Abstract: Embodiments for detecting data anomalies by a processor. A machine learning model may be trained according to collected scores and anomaly labels of a plurality of anomaly detection operations applied to one or more data sets such that the collected scores and labels identify a degree of accuracy of estimating anomalies for each of the plurality of anomaly detection operations. An anomaly may be detected in an unstructured data set by applying the trained machine learning model on an unstructured data set.

Abstract: In response to a user-initiated interaction request sent by a user using an electronic communication, subsequent actions performed by other users that received the user-initiated interaction request are analyzed. A determination is made as to whether the subsequent actions performed by the other users that received the user-initiated interaction request correlate to an intended interaction result of the user-initiated interaction request. A visual representation of a collaboration model that correlates probabilities of successful collaborations between the user and the other users is generated in accordance with determined correlations between the subsequent actions performed by the other users and the intended interaction result.

Abstract: Embodiments for detecting data anomalies by a processor. A machine learning model may be trained according to collected scores and anomaly labels of a plurality of anomaly detection operations applied to one or more data sets such that the collected scores and labels identify a degree of accuracy of estimating anomalies for each of the plurality of anomaly detection operations. An anomaly may be detected in an unstructured data set by applying the trained machine learning model on an unstructured data set.

Abstract: In response to a user-initiated interaction request sent by a user using an electronic communication, subsequent actions performed by other users that received the user-initiated interaction request are analyzed. A determination is made as to whether the subsequent actions performed by the other users that received the user-initiated interaction request correlate to an intended interaction result of the user-initiated interaction request. A visual representation of a collaboration model that correlates probabilities of successful collaborations between the user and the other users is generated in accordance with determined correlations between the subsequent actions performed by the other users and the intended interaction result.

Abstract: In response to a user-initiated interaction request sent by a user using an electronic communication, subsequent actions performed by other users that received the user-initiated interaction request are analyzed. A determination is made as to whether the subsequent actions performed by the other users that received the user-initiated interaction request correlate to an intended interaction result of the user-initiated interaction request. A visual representation of a collaboration model that correlates probabilities of successful collaborations between the user and the other users is generated in accordance with determined correlations between the subsequent actions performed by the other users and the intended interaction result.

Abstract: In response to a user-initiated interaction request sent by a user using an electronic communication, subsequent actions performed by other users that received the user-initiated interaction request are analyzed. A determination is made as to whether the subsequent actions performed by the other users that received the user-initiated interaction request correlate to an intended interaction result of the user-initiated interaction request. A visual representation of a collaboration model that correlates probabilities of successful collaborations between the user and the other users is generated in accordance with determined correlations between the subsequent actions performed by the other users and the intended interaction result.