Abstract: Systems, methods, and non-transitory computer-readable media can receive from a user computing device raw stack trace information associated with an exception that has occurred within a computing application on the user computing device. Debugging information associated with the computing application is retrieved. Human-readable stack trace information is generated based on the raw stack trace information and the debugging information.

Abstract: The present disclosure provides a method, system and non-transient computer readable medium for evaluating system behaviour by deriving a statistical distance between each entity in a multi-entity system, and summing the statistical distance to each other entity to create a ranked abnormality score for each entity in the system.

Abstract: In some examples, a plurality of multi-dimensional data samples representing respective behaviors of entities in a computing environment are sorted, where the sorting is based on values of dimensions of each respective multi-dimensional data sample. For a given multi-dimensional data sample, a subset of the plurality of multi-dimensional data samples is selected based on the sorting. An anomaly indication is computed for the given multi-dimensional data sample based on applying a function on the multi-dimensional data samples in the subset. It is determined whether the given multi-dimensional data sample represents an anomalous entity in the computing environment based on the computed anomaly indication.

Abstract: The present disclosure relates to an interactive system that manages analytics contexts through a series of analytics interactions. The disclosed interactive system receives a selection of an analytics interaction from a user during an interactive analytics session. Then, the system generates a series of analytics interactions by the user during the interactive analytics session. Each analytics interaction represents an analytics context that comprises an analytics interaction, a result, and a reference analytics context. Moreover, the system manages a plurality of analytics contexts by selecting the reference analytics context from previous analytics interactions, or by navigating to a different analytics context, or by deactivating a user-selected analytics context, and presents to the user the series of analytics interactions with the result corresponding to both the selection of the analytics interaction and the reference analytics context.

Abstract: The present disclosure provides a method, system and non-transient computer readable medium for evaluating system behaviour by deriving a statistical distance between each entity in a multi-entity system, and summing the statistical distance to each other entity to create a ranked abnormality score for each entity in the system.

Abstract: In some examples, a plurality of multi-dimensional data samples representing respective behaviors of entities in a computing environment are sorted, where the sorting is based on values of dimensions of each respective multi-dimensional data sample. For a given multi-dimensional data sample, a subset of the plurality of multi-dimensional data samples is selected based on the sorting. An anomaly indication is computed for the given multi-dimensional data sample based on applying a function on the multi-dimensional data samples in the subset. It is determined whether the given multi-dimensional data sample represents an anomalous entity in the computing environment based on the computed anomaly indication.

Abstract: The present disclosure relates to an interactive system that manages analytics contexts through a series of analytics interactions. The disclosed interactive system receives a selection of an analytics interaction from a user during an interactive analytics session. Then, the system generates a series of analytics interactions by the user during the interactive analytics session. Each analytics interaction represents an analytics context that comprises an analytics interaction, a result, and a reference analytics context. Moreover, the system manages a plurality of analytics contexts by selecting the reference analytics context from previous analytics interactions, or by navigating to a different analytics context, or by deactivating a user-selected analytics context, and presents to the user the series of analytics interactions with the result corresponding to both the selection of the analytics interaction and the reference analytics context.

Abstract: An interactive system to detect malware is provided to interactively analyze web proxy log data. The log data is progressively processed to compute analytics for different context settings. The system has a context module, an interaction module and a plurality of analytics modules. When a change of the context setting (filter, weights etc.) is requested, the processing and calculation of analytics for the current context setting is paused and subsequently restarted for the now changed context setting. An analytics interface provided via a graphical user interface is updated upon the change of context settings.