Abstract: A method including receiving events from different data sources for a service automatically executing in an enterprise system. A first event is enriched by providing the first event with first metadata that associates the first event with a first application used by the service. The first event is assigned to a time slice associated with the first application. A second event is enriched in a similar manner. A correlation graph of nodes and edges is built using the enriched events, with nodes representing the events and edges indicating relationships between the edges. A third event indicating a fault in the first application associated with the first node is received. The source of the error for the third event is identified using the second updated correlation graph and the time slice. The source of error is then mitigated.

Abstract: A processor may generate a cluster of at least two (or any other minimal cluster size) of a plurality of vectors using a density-based clustering algorithm. The generating may include optimizing at least one hyperparameter of the density-based clustering algorithm by minimizing a loss function to increase a cluster count and decrease a cluster variance. The processor may select a vector closest to the center of the cluster as a representative vector.

Abstract: Systems and methods are provided to structure event description data.

Abstract: A method implements last mile churn prediction. The method includes retrieving data during a user session in response to a trigger. The data includes a list of screen identifiers and a corresponding list of timestamps. The method further includes converting the list of timestamps to a list of time deltas. The list of time deltas includes a time delta that identifies an amount of time between two timestamps of the list of timestamps. The method further includes generating a churn risk from the list of screen identifiers and the list of time deltas. The churn risk identifies a probability that the user session will be terminated. The method further includes transmitting an intervention to intervene in the user session based on the churn risk.

Abstract: A system includes a categorization application including a personal categorization model for execution on a user device, the personal categorization model configured to assign a personal assigned category and a personal assigned categorization confidence value to transaction record. The system also includes a global categorization model executing on a central server. The global categorization model is configured to receive the transaction record from the user device, and process the transaction record through the global categorization model to obtain a global assigned category and a global assigned categorization confidence value. The system is configured to select a transaction category from the personal assigned category and the global assigned category using the personal assigned categorization confidence value and the global assigned categorization confidence value, and categorize the transaction with the transaction category.

Abstract: Systems and methods for updating a data catalog in real-time with changes to metadata are disclosed. An example method may be performed by one or more processors of a system and include detecting changes to metadata in a metadata store, generating an event queue indicating a plurality of change events associated with system objects based on the changes, each of the plurality of change events indicating a number of event attributes and a system object associated with the change event, identifying recent events among the plurality of change events, identifying relevant events among the recent events, extracting unique identifiers from the relevant events, identifying priority objects among the system objects, obtaining, from the metadata store, current metadata for each of the priority objects, and updating information associated with the priority objects in a data catalog, the updating occurring in real-time with the changes to the metadata in the metadata store.

Abstract: Certain aspects of the present disclosure provide techniques for entering user credentials through a proxy. One example method generally includes receiving, at a user device, a push request for user data from a cloud server and receiving a request file from an aggregation system. The method further includes injecting user credentials stored on the user device into the request file, wherein when injected the user credentials replace at least one dummy entry of the request file, and transmitting the request file to a data source associated with the request file. The method further includes receiving user data from the data source and transmitting the user data to the aggregation system.

Abstract: In one embodiment a finite rank deep kernel learning method includes: receiving a training dataset; forming a plurality of training data subsets from the training dataset; for each respective training data subset of the plurality of training data subsets: calculating a subset-specific loss based on a loss function and the respective training data subset; and optimizing a model based on the subset-specific loss; determining a set of embeddings based on the optimized model; determining, based on the set of embeddings, a plurality of dot kernels; combining the plurality of dot kernels to form a composite kernel for a Gaussian process; receiving live data from an application; and predicting a plurality of values and a plurality of uncertainties associated with the plurality of values simultaneously using the composite kernel.

Abstract: Certain aspects of the present disclosure provide techniques for communicating between an application executing on a client device and a server using a persistent connection. An example method generally includes initializing a persistent connection between an application executing on a client device and a server. Information about an event within the application is received. Communications between the application and the server are performed via streaming data related to the information about the event carried on the persistent connection. Generally, the streaming data may be translated from an application-native format to a platform-agnostic format and may include application-specific information. One or more actions are taken within the application based on the streaming data related to the event and carried on the persistent connection.

Abstract: A processor may receive an image and determine a number of foreground pixels in the image. The processor may obtain a result of optical character recognition (OCR) processing performed on the image. The processor may identify at least one bounding box surrounding at least one portion of text in the result and overlay the at least one bounding box on the image to form a masked image. The processor may determine a number of foreground pixels in the masked image and a decrease in the number of foreground pixels in the masked image relative to the number of foreground pixels in the image. Based on the decrease, the processor may modify an aspect of the OCR processing for subsequent image processing.

Abstract: A method including receiving, at a platform and from a first user using a first user device, selection of a uniform resource indicator (URI) unique to a second user using a second user device. The method also includes generating, automatically by the platform in response to receiving the URI, a conference session unique to the first user and the second user. The method also includes transmitting, automatically by the platform, a message to the second user, the message indicating that the conference session is initiated. The method also includes receiving, by the platform, an indication from the second user device that the second user joins the conference session. The method also includes joining, automatically by the platform, the first user device and the second user device in the conference session.

Abstract: Systems and methods for synchronizing cloud resources are disclosed. An example method may include receiving a first request to synchronize first target cloud resources to a first specified state defined in a configuration repository, generating one or more first configuration commands corresponding to the first request, the one or more first configuration commands associated with a first cloud provider and a first cloud configuration framework, and executing the one or more first configuration commands to set a state of the first target cloud resources to the first specified state.

Abstract: A method and system to learn new forms to be incorporated into an electronic document preparation system, or to learn the behavior of existing systems, receive form data related to a new form having a plurality of data fields that expect data values based on specific functions. The method and system gather training set data including previously filled forms having completed data fields corresponding to the data fields of the new form. The method and system include multiple analysis modules that each generate candidate functions for providing data values for the data fields of the new form. The method and system evaluate the candidate functions from each analysis technique and select the candidate functions that are most accurate based on comparisons with the training set data.

Abstract: Forecasts are provided based on dynamic model selection for different sets of time series. A model comprises a transformation and a prediction algorithm. Given a time series, a transformation is selected for the time series and a prediction algorithm is selected to make a forecast based on the transformed time series. Sets of time series are distinguished from each other based on diverse sparsities, temporal scales and other time series attributes. A model is dynamically selected based on time series attributes to increase forecasting accuracy and decrease forecasting computation time. The dynamic model selection is based on the creation of a meta-model from historical sets of historical time series.

Abstract: A method including training predictor machine learning models (MLMs) using a first data set. The trained predictor MLMs are trained to predict classifications of data items in the first data set. The method also includes training confidence MLMs using second classifications, output by the trained predictor MLMs. The method also includes generating an aggregated ranked list of classes based on third classifications output by the trained predictor MLMs and second confidences output by the trained confidence MLMs. The method also includes training an ensemble confidence MLM using the aggregated ranked list of classes to generate a trained ensemble confidence MLM. The trained ensemble confidence MLM is trained to predict a corresponding selected classification for each corresponding data item in a training data set containing second data items similar to the first data items.

Abstract: A method and system learn functions to be associated with data fields of forms to be incorporated into an electronic document preparation system. The functions are essentially sets of operations required to calculate the data field. The method and system receive form data related to a data field that expects data values resulting from performing specific operations. The method and system utilize machine learning and training set data to generate, test, and evaluate candidate functions to determine acceptable functions.

Abstract: A method predicts custom fields from text. Transaction text is normalized from transaction data to generate normalized text. A field prediction and a type prediction are selected using prediction data and the normalized text. The prediction data is generated using a machine learning model trained to identify field predictions from free form text. The field prediction and the type prediction are presented to a client device. In response to user input from the client device, the transaction data is updated with the field prediction.

Abstract: Systems and methods for training machine learning models are disclosed. An example method includes receiving a plurality of first outputs and a ground truth value for each first output, each first output including an extracted string and a raw confidence score, determining, for each first output, an accuracy metric based at least in part on the extracted string and its corresponding ground truth value, for each extracted string: determining a similarity metric between the respective extracted string and each other extracted string of the plurality of first outputs, and determining a pseudo-accuracy based at least in part on the determined similarity metrics and the determined accuracy metrics, generating training data based at least in part on the determined pseudo-accuracies and the plurality of first outputs, and training the machine learning model, based on the training data, to predict pseudo-accuracies associated with subsequent outputs from a document extraction model.

Abstract: Systems and methods for forecasting future values of data streams are disclosed. One example method may include receiving information characterizing each of a plurality of forecasting models, retrieving historical data for each of a plurality of data streams, determining one or more constraints, dynamically selecting one of the plurality of forecasting models for each of the data streams based on accuracy metrics for the forecasting models, estimating cost metrics associated with each forecasting model, dynamically selecting the forecasting model based at least in part on the accuracy metrics, the cost metrics, and the determined constraints, and forecasting a first subsequent value of each data stream using the corresponding selected forecasting model.

Abstract: The disclosed embodiments provide a system that verifies user attributes. During operation, the system obtains financial data containing a set of strings representing a set of deposits into one or more financial accounts of one or more users. Next, the system generates one or more clusters of the deposits according to a similarity between pairs of strings in the set of strings. For each cluster from the one or more clusters, the system associates a subset of the strings in the cluster with an income source and extracts one or more employment attributes of a user from a subset of the deposits associated with the user in the cluster. Finally, the system uses the one or more employment attributes to provide an automatic verification of an income of the user.