Abstract: A method of rendering a service graph illustrating dependencies between a frontend and a backend of an application comprises generating a plurality of frontend traces from a plurality of frontend spans and generating a plurality of backend traces from a plurality of backend spans ingested from the application. The method also comprises aggregating frontend metrics data using the plurality of frontend traces and backend metrics data using the plurality of backend traces. The method further comprises determining connection information between one or more frontend traces of the plurality of frontend traces and corresponding backend traces of the plurality of backend traces. The method also comprises rendering the service graph using the connection information and the aggregated frontend and backend metrics data.

Abstract: A method of rendering a service graph illustrating dependencies between a frontend and a backend of an application comprises generating a plurality of frontend traces from a plurality of frontend spans and generating a plurality of backend traces from a plurality of backend spans ingested from the application. The method also comprises aggregating frontend metrics data using the plurality of frontend traces and backend metrics data using the plurality of backend traces. The method further comprises determining connection information between one or more frontend traces of the plurality of frontend traces and corresponding backend traces of the plurality of backend traces. The method also comprises rendering the service graph using the connection information and the aggregated frontend and backend metrics data.

Abstract: In various implementations, a client device includes a dashboard rendering framework that processes a dashboard definition. A search query in the dashboard definition is associated with smart source tokens corresponding to lifecycle events of the search query being executed. A dynamic data source transmits the search query to retrieve field values and a token management system automatically sets the smart source tokens to be updated. As the search query is being executed, and the token management system receives updated data values and updates the smart source tokens. The dashboard rendering framework retrieves events with the applicable values for the smart source tokens and renders the dashboard according to the dashboard definition by substituting smart source tokens with the corresponding values. The dashboard rendering framework then updates one or more visualizations in real time as the token management system updates the smart source tokens in real-time.

Abstract: A method of persisting and querying Real User Monitoring (RUM) data comprises grouping together spans associated with a user-interaction with a webpage or application that are ingested during a given time duration. The method also comprises generating one or more data sets each associated with an analysis modality using the grouped spans, wherein each analysis modality extracts a different level of detail from the spans. Further, the method comprises selecting, based on a first user query, a first analysis modality for generating a response to the first user query and accessing a data set that is associated with the first analysis modality. The method also comprises generating the response to the first user query using the data set associated with the first analysis modality, wherein the first user query requests information pertaining to a performance of the application in response to the user-interaction.

Abstract: A system can collapse steps into an aggregate step to simplify analysis while maintaining underlying data that forms each of the steps collapsed into the aggregate step. The steps may or may not be related in a sequence or grouping of steps. The aggregate step may be a new step that comprises the data of the individual steps used to form the aggregate step. Alternatively, the aggregate step may be a virtual step that may reference or link to the steps used to form the aggregate step, but may not include the data itself. By forming aggregate steps, filtering and notification generation can be simplified. Further, extraneous data can be collapsed into a single aggregate step, which can be particularly advantageously when analyzing large data sets.

Abstract: Embodiments provide a deep learning framework to accurately segment liver and spleen using a convolutional neural network with both short and long residual connections to extract their radiomic and deep features from multiparametric MRI. Embodiments will provide an “ensemble” deep learning model to quantify biopsy derived liver fibrosis stage and percentage using the integration of multiparametric MRI radiomic and deep features, MRE data, as well as routinely available clinical data. Embodiments will provide a deep learning model to quantify MRE-derived liver stiffness using multiparametric MRI, radiomic and deep features and routinely-available clinical data.

Abstract: A method of rendering a service graph illustrating dependencies between a frontend and a backend of an application comprises generating a plurality of frontend traces from a plurality of frontend spans and generating a plurality of backend traces from a plurality of backend spans ingested from the application. The method also comprises aggregating frontend metrics data using the plurality of frontend traces and backend metrics data using the plurality of backend traces. The method further comprises determining connection information between one or more frontend traces of the plurality of frontend traces and corresponding backend traces of the plurality of backend traces. The method also comprises rendering the service graph using the connection information and the aggregated frontend and backend metrics data.

Abstract: A method of rendering a service graph responsive to a query comprises generating a plurality of frontend traces and a plurality of backend traces associated with an application or website. The method also comprises determining connection information between one or more frontend traces of the plurality of frontend traces and corresponding backend traces of the plurality of backend traces. Further, the method comprises consolidating the one or more frontend traces with the corresponding backend traces to form one or more end-to-end traces using the connection information. Responsive to the query, the method comprises retrieving a set of exemplary end-to-end traces from the one or more end-to-end traces and rendering a service graph in accordance with constraints applied in the query using the set of exemplary end-to-end traces and the connection information.

Abstract: A method of rendering a service graph responsive to a query comprises generating a plurality of frontend traces and a plurality of backend traces associated with an application or website. The method also comprises determining connection information between one or more frontend traces of the plurality of frontend traces and corresponding backend traces of the plurality of backend traces. Further, the method comprises consolidating the one or more frontend traces with the corresponding backend traces to form one or more end-to-end traces using the connection information. Responsive to the query, the method comprises retrieving a set of exemplary end-to-end traces from the one or more end-to-end traces and rendering a service graph in accordance with constraints applied in the query using the set of exemplary end-to-end traces and the connection information.