Security related anomalies in the data related to network entities are identified, and a risk score is assigned to each entity based on the anomalies. Visualization data is generated for a color-coded interactive visualization. Generating the visualization data includes assigning each entity to a separate polygon to be displayed concurrently on a display screen; selecting a size of each polygon to indicate one of: a number of security related anomalies associated with the entity, or a risk level assigned to the entity, where the risk level is based on the risk score of the entity, and selecting a color of each polygon to indicate the other one of: the number of security related anomalies associated with the entity, or the risk level assigned to the entity; and causing, the color-coded interactive visualization to be displayed on a display device based on the visualization data.

Described are methods, systems and computer readable media for dynamic updating of query result displays.

Described are methods, systems and computer readable media for dynamic updating of query result displays.

Aspects described herein may allow for generating a customized price rating using a machine learning algorithm. This may have the effect of improving the display of information about merchants by including customized, personalized price ratings that better reflect the tastes and preferences of a user or group of users. According to some aspects, these and other benefits may be achieved by using a machine learning model, trained to receive input corresponding to both user data and merchant data and output an indication of a customized price rating for the merchant that is specific to the user, and then to generate information about the merchant for display that includes the customized price rating.

A messaging system provides recommendations of content that account holders of the messaging system might be interested in engaging with. In order to determine what to recommend, the messaging system generates a model of account holder engagement behavior organized by type of engagement. The model parameters are trained on differences between expected engagement behavior based on past data and actual engagement behavior, and include a set of common factor matrices that are trained using data from more than on engagement type. As a consequence, engagement behavior of other account holders with respect to other types of engagements different than the one sought to be recommended serves as a partial basis for determining what engagements of the sought-after type are recommended.

A messaging system provides recommendations of content that account holders of the messaging system might be interested in engaging with. In order to determine what to recommend, the messaging system generates a model of account holder engagement behavior organized by type of engagement. The model parameters are trained on differences between expected engagement behavior based on past data and actual engagement behavior, and include a set of common factor matrices that are trained using data from more than on engagement type. As a consequence, engagement behavior of other account holders with respect to other types of engagements different than the one sought to be recommended serves as a partial basis for determining what engagements of the sought-after type are recommended.

A method including receiving a request for a report on a data set. The method also includes providing the report. The report includes a macro page having a subset of the data set. The method also includes converting the macro page into a primary tree data structure having levels. The method also includes buffering the primary tree data structure in a buffer to form a buffered tree data structure. The buffered tree data structure is buffered in a level order of the levels. The method also includes selecting a first micro page from the buffered tree data structure. The first micro page is configured for display on a user interface. The method also includes transmitting, to the user interface, the first micro page.

A search and data analysis collaboration system is described. The search and data analysis collaboration system enables users to search for and process stored data, and further includes a home page component that can help guide users embarking on data analyses; a discovery component that enables users to discover what data is available for search and analysis; a search component that enables users to efficiently search accessible data and to iterate on search queries and corresponding results; a workbooks component that enables users to create aggregated collections of data analysis artifacts; and an actions component that enables users to configure various actions to be performed in response to analyses.

A search and data analysis collaboration system is described. The search and data analysis collaboration system enables users to search for and process stored data, and further includes a home page component that can help guide users embarking on data analyses; a discovery component that enables users to discover what data is available for search and analysis; a search component that enables users to efficiently search accessible data and to iterate on search queries and corresponding results; a workbooks component that enables users to create aggregated collections of data analysis artifacts; and an actions component that enables users to configure various actions to be performed in response to analyses.

Techniques are described for providing a ML data analytics application including guided ML workflows that facilitate the end-to-end training and use of various types of ML models, where such guided workflows may also be referred to as ML “experiments.” For example, the ML data analytics application may enable users to create experiments related to prediction of numeric fields (for example, using linear regression techniques), predicting categorical fields (for example, using logistic regression), detecting numerical outliers (for example, using various distribution statistics), detecting categorical outliers (for example, using probabilistic statistics), forecasting time series data, and clustering numeric events (for example, using k-means, density-based spatial clustering of applications with noise (DBSCAN), spectral clustering, or other techniques), among other possible uses of various types of ML models to analyze data.