Systems and methods include reception of a set of data including continuous features and a discrete feature, each continuous feature associated with a plurality of values and the discrete feature associated with a plurality of discrete values, determine, for each continuous feature, a relationship factor representing a relationship between the discrete feature and the continuous feature based on the plurality of values associated with the continuous feature and the plurality of discrete values, identify one of the continuous features associated with a largest one of the determined relationship factors, generate, for each of the other features, a correlation factor representing a correlation between the continuous feature and the identified continuous feature, determine, for each of the continuous features other than the identified continuous feature, a composite relationship score based on the relationship factor and the correlation factor associated with the feature, and present a visualization associated with the discrete feature, the identified continuous feature, and a continuous feature associated with a largest composite relationship score.

An operations management system and related method obtains user activity information representing user interactions with the operations management system responsive to an incident, the incident belonging to a category of incidents. The method represents the user activity information as an itemset. The method further processes the itemset with a mining algorithm to identify one or more items of information frequently accessed for the incident. The method yet further associates the identified one or more items of information with the category of incidents.

Various embodiments relate generally to data science and data analysis, computer software and systems, and network communications to interface among repositories of disparate datasets and computing machine-based entities configured to access datasets, and, more specifically, to a computing and data storage platform configured to provide one or more computerized tools to deploy predictive data models based on in-situ auxiliary query commands implemented in a query, and configured to facilitate development and management of data projects by providing an interactive, project-centric workspace interface coupled to collaborative computing devices and user accounts. For example, a method may include activating a query engine, implementing a subset of auxiliary instructions, at least one auxiliary instruction being configured to access model data, receiving a query that causes the query engine to access the model data, receiving serialized model data, performing a function associated with the serialized model data, and generating resultant data.

A method is provided. The method comprises: obtaining, from a data source computing system and by a data filter analytics computing system, event information associated with one or more events; determining, by the data filter analytics computing system, a dynamic data filter for the event information; generating, by the data filter analytics computing system, customer information based on filtering the event information using the dynamic data filter; and causing, by the data filter analytics computing system, display of the customer information on a user device, wherein the customer information comprises a configurable graphical representation of the customer information.

Systems and methods include determination, determine, for each of a plurality of discrete features, of statistics for each discrete value of the discrete feature based on values of a continuous feature associated with the discrete value, determination, for each discrete feature, of first summary statistics based on the statistics determined for each discrete value of the discrete feature, determination, for each discrete feature, of a dissimilarity based on the first summary statistics determined for the discrete feature and on the statistics determined for each discrete value of the discrete feature, determination of candidate discrete features of the discrete features based on the determined dissimilarities, the candidate discrete features comprising less than all of the discrete features, determination, for each of the candidate discrete features, of second summary statistics based on values of the continuous feature associated with each discrete value of the candidate discrete feature, determine of a deviation score for each of the candidate discrete features based on the second summary statistics, and presentation of the candidate discrete features based on the determined deviation scores.

A query method is provided and includes: acquiring association records, in which the association record is configured to indicate an execution area, execution time and user attribute data of an execution user, of a behavior; splitting the association record into behavior records based on attribute items included in the user attribute data of the association record, in which the behavior record is configured to indicate a mapping relationship between at least one of the attribute items and the execution area-the execution time; grouping the behavior records to determine behavior statistics information of each group; in which behavior records having the same attribute item, the same execution area and the same execution time belong to the same group; and displaying behavior statistics information of a target group in response to a query operation.

An automated system configured for streamed contents, to be self-aware in preventing fraudulent tactics, during real-time and offline usages, while communicating with its owner for accurate decision making, comprising: a content player module, and a content streaming service module; configured using a codec module to embed logic, encryptions, heuristics data, associated meta data, and management data into the content format; configured to use symmetric encryption keys, public keys, biometrics, and payload data; configured to authenticate the user and content owner; configured to request, receive, send, stream content, and analytics through a secure communication; configured to provide secure virtual communications between users and content owners; configured to use a call-home data, to enable the content and content owner to communicate and update one another securely; Configured to provide real-time, and offline, fraud prevention heuristics using artificial intelligence.

A non-transitory computer-readable recording medium records an index generation program for causing a computer to execute processing of: inputting data which is described by a combination of an item and a value; and generating index information regarding an appearance position of each of the item and the value for each of the item and the value which are included in the data.

A big-data view integration platform generates integration guided user interfaces (GUIs). A first edge node ingests push-based and pull-based data from a plurality of platform services, which include legacy and non-legacy services having incompatible communication protocols. An event-based queue receives from the first edge node a plurality of queue events as indirect push-based data. A second set of queue events includes direct push-based data as received directly from a non-legacy platform service. A conformity component integrates the push-based data, the pull-based data, and the plurality of queue events into integration data having an enhanced integration format. A view integration component generates a plurality of data views from the integration data. A second edge node exposes the plurality of data views via an access services application programming interface (API). A new service execution component accesses the access services API to generate integration GUIs based on the data views.

A computing server may continuously update a set of nodes that are addable to a data tree based on past interactions of the user with one or more nodes. The computing server may track a recently interacted set of interacted nodes with which the user has interacted within a number of past interactions. The computing server may select a pool of candidate nodes based on the recently interacted set. At least one of the candidate nodes is within a domain boundary of one of the interacted nodes that is in the recently interacted set. The domain boundary may be determined by the degree of relationship. The computing server may present one or more candidate nodes in the pool as a version of the continuously updated set of nodes. The computing server may update the pool of candidate nodes as additional interactions performed by the user updates the recently interacted set.