Abstract: Word vectors are multi-dimensional vectors that represent words in a corpus of text and that are embedded in a semantically-encoded vector space; paragraph vectors extend word vectors to represent, in the same semantically-encoded space, the overall semantic content and context of a phrase, sentence, paragraph, or other multi-word sample of text. Word and paragraph vectors can be used for sentiment analysis, comparison of the topic or content of samples of text, or other natural language processing tasks. However, the generation of word and paragraph vectors can be computationally expensive. Accordingly, word and paragraph vectors can be determined only for user-specified subsets of fields of incident reports in a database.

Abstract: A network system may include a plurality of trainer devices and a computing system disposed within a remote network management platform. The computing system may be configured to: receive, from a client device of a managed network, information indicating (i) training data that is to be used as basis for generating a machine learning (ML) model and (ii) a target variable to be predicted using the ML model; transmit an ML training request for reception by one of the plurality of trainer devices; provide the training data to a particular trainer device executing a particular ML trainer process that is serving the ML training request; receive, from the particular trainer device, the ML model that is generated based on the provided training data and according to the particular ML trainer process; predict the target variable using the ML model; and transmit, to the client device, information indicating the target variable.

Abstract: A network system may include a plurality of trainer devices and a computing system disposed within a remote network management platform. The computing system may be configured to: receive, from a client device of a managed network, information indicating (i) training data that is to be used as basis for generating a machine learning (ML) model and (ii) a target variable to be predicted using the ML model; transmit an ML training request for reception by one of the plurality of trainer devices; provide the training data to a particular trainer device executing a particular ML trainer process that is serving the ML training request; receive, from the particular trainer device, the ML model that is generated based on the provided training data and according to the particular ML trainer process; predict the target variable using the ML model; and transmit, to the client device, information indicating the target variable.

Abstract: An example embodiment may involve a machine learning model representing relationships between a dependent variable and a plurality of n independent variables. The dependent variable may be a function of the n independent variables, where the n independent variables are measurable characteristics of computing devices, and where the dependent variable is a predicted behavior of the computing devices. The embodiment may also involve obtaining a target value of the dependent variable, and separating the n independent variables into n?1 independent variables with fixed values and a particular independent variable with an unfixed value. The embodiment may also involve performing a partial inversion of the function to produce a value of the particular independent variable such that, when the function is applied to the value of the particular independent variable and the n?1 independent variables with fixed values, the dependent variable is within a pre-defined range of the target value.

Abstract: A machine learning classifier may classify observations into one or more of i categories, and may be configured to: receive test data that includes j observations, each associated with a respective ground truth category, and produce output that provides, for each particular observation of the j observations, a set of i probabilities, one probability for each of the i categories. For each particular confidence threshold in k confidence thresholds, a computing device may: reclassify, into a null category, any of the j observations for which all of the set of i probabilities are less than the particular confidence threshold, and determine a respective precision value and a respective coverage value for a particular category of the i categories. A specific confidence threshold in the k confidence thresholds may be selected, and further observations may be reclassified into the null category in accordance with the specific confidence threshold.

Abstract: A machine learning classifier may classify observations into one or more of i categories, and may be configured to: receive test data that includes j observations, each associated with a respective ground truth category, and produce output that provides, for each particular observation of the j observations, a set of i probabilities, one probability for each of the i categories. For each particular confidence threshold in k confidence thresholds, a computing device may: reclassify, into a null category, any of the j observations for which all of the set of i probabilities are less than the particular confidence threshold, and determine a respective precision value and a respective coverage value for a particular category of the i categories. A specific confidence threshold in the k confidence thresholds may be selected, and further observations may be reclassified into the null category in accordance with the specific confidence threshold.

Abstract: A network system may include a plurality of trainer devices and a computing system disposed within a remote network management platform. The computing system may be configured to: receive, from a client device of a managed network, information indicating (i) training data that is to be used as basis for generating a machine learning (ML) model and (ii) a target variable to be predicted using the ML model; transmit an ML training request for reception by one of the plurality of trainer devices; provide the training data to a particular trainer device executing a particular ML trainer process that is serving the ML training request; receive, from the particular trainer device, the ML model that is generated based on the provided training data and according to the particular ML trainer process; predict the target variable using the ML model; and transmit, to the client device, information indicating the target variable.

Abstract: Systems and methods for using a mathematical model based on historical information to automatically schedule and monitor work flows are disclosed. Prediction methods that use some variables to predict unknown or future values of other variables may assist in reducing manual intervention when addressing incident reports or other task-based work items. For example, work items that are expected to conform to a supervised model built from historical customer information. Given a collection of records in a training set, each record contains a set of attributes with one of the attributes being the class. If a model can be found for the class attribute as a function of the values of the other attributes, then previously unseen records may be assigned a class as accurately as possible based on the model. A test data set is used to determine model accuracy prior to allowing general use of the model.

Abstract: A network system may include a plurality of trainer devices and a computing system disposed within a remote network management platform. The computing system may be configured to: receive, from a client device of a managed network, information indicating (i) training data that is to be used as basis for generating a machine learning (ML) model and (ii) a target variable to be predicted using the ML model; transmit an ML training request for reception by one of the plurality of trainer devices; provide the training data to a particular trainer device executing a particular ML trainer process that is serving the ML training request; receive, from the particular trainer device, the ML model that is generated based on the provided training data and according to the particular ML trainer process; predict the target variable using the ML model; and transmit, to the client device, information indicating the target variable.

Abstract: An example embodiment may involve a machine learning model representing relationships between a dependent variable and a plurality of n independent variables. The dependent variable may be a function of the n independent variables, where the n independent variables are measurable characteristics of computing devices, and where the dependent variable is a predicted behavior of the computing devices. The embodiment may also involve obtaining a target value of the dependent variable, and separating the n independent variables into n?1 independent variables with fixed values and a particular independent variable with an unfixed value. The embodiment may also involve performing a partial inversion of the function to produce a value of the particular independent variable such that, when the function is applied to the value of the particular independent variable and the n?1 independent variables with fixed values, the dependent variable is within a pre-defined range of the target value.

Abstract: An example embodiment may involve a machine learning model representing relationships between a dependent variable and a plurality of n independent variables. The dependent variable may be a function of the n independent variables, where the n independent variables are measurable characteristics of computing devices, and where the dependent variable is a predicted behavior of the computing devices. The embodiment may also involve obtaining a target value of the dependent variable, and separating the n independent variables into n?1 independent variables with fixed values and a particular independent variable with an unfixed value. The embodiment may also involve performing a partial inversion of the function to produce a value of the particular independent variable such that, when the function is applied to the value of the particular independent variable and the n?1 independent variables with fixed values, the dependent variable is within a pre-defined range of the target value.

Abstract: A computer implemented system for automating the generation of an analytic model includes a processor configured to process a plurality of data sets. Each data set includes values for a plurality of variables. A time-stamping module is configured to derive values for a plurality of elapsed-time variables for each data set, and the plurality of variables and plurality of elapsed-time variables are included in a plurality of model variables. A model generator is configured to create a plurality of comparison analytic models each based on a different subset of model variables. Each comparison analytic model is configured to operate on new data sets associated with current leads, and to output a likelihood of successfully closing an associated transaction. A model testing module is configured to select an operational analytic model from among the comparison analytic models based on a quality metric.

Abstract: A computer implemented system for automating the generation of an analytic model includes a processor configured to process a plurality of data sets. Each data set includes values for a plurality of variables. A time-stamping module is configured to derive values for a plurality of elapsed-time variables for each data set, and the plurality of variables and plurality of elapsed-time variables are included in a plurality of model variables. A model generator is configured to create a plurality of comparison analytic models each based on a different subset of model variables. Each comparison analytic model is configured to operate on new data sets associated with current opportunities, and to output a likelihood of successfully closing each current opportunity. A model testing module is configured to select an operational analytic model from among the comparison analytic models based on a quality metric.

Abstract: The present invention envisages a system and method for automating the generation of business decision analytic models. The system uses a plurality of predictor variables stored in a plurality of data sets, to automatically create a business decision analytic model. The system includes a processor configured to process the data sets and determine the total number of records present in each of the data sets and the number of columns containing only numerical values. The processor selects a column containing only numerical values, from a dataset under consideration, and counts the number of unique numerical values in the selected column, and the total number of records present in the selected column. The two counts are compared and the selected column is transformed using a non-linear transformation to obtain a column of transformed values. The transformed values and corresponding time stamps are utilized for the purpose of model generation.

Abstract: The present invention envisages a system and method for automating the generation of business decision analytic models. The system uses a plurality of predictor variables stored in a plurality of data sets, to automatically create a business decision analytic model. The system includes a processor configured to process the data sets and determine the total number of records present in each of the data sets and the number of columns containing only numerical values. The processor selects a column containing only numerical values, from a dataset under consideration, and counts the number of unique numerical values in the selected column, and the total number of records present in the selected column. The two counts are compared and the selected column is transformed using a non-linear transformation to obtain a column of transformed values. The transformed values and corresponding time stamps are utilized for the purpose of model generation.

Abstract: The present invention envisages a system and method for automating the generation of business decision analytic models. The system uses a plurality of predictor variables stored in a plurality of data sets, to automatically create a business decision analytic model. The system includes a processor configured to process the data sets and determine the total number of records present in each of the data sets and the number of columns containing only numerical values. The processor selects a column containing only numerical values, from a dataset under consideration, and counts the number of unique numerical values in the selected column, and the total number of records present in the selected column. The two counts are compared and the selected column is transformed using a non-linear transformation to obtain a column of transformed values. The transformed values and corresponding time stamps are utilized for the purpose of model generation.

Abstract: Embodiments of the present invention generally relate to a method for service configuration management. One embodiment of the present invention includes providing a configuration question and capturing an answer to the question. The embodiment also includes linking the answer to a requirement, tracing the requirement to a potential solution, and storing integrated information. Further, the integrated information of the embodiment includes the requirement, the potential solution, and a link between the requirement and the potential solution.

Abstract: The present invention envisages a system and method for automating the generation of business decision analytic models. The system uses a plurality of predictor variables stored in a plurality of data sets, to automatically create a business decision analytic model. The system includes a processor configured to process the data sets and determine the total number of records present in each of the data sets and the number of columns containing only numerical values. The processor selects a column containing only numerical values, from a dataset under consideration, and counts the number of unique numerical values in the selected column, and the total number of records present in the selected column. The two counts are compared and the selected column is transformed using a non-linear transformation to obtain a column of transformed values. The transformed values and corresponding time stamps are utilized for the purpose of model generation.

Abstract: To implement at least one functionality, a template having one or more logic components corresponding to the at least one functionality is provided. The template and a data collection are accessed to load the one or more logic components and data into a closure document. The closure document is provided to enable updating of data in the closure document using the one or more logic components.

Abstract: Embodiments of the present invention relate to a system for correlating configurations, comprising a model layer having a generic model library, a control layer having generic control logic implemented in terms of the generic model library, and a view layer having reusable views and common tools that operate upon generic interfaces and constructs defined by the generic model library, wherein the generic model library, generic control logic, and reusable views and commons tools are defined to support a general functionality.