Abstract: Examples relate to detecting an abnormality. The examples disclosed herein enable receiving, from a first user, a first request to perform a first transaction on at least one data record. A plurality of transactions originated from the first request may be organized in a first hierarchical tree-based data structure having multiple depth levels. The data structure may comprise a root node representing the first transaction and a leaf node representing a second transaction. The examples further enable detecting the abnormality based on at least one parameter where the at least one parameter comprises a size of the data structure and a depth level associated with the leaf node.

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: Examples relate to detecting an abnormality. The examples disclosed herein enable receiving, from a first user, a first request to perform a first transaction on at least one data record. A plurality of transactions originated from the first request may be organized in a first hierarchical tree-based data structure having multiple depth levels. The data structure may comprise a root node representing the first transaction and a leaf node representing a second transaction. The examples further enable detecting the abnormality based on at least one parameter where the at least one parameter comprises a size of the data structure and a depth level associated with the leaf node.

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: Examples relate to detecting an abnormality. The examples disclosed herein enable receiving, from a first user, a first request to perform a first transaction on at least one data record. A plurality of transactions originated from the first request may be organized in a first hierarchical tree-based data structure having multiple depth levels. The data structure may comprise a root node representing the first transaction and a leaf node representing a second transaction. The examples further enable detecting the abnormality based on at least one parameter where the at least one parameter comprises a size of the data structure and a depth level associated with the leaf node.

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 one implementation, a workflow system can include a storage engine and a merger engine. The storage engine maintains a restricted workflow part on a first storage resource and maintains a customizable workflow part on a second storage resource. The merger engine retrieves the restricted workflow part based on the product version and merge the restricted workflow part with the customizable workflow part associated with the restricted workflow part.

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: 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: 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 one implementation, a workflow system can include a storage engine and a merger engine. The storage engine maintains a restricted workflow part on a first storage resource and maintains a customizable workflow part on a second storage resource. The merger engine retrieves the restricted workflow part based on the product version and merge the restricted workflow part with the customizable workflow part associated with the restricted workflow part.

Abstract: Examples relate to detecting an abnormality. The examples disclosed herein enable receiving, from a first user, a first request to perform a first transaction on at least one data record. A plurality of transactions originated from the first request may be organized in a first hierarchical tree-based data structure having multiple depth levels. The data structure may comprise a root node representing the first transaction and a leaf node representing a second transaction. The examples further enable detecting the abnormality based on at least one parameter where the at least one parameter comprises a size of the data structure and a depth level associated with the leaf node.