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: A predefined hierarchical service tree can be stored that includes a top at a service category definition level and a bottom at a level of a number of devices, each of the number of devices selected to perform a specific service function. A sequential progression can be enforced through the predefined hierarchical service tree to perform a service.

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: Systems, methods, and other embodiments associated with graph transformation are described. One example method includes transforming a base graph into a folded graph as a function of a folding query.

Abstract: Examples disclosed herein relate, among other things, to a first computing device including a server optimization module communicatively coupled to a server application. The server optimization module may receive, from a second computing device, a resource request identifying a resource, and send to the second computing device a packet list identifying a set of packets associated with the resource, where each packet describes differences between different versions of the resource. The server optimization module may also receive from the second computing device a set of packet requests, each packet request identifying a packet from the set of packets, and for each packet in the set of packets, send the packet to the second computing device based on a determination that the packet has been updated since the packet has been last sent to the second computing device.

Abstract: Systems, methods, and other embodiments associated with virtual topological queries are described. One example method includes executing a first topological query on a stored graph to generate a first result graph. The example method may also include manipulating the first result graph to generate a virtual query result graph as a function of a second topological query. The example method may also include providing the virtual query result graph.

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: Examples disclosed herein relate, among other things, to a first computing device including a server optimization module communicatively coupled to a server application. The server optimization module may receive, from a second computing device, a resource request identifying a resource, and send to the second computing device a packet list identifying a set of packets associated with the resource, where each packet describes differences between different versions of the resource. The server optimization module may also receive from the second computing device a set of packet requests, each packet request identifying a packet from the set of packets, and for each packet in the set of packets, send the packet to the second computing device based on a determination that the packet has been updated since the packet has been last sent to the second computing device.

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 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: Updating service level targets may include aggregating a plurality of actions performed over each of a plurality of information technology service management (ITSM) records in a single service level target (SLT) database table since a prior update. Examples may include applying the aggregated plurality of actions to the SLT database table in a bulk operation. Examples may further include updating, at a predetermined elapsed time, each of the plurality of ITSM records in the SLT database table.

Abstract: A predefined hierarchical service tree can be stored that includes a top at a service category definition level and a bottom at a level of a number of devices, each of the number of devices selected to perform a specific service function. A sequential progression can be enforced through the predefined hierarchical service tree to perform a service.

Abstract: Systems, methods, and other embodiments associated with virtual topological queries are described. One example method includes executing a first topological query on a stored graph to generate a first result graph. The example method may also include manipulating the first result graph to generate a virtual query result graph as a function of a second topological query. The example method may also include providing the virtual query result graph.

Abstract: Systems, methods, and other embodiments associated with graph transformation are described. One example method includes transforming a base graph into a folded graph as a function of a folding query.