full-text search engine

BMC Helix Business Workflows completes processing the search results. It also ensures row level security by eliminating records that should not be visible to the current user.

BMC Helix Innovation Studio post processing

Search results are displayed based on the relevance score or weight returned by the full-text search.

BMC Helix Business Workflows

Depending on when the information is created or modified, BMC Helix Business Workflows creates an index of knowledge articles, related cases, similar cases, recommended case templates, and so on. This is used to search the relevant entities in BMC Helix Business Workflows.

Relevance scoring in the search engine

This section describes the features related to relevance scoring in the search engine.


When you add or update any information, the search engine updates the index. This re-indexing involves analysis of the information. 

BMC Helix Business Workflows treats a record definition or an entry as a document and a field of the record marked for indexing as a field within a document. For example, a case record definition is a document and Description or Notes are fields in the document. The fields are marked for 'MFS only' or 'FTS and MFS' indexing and become the fields to be indexed by the search engine within the document.

The search engine builds a field level index.

When indexing a field, the search engine performs the following tasks:

  • Extracts keywords and calculates the number of occurrences per field and per record definition. This is called Term Frequency.
  • Uses root words. This is called Stemming.
  • Provides a dictionary for similar words or synonyms.
  • Provides a list of words which must be ignored. These words are called Stop Words.


Based on the search words provided by a case agent, the search engine uses the existing index to find matching or similar documents. It tries to find relevancy of the documents against the search terms and assigns a score to each document. The score is determined by three factors listed in the following table:

How often do the search terms appear in the document

The more frequently the term is found, the higher the score.

For example, a field containing five mentions of the same term is more likely to be relevant than a field containing just one mention.

How often do the search terms appear across the documents in the collectionThe more frequently the term is found, the lower the score. Common terms like go and find contribute less to relevance than uncommon terms like MongoDB, Outlook, and so on.
How long is the field in which the search terms appearThe shorter the field, the higher the score. If a term appears in a shorter field like Title or Keyword, the probability of it describing the whole document is higher than a body field.

In case multiple fields are indexed in the same document, the above scores are aggregated across the field level scores for each document.

There are some more factors contributing to the score like Term Proximity for phrase queries and Term Similarity in case of fuzzy queries. However, BMC Helix Business Workflows does not use phrase query by default and does not support fuzzy queries. 

Summarizing the search engine relevance

The following considerations decide the relevancy of results and consequently, the order of the documents in the result:

  • Documents containing all or maximum search words appear on the top in the search result list.
  • Matches found on rare words are better than common words.
  • Long documents are not as good as short documents.
  • Documents mentioning the search terms multiple times are good.

For more information about full-text search, see  Leveraging full-text and global search capabilities in your application Open link  and  Enabling full-text search in an application Open link .

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