Abstract: A system and method for identifying a user is described. The system identifies collaboration metrics based on user interaction data of users of an application from an enterprise. The system accesses enterprise organizational data of the enterprise and identifies topic data from the user interaction data and the enterprise organizational data. The system trains a machine learning model based on the collaboration metrics, the enterprise organizational data, and the topic data.

Abstract: A system and method for identifying a user is described. The system identifies collaboration metrics based on user interaction data of users of an application from an enterprise. The system accesses enterprise organizational data of the enterprise and identifies topic data from the user interaction data and the enterprise organizational data. The system trains a machine learning model based on the collaboration metrics, the enterprise organizational data, and the topic data.

Abstract: A system and method for applying noise to data is described. The system accesses a metric value of a metric of each user from a group of users of an application. The metric indicates a measure of an operation of the application by a corresponding user. The system generates noise values and defines a distribution of the noise values to the group of users. The system modifies the metric value of the metric of each user with a corresponding noise value from the noise values based on the distribution.

Abstract: A system and method for applying noise to data is described. The system accesses a metric value of a metric of each user from a group of users of an application. The metric indicates a measure of an operation of the application by a corresponding user. The system generates noise values and defines a distribution of the noise values to the group of users. The system modifies the metric value of the metric of each user with a corresponding noise value from the noise values based on the distribution.

Abstract: A system and method for identifying a user is described. The system identifies collaboration metrics based on user interaction data of users of an application from an enterprise. The system accesses enterprise organizational data of the enterprise and identifies topic data from the user interaction data and the enterprise organizational data. The system trains a machine learning model based on the collaboration metrics, the enterprise organizational data, and the topic data.

Abstract: A method may include retrieving metric data on a plurality of groups of users, the metric data including: a value of a performance metric for each of the plurality of groups; and an indication that a first group of the plurality of groups is anomalous with respect to a value of the performance metric of a control group of the plurality of groups; and presenting a user interface, the user interface including: a first portion including a visualization of a comparison of the value of the performance metric for the first group and values of the performance metric of other groups in the plurality of groups; and a second portion including a visualization of trend data of the performance metric for the first group over a period of time.

Abstract: A method may include retrieving metric data on a plurality of groups of users, the metric data including: a value of a performance metric for each of the plurality of groups; and an indication that a first group of the plurality of groups is anomalous with respect to a value of the performance metric of a control group of the plurality of groups; and presenting a user interface, the user interface including: a first portion including a visualization of a comparison of the value of the performance metric for the first group and values of the performance metric of other groups in the plurality of groups; and a second portion including a visualization of trend data of the performance metric for the first group over a period of time.

Abstract: A method may include accessing a plurality of data items, each data item in the plurality of data items having a plurality of stored dimensions; selecting a subset of the data items based on a shared value of a first dimension of plurality of dimensions; identifying an outcome metric for the first group; determining a control group for comparison with the first group with respect to the outcome metric, wherein data items in the control group are determined based on dimensions that influence the first dimension and the outcome metric; determining that the outcome metric of the first group is anomalous with respect to the outcome metric of the control group; and presenting a notification to a computing device indicating the anomaly.

Abstract: Various embodiments of the present technology provide for a space optimization tool. More specifically, some embodiments provide for a space optimization tool that uses team collaboration patterns to guide team-to-location allocation planning. Some embodiments of the space optimization tool use social collaboration data that tracks people's communication patterns, such as how frequently teams talk to each other. The social collaboration data can be used to by the space optimization tool to guide how individuals and teams should sit on different locations (e.g., within floors, buildings, etc.). The space optimization tool can create a smart floor layout that achieves desired business outcomes, such as minimizing employee's commute time to other teams, stimulating collaborations between teams, and the like. In accordance with various embodiments, the space optimization tool can create a smart layout by using an optimization model to automatically optimize a target function at global level.