Abstract: A computer system includes memory hardware configured to store computer-executable instructions and processor hardware configured to execute instructions. The instructions include receiving, from a user device, a user verification request. The user verification request includes user identity credentials, a login context describing a login event, and a request for access to a computer system object. The instructions include determining whether the user identity credentials are verified. The instructions include, in response to determining that the user identity credentials are verified, requesting a user token based on the user identity credentials and the login context. The user token specifies entitlements associated with the user identity credentials. The instructions include determining whether the user token specifies entitlements sufficient to access the computer system object.

Abstract: A computer-implemented method includes receiving a group of sets. Each set has values for immutable attributes that match values for at least one mutable attribute in a prediction request. The method includes generating a deviation model based on the group of sets. The method includes generating multiple sets of likely mutable attribute values using the deviation model. The method includes automatically selecting a neural network based on at least one of the likely mutable attribute values. The neural network includes a set of layers. Each layer includes a set of nodes. A first layer receives inputs at the set of nodes of the input layer. A last layer outputs, from the neural network, outputs modified by a preceding layer. The method includes generating a likelihood corresponding to the at least one mutable attribute in the prediction request using the neural network.

Abstract: An apparatus may be configured to determine an industry object model for a data set of an enterprise; and generate an archive package for an archive time point by generating a set of global objects such that each global objects of the set of global objects is represented in the data set and for a respective data source of the set of data sources, generating a set of data source objects based on the industry object model, associating the set of data source objects with the set of global objects, and storing, in the archive package, the set of data source objects and the associated global objects for the respective data source.

Abstract: A computer system includes memory hardware configured to store a machine learning model, historical feature vector inputs, and computer-executable instructions, and processor hardware configured to execute the instructions. The instructions include training a first machine learning model with the historical feature vector inputs to generate a title score output, and training a second machine learning model with the historical feature vector inputs to generate a background score output. For each entity in a set, the instructions include processing a title feature vector input with the first machine learning model, and processing a background feature vector with a second machine learning model, to generate a tittle score output and a background score output each indicative of a likelihood that the entity is a decision entity. The instructions include automatically distributing structured campaign data to the entity based on the title score output and the background score output.

Abstract: A method includes, in response to receiving a forecasting request from a user device via a web portal, determining a measure of uncertainty associated with an input vector specified by the forecasting request. The measure of uncertainty includes a compact representation of uncertainty associated with future time intervals specified by the forecasting request. The method includes obtaining a set of historical data from a time-series data store. The method includes generating a forecast model using the set of historical data, the input vector, and the measure of uncertainty to predict outcomes incrementally for the future time intervals. The method includes determining a predicted outcome using the forecast model at an end of the future time intervals. The method includes, in response to the predicted outcome exceeding a threshold, generating a graphical user interface. The graphical user interface illustrates the mean vector and a magnitude of the predicted uncertainty measure.

Abstract: A computer system includes memory hardware configured to store software application files and computer-executable instructions executed by processor hardware. The instructions include determining memory space required for reinstallation of an archived target software application into a directory and preserving the memory space by creating a storage space preservation file in the directory based on the determined memory space. The storage space preservation file is unusable as the archived target software application. The instructions include detecting an execution call to a first replacement executable file and, in response to detecting the execution call, restoring files of the archived target software application to the directory and forwarding the execution call to a first executable file of the archived target software application in the directory.

Abstract: A logging management server is provided for enhanced centralized monitoring of cloud computing platforms. The processor is configured to receive logging data sub-streams from the cloud computing platform. Each of the logging data sub-streams includes compressed logging data. The processor is also configured to apply a transformation function to each of the logging data sub-streams to obtain a transformed centralized logging data stream. The processor is further configured to transmit the transformed centralized logging data stream to write to a centralized object storage container. The processor is also configured to decompress a portion of the compressed logging data of the centralized logging data stream. The processor is further configured to identify the appended account identifier and the appended log group associated with the decompressed portion of logging data. The processor is also configured to route the decompressed portion of logging data to a sorted object storage container.

Abstract: A system includes a processor and a memory storing instructions. The instructions include, in response to receiving a first input indicating a first data model from a user device, obtaining the first data model and identifying objects within the first data model. The instructions include obtaining a set of rules and, for each rule of the set of rules, applying the rule to each object to determine an adherence of the object to the rule or a violation of the object of the rule. The instructions include generating a report that indicates, for each rule and for each object, the adherence of the object to the rule or the violation of the object of the rule and calculating an adherence score based on an overall adherence of the objects to the set of rules. The instructions include transmitting, for display on the user device, the generated report and adherence score.

Abstract: A system includes memory hardware that stores an event logging database including entries corresponding to events processed at local servers. The processor hardware executes instructions including, in response to receiving a first message from a first local server (the first message indicates that a first event occurred), identifying a causal parent event preceding the first event. The causal parent event is a most recent event that occurred at the first local server. The instructions include identifying a temporal parent event preceding the first event. The temporal parent event is a most recent event that occurred across all of the local servers. The instructions include generating a new entry based on data from the first message and a cryptographic hash based on a hash of a first entry recording the causal parent event and a hash of a second entry recording the temporal parent event.

Abstract: Systems and methods for constructing sets of synthetic data. A single data record is identified from a first set of data. The first set of data comprises a first plurality of data records, each of the data records including multiple items of data describing an entity. Using pattern recognition, the single data record is processed to identify a group of records from within the first set that have corresponding characteristics equivalent to the single data record. The identified group of records comprises a target set of variables and the group of records from the first set that are not identified comprises a control set of variables. The target set of variables and the control set of variables are processed, using probability estimation and optimization constraints, to determine a score for each of the records in the first set. The score describes how similar each of the records in the first set is to the single data record. The records associated with a percentage of the highest scores are identified.

Abstract: A confidence interval determination system includes a processor and memory. The memory stores a historical data store and instructions. The instructions include, in response to receiving a prediction request, determining a size of each element of the prediction request and obtaining a previous prediction request. The instructions include, in response to the prediction request being different from the previous prediction request, for each element of the prediction request, generating a classifier based on the training data and, for each bin of a set of bins of the element, determining a set of predicted likelihoods using the classifier. The instructions include generating a mean vector prediction and a covariance matrix using the sets of bins generated, determining a predicted outcome and a confidence interval using the mean vector prediction and the covariance matrix, and, in response to the confidence interval exceeding a threshold, outputting the predicted outcome and the confidence interval.

Abstract: A technology management server for domain-based technology deployment and management is provided. The server includes a processor configured to receive an architecture definition file identifying a prior system status for each snapshotted application system. The processor is also configured to scan the application systems and classify each of the scanned application systems into an associated technology domain using a domain classification algorithm. The processor is configured to identify each scanned application system with a changed system status. The processor is configured to obtain a system update for each scanned application system with a changed system status, based on the technology domain. The system updates define implementation characteristics of each changed scanned application system. The processor is also configured to redefine the architecture definition file with the system updates.

Abstract: A computer-implemented method of mapping attribute names of a source data model to a destination data model includes processing one or more source attribute names from the source data model and one or more destination attribute names of the destination data model to obtain standardized attribute names. The method includes determining whether any of the source attribute names have an existing entry in a custom dictionary. The method includes, for each source attribute name that has an existing entry in the custom dictionary, recording an output mapping for the source attribute name according to the existing entry in the custom dictionary. The method includes, for each source attribute name that does not have an existing entry in the custom dictionary, using a machine learning model to predict the mapping of each source attribute name to a corresponding one of the destination attribute names.

Abstract: Methods and systems for anomaly detection in relational databases. The methods and systems perform operations comprising: accessing a data object including a data set that has been collected over a given time interval; selecting a reference data point from the data set; computing an angle representing a data growth rate of the data set at least within the given time interval relative to the reference data point; determining that the angle representing the data growth rate of the data set within the given time interval exceeds a specified threshold; and in response to determining that the angle representing the data growth rate of the data set within the given time interval exceeds the specified threshold, triggering an anomaly detection operation.

Abstract: An apparatus includes processing circuitry and a memory storing instructions that, when executed by the processing circuitry, cause the apparatus to identify a plurality of components and a data flow that interconnects the plurality of components. The instructions cause the apparatus to determine a lineage of a record generated by the plurality of components based on the data flow. The lineage indicates the data flow from a first component to a second component of the plurality of components to generate the record. The instructions cause the apparatus to present, to a user, a visual depiction of the lineage of the record. The visual depiction indicates the data flow of a query through at least the first component and the second component of the plurality of the components to generate the record.

Abstract: A computer-implemented method includes selecting a group of sets. Each set has values for immutable attributes that match values for at least one mutable attribute in a prediction request. The method includes determining a conditional covariance matrix for the group of sets. The method includes generating a deviation model based on the conditional covariance matrix. The method includes sampling the deviation model to generate multiple sets of likely mutable attribute values. The method includes automatically selecting a neural network from a set of outcome models based on the likely mutable attribute values. Each neural network includes a set of layers. Each layer includes a set of nodes. A first layer receives inputs at the set of nodes of the input layer. Each layer other than the first layer receives outputs from a preceding layer and creates modified outputs. A last layer outputs the modified outputs from the neural network.

Abstract: A smart mirror system includes a screen configured to generate a display for viewing by a user, a mirror positioned in front of the screen, a near-field-communication (NFC) card reader located behind the mirror, a network interface for communicating with a remote health data server, memory configured to store computer-executable instructions, and at least one processor configured to execute the instructions. The instructions include selectively detecting, by the NFC card reader, an NFC chip of a member card placed in proximity to the NFC card reader. The instructions include, in response to detecting the NFC chip, obtaining member information from the detected NFC chip, and authenticating the user to the remote health data server, via the network interface, according, at least in part, to the obtained member information.

Abstract: A computer-implemented method includes determining whether historical profile data structures are stored in a database with corresponding structured supplemental data and selected health care plan option identifiers of a set of health care plan option identifiers. The method includes generating historical feature vectors using the historical data structures stored in the database or generating the historical feature vectors using created sample profile data structures. The method includes training machine learning models using the generated historical feature vectors, selecting one of the machine learning models for use in generating recommendation outputs, presenting an interactive voice interface to an entity to generate audio questions and prompts for obtaining response data from the entity, classifying voice survey responses of the entity, and generating feature vectors.

Abstract: Methods and systems for analyzing data are described. In one embodiment, a method comprises a processor receiving a data analysis algorithm over a network and executing the data analysis algorithm, the data analysis algorithm analyzing data stored in a database using machine learning to identify a database organizational format, the data analysis algorithm identifying one or more locations for a set of data stored on the database based on identifying the database organizational format, the data analysis algorithm parsing the set of data to identify whether any entries in the database associated with the set of data includes a particular value, and the data analysis algorithm communicating over the network at least a first number of entries in the database that include the particular value and a second number of entries in the database that do not include the particular value.

Abstract: A computerized analysis system includes a data retrieval module, a spike filter module, a chargemaster analysis module, and an alert module. The data retrieval module is configured to retrieve claimline data for a period of time from an input data store. The claimline data includes billing codes and prices of the billing codes associated with a provider. The spike filter module is configured to flag and filter price spikes in the claimline data. The chargemaster analysis module is configured to determine whether a changepoint exists in the billing codes in the claimline data. The chargemaster analysis module is configured to identify a chargemaster increase for the provider in response to a statistical parameter of changepoints exceeding a threshold. The alert module is configured to, in response to identification of a chargemaster increase, publish an alert that a chargemaster increase has occurred for the provider.