Abstract:
An interactive, graph-based user interaction data analysis system is disclosed. The system is configured to provide analysis and graphical visualizations of user interaction data to a system operator. In various embodiments, interactive visualizations and analyses provided by the system may be based on user interaction data aggregated across particular groups of users, across particular time frames, and/or from particular computer-based platforms and/or applications. According to various embodiments, the system may enable insights into, for example, user interaction patterns and/or ways to optimize for desired user interactions, among others. In an embodiment, the system allows an operator to analyze and investigate user interactions with content provided via one or more computer-based platforms, software applications, and/or software application editions.
Abstract:
A computer system identifies malicious Uniform Resource Locator (URL) data items from a plurality of unscreened data items that have not been previously identified as associated with malicious URLs. The system can execute a number of pre-filters to identify a subset of URLs in the plurality of data items that are likely to be malicious. A scoring processor can score the subset of URLs based on a plurality of input vectors using a suitable machine learning model. Optionally, the system can execute one or more post-filters on the score data to identify data items of interest. Such data items can be fed back into the system to improve machine learning or can be used to provide a notification that a particular resource within a local network is infected with malicious software.
Abstract:
A computer system identifies malicious Uniform Resource Locator (URL) data items from a plurality of unscreened data items that have not been previously identified as associated with malicious URLs. The system can execute a number of pre-filters to identify a subset of URLs in the plurality of data items that are likely to be malicious. A scoring processor can score the subset of URLs based on a plurality of input vectors using a suitable machine learning model. Optionally, the system can execute one or more post-filters on the score data to identify data items of interest. Such data items can be fed back into the system to improve machine learning or can be used to provide a notification that a particular resource within a local network is infected with malicious software.
Abstract:
Systems and methods are provided for analyzing entity performance. In one implementation, a method is provided that includes recognizing an identifier associated with an entity and accessing a data structure comprising information associated with a plurality of interactions. The method also comprises identifying one or more interactions of the plurality of interactions based on the recognized identifier. The method further comprises processing the information of the identified interactions to analyze a performance of the entity and providing the processed information to display the performance of the entity on a user interface.
Abstract:
An interactive, graph-based user interaction data analysis system is disclosed. The system is configured to provide analysis and graphical visualizations of user interaction data to a system operator. In various embodiments, interactive visualizations and analyses provided by the system may be based on user interaction data aggregated across particular groups of users, across particular time frames, and/or from particular computer-based platforms and/or applications. According to various embodiments, the system may enable insights into, for example, user interaction patterns and/or ways to optimize for desired user interactions, among others. In an embodiment, the system allows an operator to analyze and investigate user interactions with content provided via one or more computer-based platforms, software applications, and/or software application editions.
Abstract:
Systems and methods are provided for analyzing entity performance. In one implementation, a method is provided that includes accessing a data structure comprising a plurality of interactions associated with multiple entities. The method also includes evaluating one or more interactions of the plurality of interactions associated with a consuming entity of the multiple entities. The method further includes determining whether the one or more interactions associated with the consuming entity comprise an identified location information of the consuming entity.
Abstract:
Embodiments of the present disclosure relate to a computer system and interactive user interfaces configured to enable efficient and rapid access to multiple different data sources simultaneously, and by an unskilled user. The unskilled user may provide simple and intuitive search terms to the system, and the system may thereby automatically query multiple related data sources of different types and present results to the user. Data sources in the system may be efficiently interrelated with one another by way of a mathematical graph in which nodes represent data sources and/or portions of data sources (for example, database tables), and edges represent relationships among the data sources and/or portions of data sources. For example, edges may indicate relationships between particular rows and/or columns of various tables. The table graph enables a compact and memory efficient storage of relationships among various disparate data sources.
Abstract:
Embodiments of the present disclosure relate to a computer system and interactive user interfaces configured to enable efficient and rapid access to multiple different data sources simultaneously, and by an unskilled user. The unskilled user may provide simple and intuitive search terms to the system, and the system may thereby automatically query multiple related data sources of different types and present results to the user. Data sources in the system may be efficiently interrelated with one another by way of a mathematical graph in which nodes represent data sources and/or portions of data sources (for example, database tables), and edges represent relationships among the data sources and/or portions of data sources. For example, edges may indicate relationships between particular rows and/or columns of various tables. The table graph enables a compact and memory efficient storage of relationships among various disparate data sources.