Abstract: Systems and methods are disclosed for determining an engagement level of a user interacting with an electronic program, comprising receiving or determining a decision node tree, each node in the decision node tree corresponding to a user decision point in the electronic program, determining a number of levels in the decision node tree, determining a user traversed count comprising a number of levels the user has traversed in the decision node tree, and determining the engagement level of the user with the electronic program based upon the user traversed count and the number of levels in the decision node tree.

Abstract: Systems and methods are disclosed for demographic prediction based on aggregated training data. The predictions are based on auditing aggregated data associated with identified properties of web requests. The audited information is based on a batch of prior web requests that have the same property and have been audited by a measurement company.

Abstract: Systems and methods are disclosed for determining an engagement level of a user interacting with an electronic program, comprising receiving or determining a decision node tree, each node in the decision node tree corresponding to a user decision point in the electronic program, determining a number of levels in the decision node tree, determining a user traversed count comprising a number of levels the user has traversed in the decision node tree, and determining the engagement level of the user with the electronic program based upon the user traversed count and the number of levels in the decision node tree.

Abstract: Embodiments of the present invention provide systems, methods, and computer storage media directed at interpreting a query related to a dataset to reduce ambiguity of the query. In embodiments, a method includes determining potential meanings of words of a natural language query related to a particular dataset. A matching graph can be generated for the words of the query and the potential meanings for the words including similarity scores for the potential meanings. The method further includes reducing ambiguity of the matching graph by assigning selected potential meanings to the words. Upon reducing ambiguity of the matching graph, the matching graph can be used to provide a response to the query. Other embodiments may be described and/or claimed herein.

Abstract: Embodiments of the present invention provide systems, methods, and computer storage media directed at transforming event logs into features for use in machine learning. In embodiments, a method may include receiving an event log for a user. The event log can indicate an occurrence of a first event associated with the user. The method can also include generating a feature value for the first event. The feature value can be indicative of an amount of time that has passed since the occurrence of the first event. Based, at least in part, on the feature value, an occurrence of a second event can be predicted utilizing a predictive model. The prediction can then be output to enable targeted content associated with the second event to be delivered to the user. Other embodiments may be described and/or claimed herein.

Abstract: Systems and methods are disclosed for determining an engagement level of a user interacting with an electronic program, comprising receiving or determining a decision node tree, each node in the decision node tree corresponding to a user decision point in the electronic program, determining a number of levels in the decision node tree, determining a user traversed count comprising a number of levels the user has traversed in the decision node tree, and determining the engagement level of the user with the electronic program based upon the user traversed count and the number of levels in the decision node tree.

Abstract: Systems and methods are disclosed for determining an engagement level of a user interacting with an electronic program, comprising receiving or determining a decision node tree, each node in the decision node tree corresponding to a user decision point in the electronic program, determining a number of levels in the decision node tree, determining a user traversed count comprising a number of levels the user has traversed in the decision node tree, and determining the engagement level of the user with the electronic program based upon the user traversed count and the number of levels in the decision node tree.

Abstract: Embodiments of the present invention provide systems, methods, and computer storage media directed at interpreting a query related to a dataset to reduce ambiguity of the query. In embodiments, a method includes determining potential meanings of words of a natural language query related to a particular dataset. A matching graph can be generated for the words of the query and the potential meanings for the words including similarity scores for the potential meanings. The method further includes reducing ambiguity of the matching graph by assigning selected potential meanings to the words. Upon reducing ambiguity of the matching graph, the matching graph can be used to provide a response to the query. Other embodiments may be described and/or claimed herein.

Abstract: Systems and methods are disclosed for demographic prediction based on aggregated training data. The predictions are based on auditing aggregated data associated with identified properties of web requests. The audited information is based on a batch of prior web requests that have the same property and have been audited by a measurement company.

Abstract: Systems and methods are disclosed for determining an engagement level of a user interacting with an electronic program, comprising receiving or determining a decision node tree, each node in the decision node tree corresponding to a user decision point in the electronic program, determining a number of levels in the decision node tree, determining a user traversed count comprising a number of levels the user has traversed in the decision node tree, and determining the engagement level of the user with the electronic program based upon the user traversed count and the number of levels in the decision node tree.

Abstract: Embodiments of the present invention provide systems, methods, and computer storage media directed at transforming event logs into features for use in machine learning. In embodiments, a method may include receiving an event log for a user. The event log can indicate an occurrence of a first event associated with the user. The method can also include generating a feature value for the first event. The feature value can be indicative of an amount of time that has passed since the occurrence of the first event. Based, at least in part, on the feature value, an occurrence of a second event can be predicted utilizing a predictive model. The prediction can then be output to enable targeted content associated with the second event to be delivered to the user. Other embodiments may be described and/or claimed herein.

Abstract: A method for predictive analytics in a semi-structured process including updating, iteratively, at least one probability of a probabilistic process model based on a completed task, wherein updating the at least one probability of the probabilistic process model includes receiving the probabilistic process model associated with a todo list including a plurality of tasks of the semi-structured process, defining a cost of each of the plurality of tasks, prioritizing the plurality of tasks according to the costs, and recommending a next task from the todo list according to a prioritization

Abstract: Systems and methods are provided for the automatic detection of different types of changes in a business process. A system includes a transformer for performing a transformation on data derived from process traces or models extracted from the processes traces to generate transformed data. The process traces are for a business process corresponding to a set of related tasks for a specified goal. Each of the models has at least a transition matrix of dimension N×N, where N is a total number of the related tasks. The system further includes a change detector for performing change detection on the transformed data to identify at least one of when a change occurs in the business process and a degree of the change.

Abstract: A method for predictive analytics in a semi-structured process including receiving traces of the semi-structured process, at least one of the traces including a document content value, determining a process model from the traces, the process model comprising tasks at nodes of the semi-structured process and embodies all possible execution sequences in the process, determining a probabilistic graph including a probability at each of the tasks of the semi-structured process advancing from one task to another task, and combining the process model and probabilistic graph to determine a probabilistic process model including probabilities and strengths of transitions between tasks.

Abstract: A method for predictive analytics in a semi-structured process including updating, iteratively, at least one probability of a probabilistic process model based on a completed task, wherein updating the at least one probability of the probabilistic process model includes receiving the probabilistic process model associated with a todo list including a plurality of tasks of the semi-structured process, defining a cost of each of the plurality of tasks, prioritizing the plurality of tasks according to the costs, and recommending a next task from the todo list according to a prioritization

Abstract: Systems and methods are provided for the automatic detection of different types of changes in a business process. A system includes a transformer for performing a transformation on data derived from process traces or models extracted from the processes traces to generate transformed data. The process traces are for a business process corresponding to a set of related tasks for a specified goal. Each of the models has at least a transition matrix of dimension N×N, where N is a total number of the related tasks. The system further includes a change detector for performing change detection on the transformed data to identify at least one of when a change occurs in the business process and a degree of the change.

Abstract: A method for predictive analytics in a semi-structured process including receiving traces of the semi-structured process, at least one of the traces including a document content value, determining a process model from the traces, the process model comprising tasks at nodes of the semi-structured process and embodies all possible execution sequences in the process, determining a probabilistic graph including a probability at each of the tasks of the semi-structured process advancing from one task to another task, and combining the process model and probabilistic graph to determine a probabilistic process model including probabilities and strengths of transitions between tasks.

Abstract: A method for beamforming is described. The method includes generating a pseudo-random unitary matrix. A first codebook is rotated with the pseudo-random unitary matrix. The method includes generating a second codebook based upon the rotated codebook and a correlation matrix. A codeword is selected from the second codebook using a channel matrix. The correlation matrix is updated based upon the selected codeword. The method includes transmitting an index of the selected codeword in the codebook. The method includes receiving the codeword index. A codebook is consulted using the codeword index to locate a codeword. Beamforming is performed based upon the located codeword. An apparatus is also described.