Abstract: A process model can be modified to be more precise by unrolling loops of the process model and evaluating or using the process model with the loops unrolled. After determining loops in a process model, sequential forward path executions of each loop identified in an input process model are counted within each trace of an event log. For each loop, a greatest common divisor (gcd) of the sequential forward path execution counts is determined. An intermediate process model is then created with the loops unrolled according to the respective gcd(s). The event log is then (re)played with the intermediate process model to identify traversed elements of the process model. Elements of the intermediate process model that were not traversed are removed to yield a more precise process model.

Abstract: A method includes performing, by a processor: receiving a first document, the first document comprising a first plurality of sub-documents that are related to one another in a first time sequence; converting the first plurality of sub-documents to a vector format to generate a vectorized document that encodes a probability distribution of words in the document and transition probabilities between words; detecting a plurality of topics within the vectorized document, the plurality of topics being related to one another in the first time sequence; applying a process discovery algorithm to the plurality of topics to generate a model that is representative of relationships between the plurality of topics; receiving a second document containing subject matter related to a course of action, the second document comprising a second plurality of sub-documents that are related to one another in a second time sequence; using the model to generate a classification for the second document; and adjusting the course of acti

Abstract: A network device associated with a database management system receives information associated with a customer support ticket. Based on information in the database management system, a direct relationship between the received customer support ticket and a customer support ticket in the database may be determined. A graph including nodes representing customer support tickets is generated based on information in the database. Edge prediction is performed on the graph to derive relationships among the nodes in the graph. A predictive relationship between customer support tickets is derived. A relationship data set based on the direct relationship between the customer support tickets and based on the predictive relationship between the customer support tickets is generated. The relationship data set associated with the customer support ticket is communicated to the user device.

Abstract: A method includes performing, by a processor: receiving a document containing subject matter related to a course of action, the document comprising a plurality of sub-documents that are related to one another in a time sequence, converting the document to a vector format to generate a vectorized document that encodes a probability distribution of words in the document and transition probabilities between words, applying a machine learning algorithm to the vectorized document to generate an estimated vectorized document, associating the estimated vectorized document with a reference document; predicting future subject matter contained in a future sub-document of the document based on the reference document, and adjusting the course of action responsive to predicting the future subject matter.

Abstract: In a computer server, a ticket element generated by an issue tracking system responsive to a client request is received, where the ticket element includes a data field including data indicative of an attribute. A database including state data stored therein is accessed responsive to receipt of the ticket element. The state data relates a plurality of states that are specific to the attribute. One of the plurality of states related by the state data is identified as corresponding to a future state of the attribute based on a current state of the attribute, and a future probability of escalation of the ticket element is computed based on the future state of the attribute responsive to identification of the one of the plurality of states as corresponding thereto. An action indicator is provided in response to the client request.

Abstract: Current behavior can be evaluated to efficiently identify behavioral anomalies with process models of different scopes and/or different degrees of precision. For meaningful behavioral evaluation of an actor (i.e., a user or a device), these multiple process models are constructed with different sets of event logs of a system. A model of a scope of an individual actor and a model of a scope of a group of actors are constructed and used for evaluation. These models of different scope expand “normal” behavior of an actor to include behavior of the group of actors. Although these process models of different scopes likely have different precision, additional models of different precision and/or different scopes can be constructed and used for behavioral evaluation. These different process models allow for behavioral variation within relevant groups of actors.