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: 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 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: A method for discovering representative composite configuration item (CI) patterns in an IT system that includes a plurality of configuration items may include data mining a graph representing the IT system to extract extended frequent composite CI patterns. The method may also include clustering the extended frequent composite CI patterns into clusters based on similarity between the maximal frequent composite CI patterns. The method may further include extracting a representative composite CI pattern for each of the clusters, and using an output device, outputting the representative composite CI pattern for each of the clusters.

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.

Abstract: A method for discovering representative composite configuration item (CI) patterns in an IT system that includes a plurality of configuration items may include data mining a graph representing the IT system to extract extended frequent composite CI patterns. The method may also include clustering the extended frequent composite CI patterns into clusters based on similarity between the maximal frequent composite CI patterns. The method may further include extracting a representative composite CI pattern for each of the clusters, and using an output device, outputting the representative composite CI pattern for each of the clusters.