Abstract: Techniques are described that facilitate online monitoring of clinical data streams in association with detecting missing data and other suspicious data deviations. According to an embodiment, a computer implemented comprises receiving, by a system comprising a processor, a data stream from a plurality of different clinical data information systems configured to report defined clinical events within the data stream and recording arrival times of received events of the defined clinical events. The method further comprises detecting, by the system, data failure events associated with the data stream based on time differences between the arrival times for defined clinical events of the same type and estimated probabilities that the time differences are expected, wherein the detecting comprises estimating the probabilities that the time differences are expected using time-to-event models developed for each of the defined clinical events of the same type.

Abstract: Example apparatus, systems, and methods for image data processing are disclosed and described. An example system includes an image capturer to facilitate capture of an image. The example system includes a Doppler spectrum recorder to record a Doppler spectrum. The example system includes a study type inferrer to infer a study type associated with the Doppler spectrum by: processing the Doppler spectrum using at least one neural network to generate a first probability distribution among study type classifications; processing the image using the at least one neural network to generate a second probability distribution among the study type classifications; and combining the first probability distribution and the second probability distribution to infer a study type.

Abstract: Certain described examples are directed towards analyzing network data. The network data is processed to generate a graph data structure that has edges that are associated with communication times from the network data and nodes that are associated with computer devices. Representations of the graph data structure are generated over time. Given an indication of at least a computing device, for example as involved in anomalous activity or a security incident, the representations of the graph data structure may be used to determine further associated computer devices that are associated with the indicated device.

Abstract: Example apparatus, systems, and methods for image data processing are disclosed and described. An example system includes an image capturer to facilitate capture of an image. The example system includes a Doppler spectrum recorder to record a Doppler spectrum. The example system includes a study type inferrer to infer a study type associated with the Doppler spectrum by: processing the Doppler spectrum using at least one neural network to generate a first probability distribution among study type classifications; processing the image using the at least one neural network to generate a second probability distribution among the study type classifications; and combining the first probability distribution and the second probability distribution to infer a study type.

Abstract: A method, a computing system, and a non-transitory machine readable storage medium containing instructions for identifying relationships between entities are provided. In an example, the method includes receiving a query. The query specifies a first computing entity, a second computing entity, and a window of time. A data structure is queried based on the query to identify a set of relationship instances each corresponding to a relationship between the first computing entity and the second computing entity during the window of time. A representation of the first computing entity, the second computing entity, and the set of relationship instances is provided at a user interface.

Abstract: Certain described examples are directed towards analyzing network data. The network data is processed to generate a graph data structure that has edges that are associated with communication times from the network data and nodes that are associated with computer devices. Representations of the graph data structure are generated over time. Given an indication of at least a computing device, for example as involved in anomalous activity or a security incident, the representations of the graph data structure may be used to determine further associated computer devices that are associated with the indicated device.

Abstract: A method, a computing system, and a non-transitory machine readable storage medium containing instructions for identifying relationships between entities are provided. In an example, the method includes receiving a query. The query specifies a first computing entity, a second computing entity, and a window of time. A data structure is queried based on the query to identify a set of relationship instances each corresponding to a relationship between the first computing entity and the second computing entity during the window of time. A representation of the first computing entity, the second computing entity, and the set of relationship instances is provided at a user interface.

Abstract: Data entries can include values for each of a number of features that each have a number of permissible or possible values. The features and the permissible values thereof are ranked based on a graph constructed from the features and the permissible values. The data entries can include textual data for free-text features that do not have permissible values or possible values, and new features created based on information extracted from the textual data, where nodes and edges are added to the graph from these new features. Graphical elements corresponding to the features and graphical representations based on frequencies of the permissible values of the features can be displayed.