A computer system for analyzing page tags of a website. The system may include a processor in communication with a database; and a storage medium. The storage medium may store instructions that, when executed, configure the processor to: access the website, the website comprising plurality of page tags; generate a collected tag record by aggregating the page tags, the collected tag record comprising collected elements; request, from the database, a benchmark tag record, the benchmark record comprising benchmark elements, the benchmark tag record being based on historic page tags stored in the database; generate a result tag record, the result tag record indicating at least one of matches or mismatches between the benchmark tag record and the collected tag record; and display at least one of the result tag record or an analysis result, the analysis result representing an aggregation of the result.

A system with an interactive user interface for users to view and interact with sanitized log data received from a plurality of hosts, such as those associated with various services of an organization. The system may receive from hosts log files and/or metadata that have been filtered by agents executing on the respective hosts to remove or anonymize any sensitive or confidential information prior to transmission to the system. In some embodiments the system does further filtering of the sanitized data. Received sanitized data is parsed, indexed, and/or otherwise processed for optimal searching, and stored in a log pipeline. The system causes display of an electronic visualization interface comprising a dynamic electronic search configured to receive an indication of various log search criteria, such as an error or trace identifier, that are used to identify matching log files meeting the provided criteria, such as log files associated with services executed on different hosts.

In some examples, a prototype model that includes a representative subset of data points (e.g., inputs and output classifications) of a machine learning model is analyzed to efficiently interpret the machine learning model's behavior. Performance metrics such as a critic fraction, local explanation scores, and global explanation scores are determined. A local explanation score capture an importance of a feature of a test point to the machine learning model determining a particular class for the test point and is computed by comparing a value of a feature of a test point to values for prototypes of the prototype model. Using a similar approach, global explanation scores may be computed for features by combining local explanation scores for data points. A critic fraction may be computed to quantify a misclassification rate of the prototype model, indicating the interpretability of the model.

An originating server of a payment processing system comprising multiple communicating servers first processes a transaction event, generates a correlating identifier and transmits the correlating identifier and processing information to a central data log accessible by the multiple communicating servers. One or more intermediate servers and a terminating server then process the transaction event, each successive intermediate server and the terminating server receiving the transaction event and a correlation identifier associated with the transaction event generated by the previous server which processed the transaction event. Each successive intermediate server and the terminating server generates a correlation identifier, and transmits both the received and generated correlation identifiers to the central transaction log. A query comprising a correlation identifier associated with the transaction event is received. The payment processing system extracts successive sets of entries from the central data log by matching corresponding correlation identifiers and generates a transaction event history.

Systems and methods for constructing extensible event log with JavaScript Object Notation (JSON) encoded payload data. The system includes a computing device. The computing device may be connected to a device, where an event has occurred. The device may send a message to the management software of the computing device based on the event, where the message includes information corresponding to an event occurred at the device and payload data of the event. When the management software receives the message, the management software may determine an event type of the event based on the information, and then convert the payload data of the event to encoded payload data in an extensible format, such as the JSON format. Thus, the management software may generate and store an event log comprising the event type and the encoded payload data.

Systems and methods for management of event metadata. The methods may include maintaining a plurality of data storage systems in communication with an external metadata management system. The methods may also include operating the metadata management system to store tagged event metadata corresponding to the plurality of data storage systems. Event metadata corresponding to a data storage system includes information associated with at least one data operation event executed on data residing in that data storage system.

A system with an interactive user interface for users to view and interact with sanitized log data received from a plurality of hosts, such as those associated with various services of an organization. The system may receive from hosts log files and/or metadata that have been filtered by agents executing on the respective hosts to remove or anonymize any sensitive or confidential information prior to transmission to the system. In some embodiments the system does further filtering of the sanitized data. Received sanitized data is parsed, indexed, and/or otherwise processed for optimal searching, and stored in a log pipeline. The system causes display of an electronic visualization interface comprising a dynamic electronic search configured to receive an indication of various log search criteria, such as an error or trace identifier, that are used to identify matching log files meeting the provided criteria, such as log files associated with services executed on different hosts.

A method implemented by a data processing system including: executing a dataflow graph that includes the plurality of components and the links, with a given component of the plurality including an input port, an audit port and an output port; processing, by the dataflow graph with the components and the links, the one or more data records representing the transaction, wherein the at least one of the components saves a state specifying one or more input records that are processed by the at least one of the components; when an error occurs during processing of one or more input records by the given component, restoring a state of the at least one of the components to the saved state; and based on the restored state, recovering at least some of the audit data for the given component of the dataflow graph.

Detecting data anomalies by receiving a first data set related to a first variable metric, determining data anomaly detection scores for data points of the first data set according to a plurality of data anomaly detection techniques, generating an adaptive ground-truth window according to the data anomaly detection scores, assigning a weighting value to each data point within the adaptive ground-truth window, training a machine learning system using the set of data anomaly detection scores and weighting values, and providing a trained machine learning system for evaluating a second data set.

The present invention is a method and system for automatedly producing at least one desktop analytics trigger. Upon receiving at least one type of data input, the system analyzes the data input and produces at least one desktop analytics trigger based on the results of the analysis of the data input. The data input can include data on the programs, applications, or information a user utilizes during a task, to allow use of desktop process analytics. This process may be used to either generate a new desktop analytics trigger or update an existing desktop analytics trigger.