Methods and systems for presenting time series for analysis. A method includes presenting a first visualization of summary information for an initial data set of a plurality of batches, presenting a filtered data set of the initial data set having a first batch identifier associated with a first batch and the second batch identifier associated with a second batch, executing a time series connector including transmitting a request to a time series application, the request comprising the first batch identifier, the second batch identifier, and the time series configuration data. The method further includes causing presentation of a user interface comprising a chart including a first plot for first time series data for the first batch identifier and a second plot for second time series data for the second batch identifier, the chart configured to the time series configuration data, and the first plot is aligned to the second plot.

Methods and systems for presenting time series for analysis. A method includes presenting a first visualization of summary information for an initial data set of a plurality of batches, presenting a filtered data set of the initial data set having a first batch identifier associated with a first batch and the second batch identifier associated with a second batch, executing a time series connector including transmitting a request to a time series application, the request comprising the first batch identifier, the second batch identifier, and the time series configuration data. The method further includes causing presentation of a user interface comprising a chart including a first plot for first time series data for the first batch identifier and a second plot for second time series data for the second batch identifier, the chart configured to the time series configuration data, and the first plot is aligned to the second plot.

Embodiments are directed to managing data visualizations. Candidate data fields from a data source may be determined based on a search expression. The candidate data fields may be displayed in the model panel. A working data model may be generated based on a portion of the candidate data fields such that the portion of the candidate data fields may be included in the working data model. Visualizations may be determined based on recommendation models and the working data model such that a portion of the visualizations may be determined based on shared data fields that may be included in the working data model and the visualizations. A working visualization may be determined based on a visualization listed in the display panel and the working data model such that data fields included in the working data model may be associated with the working visualization.

Data exploration as search over automated pre-generated plot objects can include data analytics systems with automated data mining and simplified user experience front ends. A computer-implemented method, that can be performed by the described data analytics systems, includes receiving a request for plots or plot types of a specified criteria; searching a plot object resource for plots relevant to the specified criteria, the plot object resource comprising an indexed repository of available plots; sorting and ranking the plots or plot types according to associated scores, the associated scores for each plot being based on information theoretic metrics relevant to a measure of interest; and providing plots satisfying a criteria of the sorting and the ranking to a source of the request.

Systems and techniques for indexing and/or querying a database are described herein. Multiple, large disparate data sources may be processed to cleanse and/or combine item data and/or item metadata. Further, attributes may be extracted from the item data sources. The interactive user interfaces allow a user to select one or more attributes and/or other parameters to present visualizations based on the processed data.

Systems and techniques for indexing and/or querying a database are described herein. Multiple, large disparate data sources may be processed to cleanse and/or combine item data and/or item metadata. Further, attributes may be extracted from the item data sources. The interactive user interfaces allow a user to select one or more attributes and/or other parameters to present visualizations based on the processed data.

Techniques and systems to provide a more intuitive user overview of events data by mapping unbounded incident scores to a fixed range and aggregating incident scores by different schemes. The system may detect possible malicious incidents associated with events processing on a host device. The events data may be gathered from events detected on the host device. The incident scores for incidents may be determined from the events data. The incident scores may be mapped to bins of a fixed range to highlight the significance of the incident scores. For instance, a first score mapped to a first bin may be insignificant while a second score mapped to a last bin may require urgent review. The incident scores may also be aggregated at different levels (e.g., host device, organization, industry, global, etc.) and at different time intervals to provide insights to the data.

Techniques and systems to provide a more intuitive user overview of events data by mapping unbounded incident scores to a fixed range and aggregating incident scores by different schemes. The system may detect possible malicious incidents associated with events processing on a host device. The events data may be gathered from events detected on the host device. The incident scores for incidents may be determined from the events data. The incident scores may be mapped to bins of a fixed range to highlight the significance of the incident scores. For instance, a first score mapped to a first bin may be insignificant while a second score mapped to a last bin may require urgent review. The incident scores may also be aggregated at different levels (e.g., host device, organization, industry, global, etc.) and at different time intervals to provide insights to the data.

A method, system, and computer program product for visualizing a machine learning model are provided. A confusion matrix and model performance metric data are received from a classification model. For each data point in the confusion matrix, a corresponding pixel is generated. The pixels are grouped into clusters. Each cluster represents a label in the confusion matrix. A centroid is generated for each cluster. Using the model performance metric data, a misclassification indicator arrow is generated for each misclassified data point. The misclassification indicator arrow indicates both the predicted class and the actual class. The clusters, the centroids, and the misclassification indicator arrow are displayed as a graphical visualization of the machine learning model.

A data processing server may receive a set of data objects for frequent pattern (FP) analysis. The set of data objects may be analyzed using an attribute diversity technique. For the set of data attributes of the set of data objects, the server may arrange the attributes in one or more dimensions. The server may initialize a set of centroids on data points and identify mean values of nearby data points. Based on an iteration of the mean value calculation, the server may identify a set of attributes corresponding to final mean values as being groups of similarly frequent attributes. These groups of similarly frequent attributes may be analyzed using an FP analysis procedure to identify frequent patterns of data attributes.