Abstract: A method for generating samples for an anomaly detection system includes receiving events that occurred during a time series of a target metric. Each respective event includes an event attribute characterizing the respective event. The method includes generating a set of event groups for the events. Each event shares a respective attribute with one or more other events of the respective event group. For each respective event group of the set of event groups, the method includes determining an influence pattern that identifies an influence of the respective event group on the target metric. The method includes clustering the set of event groups into event clusters based on a respective influence pattern of each respective event group. Each event cluster includes one or more event groups that share a similar influence pattern. The method includes generating training samples for an anomaly detection system based on a respective event cluster.

Abstract: Described herein are techniques for generating a model based on input. In an example, a first model can be generated based on decomposing a rating matrix into a product of two matrices U and V, where U represents correlations between users and a plurality of factors and V represents correlations between items and the plurality of factors. At least some of the users and items can be grouped into groups according to the correlations. Input on one or more of the groups can be received. A second model can be generated based on the input on the one or more of the groups.

Abstract: In an example, columns in a data table may be categorized according to a data type based upon an analysis of data elements contained in each column with a grammar file. The data type of a column be may be categorized as one of a general data type and a specific data type. In addition, the grammar file may be expanded by at least one of adding a data element to the grammar file in response to a column being categorized as a specific data type and inserting a new data type to the grammar file in response to a column being categorized as a general data type.

Abstract: Data entries can include values for each of a number of features that each have a number of permissible or possible values. The features and the permissible values thereof are ranked based on a graph constructed from the features and the permissible values. The data entries can include textual data for free-text features that do not have permissible values or possible values, and new features created based on information extracted from the textual data, where nodes and edges are added to the graph from these new features. Graphical elements corresponding to the features and graphical representations based on frequencies of the permissible values of the features can be displayed.

Abstract: In an example, columns in a data table may be categorized according to a data type based upon an analysis of data elements contained in each column with a grammar file. The data type of a column be may be categorized as one of a general data type and a specific data type. In addition, the grammar file may be expanded by at least one of adding a data element to the grammar file in response to a column being categorized as a specific data type and inserting a new data type to the grammar file in response to a column being categorized as a general data type.

Abstract: Described herein are techniques for generating a model based on input. In an example, a first model can be generated based on decomposing a rating matrix into a product of two matrices U and V, where U represents correlations between users and a plurality of factors and V represents correlations between items and the plurality of factors. At least some of the users and items can be grouped into groups according to the correlations. Input on one or more of the groups can be received. A second model can be generated based on the input on the one or more of the groups.

Abstract: A computer can display a graphical user interface (GUI) that includes a control. The GUI can receive text instructions in a natural language that describe a location of the control on the GUI. The text instructions can instruct an application to perform a user interface (UI) event on the control.

Abstract: A computer displays a graphical user interface (GUI) that includes a control and receives text instructions in a natural language that describe a location of the control on the GUI. The text instructions instruct an application to perform a user interface (UI) event on the control.