Abstract: A method for finding historically similar incidents is disclosed.

Abstract: A computer-implemented method for determining a signature embedding vector for information technology events. The method including: receiving a current data object representing an occurrence of an information technology event associated with a configurable item; obtaining a graph database of logical associations of the configurable item; applying a clustering algorithm to the graph database to determine a subset of graphs; extracting associated data of the subset of graphs; applying an embedding model to the extracted associated data to determine an embedding vector for each subset of graphs; aggregating the embedding vector for each subset of graphs to determine a signature embedding vector; and utilizing the signature embedding vector to perform further analysis of the current data object.

Abstract: A computer-implemented method for managing information technology event data in a graph database, the method including: obtaining a graph database, the graph database including a set of nodes and set of edges that connect the set of nodes; receiving a first data, the first data object, the first data object and second data object representing information technology data; processing the first data object, wherein processing the first data object includes assigning a first identification field to the first data object and identifying additional fields of the first data object; determining the first data object does not exist as a node in the graph database; creating a first node for the first data object and assigning the first identification field to the first node; creating a first edge connecting the first data object to a first related node from the set of nodes of the graph database.

Abstract: A method for finding historically similar incidents is disclosed.

Abstract: A method for finding historically similar incidents includes receiving a plurality of historical data objects corresponding to a plurality of previous incidents, each of the plurality of historical data objects indicating an occurrence of a previous incident and including a historical resolution text description; generating a historical embedding of each of the plurality of historical data objects; extracting noun phrases from each of the historical resolution text descriptions; applying topic modeling to the extracted noun phrases; receiving a current data object indicating an occurrence of a current incident associated with a configurable item, the current data object including an incident description; generating a current embedding of the current data object; extracting noun phrases from the current embedding; applying topic modeling to the extracted noun phrase of the current data object; and identifying a set of historically similar incidents by applying a Euclidean distance formula to the current embedd

Abstract: A method for finding historically similar incidents is disclosed. The method may include obtaining a plurality of historical embedding vectors for a plurality of historical data objects; receiving a current data object indicating an occurrence of a current incident associated with a configurable item, the current data object being associated with a line of business data object; determining a configurable item graph including one or more subtrees, wherein the configurable item graph is a graph of logical associations of the line of business data object and related IT operation events; generating embeddings for each of the one or more subtrees; computing a feature embedding vector for the current data object by averaging the embeddings for each of the one or more subtrees; and determining a set of historically similar incidents by applying a Euclidean distance formula to the feature embedding vector and the plurality of historical embedding vectors.

Abstract: A computer-implemented method for determining a signature embedding vector for information technology events. The method including: receiving a current data object representing an occurrence of an information technology event associated with a configurable item; obtaining a graph database of logical associations of the configurable item; applying a clustering algorithm to the graph database to determine a subset of graphs; extracting associated data of the subset of graphs; applying an embedding model to the extracted associated data to determine an embedding vector for each subset of graphs; aggregating the embedding vector for each subset of graphs to determine a signature embedding vector; and utilizing the signature embedding vector to perform further analysis of the current data object.

Abstract: A computer-implemented method for determining related information technology event data by applying temporal associations includes: receiving a data object including a short description indicating an occurrence of a current incident associated with a configurable item; applying a first machine learning model to the short description to determine a first cluster associated with the data object; receiving a plurality of data object indicating occurrences of current incidents that occurred within a set period of time of the current incident, the plurality of data objects including a plurality of short descriptions; applying the first machine learning model to the short descriptions to determine associated clusters; determining, based on association rules and the associated clusters, a set of similar data objects from the plurality of data objects; assigning a set of associations between the data object and each of the set of similar data objects; and storing the set of associations.

Abstract: A method for processing batch and live streaming data is disclosed. The method may include receiving a first data object from a data source, the first data object being of information technology event data; processing the first data object; determining, by utilizing a message queue, a first identifier for the first data object; storing the processed first data object in a data sink layer; transferring the first data object to a data warehouse configured to apply an analytic algorithm to the first data object, wherein the transfer is based on the first identifier; determining, by applying the analytic algorithm to the first data object, a third data object; transferring the first data object and third data object to an in-memory database; retrieving and presenting the first data object and/or third data object from the in-memory database to present in a presentation layer of a user interface.

Abstract: A computer-implemented method for determining a message in a message queue of an information technology system. The method may include receiving an information technology event data object in an object-relational database management system, the information technology event data object including metadata; determining a change to the object-relational database has occurred by implementing a change data capture platform; determining, using the change data capture platform, a log file of the change to the first table, the log file including the metadata from the information technology event data object; deploying the log file to a message queue; and processing, through a consumer module, the log file of the message queue to perform an action.

Abstract: Computer implemented system and method for routing and processing real-time information event technology data.

Abstract: A computer-implemented method for automatically retraining a machine learning system, the method including: receiving a plurality of data objects, the plurality of data objects corresponding to information technology event data and representing an occurrence of an event; processing the plurality of data objects; evaluating whether to perform hyperparameter tuning of a first model of a first machine learning system based on characteristics of the plurality of data objects; training the first model of the first machine learning system based on the processed plurality of data objects; storing a retrained model of the first machine learning system.

Abstract: A computer-implemented method for tracking a data object in a data pipeline. The method may include receiving a data object from an data source; assigning a record number to the data object; determining a first tracking stamp for the data object; broadcasting the first tracking stamp to a channel of a record tracking queue; initiating a record in the record tracking queue for the data object, the record including the record number, a station array, a station code, and a timestamp array, wherein the first tracking stamp updates the station array, station code, and timestamp array; transferring the data object to a second component; determining a second tracking stamp for the data object, the second tracking stamp including the record number; broadcasting the second tracking stamp to the channel; and updating the record with the second tracking stamp to indicate that the data object was received by the second component.