A computer-implemented system for dynamic aggregation of data and minimization of data loss is disclosed. The system may be configured to perform instructions for: aggregating information from a plurality of networked systems by collecting a set of data from the networked systems, the set of data comprising data associated with a predetermined period of time and comprising one or more central variables that are included in data associated with more than one networked systems of the plurality of networked systems and one or more associated variables that describe one or more aspects of the central variables; retrieving one or more data transformation rules based on a relational map among the central variables and the associated variables; and aggregating the first set of data into one or more master data structures corresponding to the central variables based on the data transformation rules.

Methods and systems relating to formulating requests to a commerce management engine for product information may include a synchronization engine that estimates, using error data, future synchronization errors of product information to one or more channels. Each channel has respective product data fields for that channel and the error data relates to a prior synchronization of product information from one or more storefronts to the one or more channels and includes identified errors from the prior synchronization and corresponding corrections for resolving at least a subset of the identified errors. The synchronization may formulate a request, wherein the request requests product information for one or more products of the one or more storefronts for a future product synchronization with the one or more channels and includes a request parameter that is based on the estimated future synchronization errors.

The method includes receiving event information for a plurality of events from a plurality of data sources; normalizing the event information for each event of the plurality of events into a desired format by at least one of changing a field name of the event information or transforming at least one value in the event information into a recognized value; enriching the event information for each event of the plurality of events by adding additional data points associated with the event information to the event information; and/or transmitting the event information to a data recipient.

Systems and methods are provided for detecting sub-optimal performance of one or more virtual computing platforms. Usage data representing user activity, and performance data representing computing hardware resource utilization, is collected from a plurality of virtual machines hosted on one or more virtual computing platforms. The usage data and performance data is then analyzed along with configuration data representing the hardware components of the computing devices operating the virtual computing platform.

In one embodiment, a change data monitoring system may gather change data from multiple data sources each with a different proprietary source format. The change data monitoring system may receive a change data report having a change data set describing changes made to an online system from a data source of a data source set tracking the online system using multiple proprietary source formats. The change data monitoring system may convert the change data set from a proprietary source format to a standardized data presentation format. The change data monitoring system may present the change data set to a user in the standardized data presentation format.

Systems and methods for predicting computing system issues include: receiving a set of incident management tickets for a set of computing system issues and a set of computer log files for multiple modules of the computing system; arranging the set of tickets into chronologically ordered groups associated with particular computing system issues; pre-processing the set of computer log files to remove specified information, append to each log entry an indicator of the module of the log file, and merge the log entries; determining for each group a set of patterns for the group's associated computing system issue before the group's associated computing system issue arises; calculating for each pattern in each group a similarity score; selecting a subset of patterns whose similarity scores exceed a specified threshold; and generating a computing model associating the subset of patterns in each group with the group's associated computing system issue.

A method, computer program product, and computing system for receiving content from a third-party. The content may be processed to predict the disclosure of sensitive information. The sensitive information may be obscured from a platform user, where the third-party may be a customer and the platform user may be a customer service representative.

Embodiments of the present disclosure relate to a method of tracing a computing system, a device for tracing a computing system, and a computer readable medium. According to some embodiments, tracing data is extracted from a request that requests a dedicated processing resource for a task, the request being initiated by an application executed on a client and the tracing data including a parameter for performing the task, an identifier of the application, and time elapsed from initiating the request. The tracing data is stored in a volatile memory to facilitate transmitting the tracing data to a database server. The request is caused to be processed by a computing server hosting the dedicated processing resource. In this way, the cloud computing system may be traced rather than tracing the stand-alone tasks only.

In a processing apparatus having semiconductor integrated circuits, a first status monitoring circuit included in a first semiconductor integrated circuit is configured to instruct a plurality of second semiconductor integrated circuits to transmit status information indicating statuses of the plurality of second semiconductor integrated circuits. When a second status monitoring circuit included in each of the plurality of second semiconductor integrated circuits receives the instruction for transmission of the corresponding status information, the second status monitoring circuit transmits encrypted information in which the status information is encrypted to the first semiconductor integrated circuit.

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.