Abstract: In some examples, first segment of computer language text in a first rule in IT workflow data and a second segment of computer language text in a second rule in the IT workflow data may be identified. In some examples, a similarity score may be determined between the first and the second rules based on a comparison of the first segment with the second segment.

Abstract: A new approach to automatic speech recognition is disclosed. An example method include receiving a first text representing speech recognition of a phrase spoken by a user, isolating a candidate named entity from within the phrase, receiving a first phonetic representation of the candidate named entity, comparing the first phonetic representation to phonetic representations in a mapping database which map the phonetic representations to words to yield a comparison, based on the comparison, identifying a second phonetic representation in the mapping database that matches a second text in the mapping database to the second phonetic representation and replacing the candidate named entity with the second text. The approach can be used for new brands for which automatic speech recognition error rates are high.

Abstract: Examples relate to determining suggested offerings for users. The examples disclosed herein identify, a recent entity accessed by a user. The recent entity is matched with a trending topic, where the trending topic is determined by analyzing recent entities accessed by a plurality of users. A number of suggested offerings from the trending topic is calculated based on an importance score of the matched trending topic. At least one suggested offering for the trending topic is determined, based on the number of suggested offerings, from the recent entities accessed by the plurality of users. The at least one suggested offering is suggested to the user.

Abstract: Example implementations relate to generating a combined entity workflow. For example, a method for generating a combined entity workflow may include defining, using a workflow engine, a business logic block for each of a plurality of information technology (IT) entity types within an IT system, wherein the business logic block comprises a non-scripted declarative business rule, defining, for each of the plurality of entity types and using the workflow engine, a relationship between the business logic block and a model workflow for the respective entity type, and generating, using the workflow engine, a combined entity workflow for each of the plurality of entity types, by combining the business logic block and the model workflow.

Abstract: In some examples, first segment of computer language text in a first rule in IT workflow data and a second segment of computer language text in a second rule in the IT workflow data may be identified. In some examples, a similarity score may be determined between the first and the second rules based on a comparison of the first segment with the second segment.

Abstract: In some examples, a first difference may be determined across respective first and second workflow elements in a first hierarchical level of respective first and second IT workflow data. A second difference may be determined across respective third and fourth workflow elements in a second hierarchical level of the respective first and second IT workflow data. A display representing the first and second differences may be generated.

Abstract: Example implementations relate to generating a combined entity workflow. For example, a method for generating a combined entity workflow may include defining, using a workflow engine, a business logic block for each of a plurality of information technology (IT) entity types within an IT system, wherein the business logic block comprises a non-scripted declarative business rule, defining, for each of the plurality of entity types and using the workflow engine, a relationship between the business logic block and a model workflow for the respective entity type, and generating, using the workflow engine, a combined entity workflow for each of the plurality of entity types, by combining the business logic block and the model workflow.

Abstract: Examples relate to determining suggested offerings for users. The examples disclosed herein identify, a recent entity accessed by a user. The recent entity is matched with a trending topic, where the trending topic is determined by analyzing recent entities accessed by a plurality of users. A number of suggested offerings from the trending topic is calculated based on an importance score of the matched trending topic. At least one suggested offering for the trending topic is determined, based on the number of suggested offerings, from the recent entities accessed by the plurality of users. The at least one suggested offering is suggested to the user.