Abstract:
Embodiments of the present disclosure relate to user interfaces and systems that may enable dynamic and interactive access of, investigation of, and analysis of data objects stored in one or more databases. The data objects may be accessed from the one or more databases, and presented in multiple related portions of a display. In particular, the system provides a time-based visualization of data objects (and/or properties associated with the data objects) to a user such that the user may, for example, determine connections between various data objects, observe flows of information among data objects, and/or investigate related data objects.
Abstract:
Embodiments of the present disclosure relate to a data analysis system that may automatically generate memory-efficient clustered data structures, automatically analyze those clustered data structures, and provide results of the automated analysis in an optimized way to an analyst. The automated analysis of the clustered data structures (also referred to herein as data clusters) may include an automated application of various criteria or rules so as to generate a compact, human-readable analysis of the data clusters. The human-readable analyses (also referred to herein as “summaries” or “conclusions”) of the data clusters may be organized into an interactive user interface so as to enable an analyst to quickly navigate among information associated with various data clusters and efficiently evaluate those data clusters in the context of, for example, a fraud investigation. Embodiments of the present disclosure also relate to automated scoring of the clustered data structures.
Abstract:
System and method for terminating instances and autoscaling instance groups of computing platforms. For example, a method includes determining whether an instance of an instance group is identified as eligible for termination. The method further includes, in response to determining that the instance of the instance group is identified as eligible for termination, terminating the eligible instance. The terminating the eligible instance includes, in response to a runtime of the eligible instance being equal to or larger than a predetermined maximum lifetime, terminating the eligible instance. The terminating the eligible instance further includes, in response to the runtime being smaller than the predetermined maximum lifetime, detaching the eligible instance from the instance group to allow a new instance to be associated with the instance group, and in response to the eligible instance being detached from the instance group: waiting for the new instance to be associated with the instance group, and evicting each pod associated with the detached instance. The method is performed using one or more processors.
Abstract:
Embodiments of the present disclosure relate to a data analysis system that may automatically generate memory-efficient clustered data structures, automatically analyze those clustered data structures, automatically tag and group those clustered data structures, and provide results of the automated analysis and grouping in an optimized way to an analyst. The automated analysis of the clustered data structures (also referred to herein as data clusters) may include an automated application of various criteria or rules so as to generate a tiled display of the groups of related data clusters such that the analyst may quickly and efficiently evaluate the groups of data clusters. In particular, the groups of data clusters may be dynamically re-grouped and/or filtered in an interactive user interface so as to enable an analyst to quickly navigate among information associated with various groups of data clusters and efficiently evaluate those data clusters in the context of, for example, a fraud investigation.
Abstract:
Embodiments of the present disclosure relate to a data analysis system that may automatically generate memory-efficient clustered data structures, automatically analyze those clustered data structures, automatically tag and group those clustered data structures, and provide results of the automated analysis and grouping in an optimized way to an analyst. The automated analysis of the clustered data structures (also referred to herein as data clusters) may include an automated application of various criteria or rules so as to generate a tiled display of the groups of related data clusters such that the analyst may quickly and efficiently evaluate the groups of data clusters. In particular, the groups of data clusters may be dynamically re-grouped and/or filtered in an interactive user interface so as to enable an analyst to quickly navigate among information associated with various groups of data clusters and efficiently evaluate those data clusters in the context of, for example, a fraud investigation.
Abstract:
Embodiments of the present disclosure relate to a data analysis system that may automatically generate memory-efficient clustered data structures, automatically analyze those clustered data structures, and provide results of the automated analysis in an optimized way to an analyst. The automated analysis of the clustered data structures (also referred to herein as data clusters) may include an automated application of various criteria or rules so as to generate a compact, human-readable analysis of the data clusters. The human-readable analyses (also referred to herein as “summaries” or “conclusions”) of the data clusters may be organized into an interactive user interface so as to enable an analyst to quickly navigate among information associated with various data clusters and efficiently evaluate those data clusters in the context of, for example, a fraud investigation. Embodiments of the present disclosure also relate to automated scoring of the clustered data structures.
Abstract:
An event matrix may comprise labels and indicators corresponding to objects and links of an ontology. The objects and links may be determined from a plurality of data sources by a data integration system. Some of the labels may correspond to event objects, and may be arranged in a first spatial dimension at least in part on the basis of dates associated with said event objects. Other labels may correspond to non-event objects, and may be arranged in a second spatial dimension. Indicators may correspond to links between the event and non-event objects. An indicator for a particular link may be positioned with respect to the first and second spatial dimensions in accordance with the locations of the labels that correspond to the objects connected by the link.
Abstract:
An event matrix may comprise labels and indicators corresponding to objects and links of an ontology. The objects and links may be determined from a plurality of data sources by a data integration system. Some of the labels may correspond to event objects, and may be arranged in a first spatial dimension at least in part on the basis of dates associated with said event objects. Other labels may correspond to non-event objects, and may be arranged in a second spatial dimension. Indicators may correspond to links between the event and non-event objects. An indicator for a particular link may be positioned with respect to the first and second spatial dimensions in accordance with the locations of the labels that correspond to the objects connected by the link.
Abstract:
An event matrix may comprise labels and indicators corresponding to objects and links of an ontology. The objects and links may be determined from a plurality of data sources by a data integration system. Some of the labels may correspond to event objects, and may be arranged in a first spatial dimension at least in part on the basis of dates associated with said event objects. Other labels may correspond to non-event objects, and may be arranged in a second spatial dimension. Indicators may correspond to links between the event and non-event objects. An indicator for a particular link may be positioned with respect to the first and second spatial dimensions in accordance with the locations of the labels that correspond to the objects connected by the link.
Abstract:
Disclosed are data gathering and analysis systems, methods, and computer-readable storage media to facilitate an investigation process. The method includes accessing a data object representing an investigative issue as part of initiating an investigative session. The method further includes causing presentation, on a display of a device, of a user interface configured to receive user search queries and present search results for each received search query. The method further includes tracking user activity including one or more user actions performed during the investigative session. The method further includes creating a record of the user activity, and linking the record of the user activity with the data object representing the investigative issue.