Abstract: In an example embodiment, a computer-implemented method comprises obtaining labels from messages associated with an email service provider, wherein the labels indicate for each message IP how many spam and non-spam messages have been received; obtaining network data features from a cloud service provider; providing the labels and network data features to a machine learning application; generating a prediction model representing an algorithm for determining whether a particular set of network data features are spam or not; applying the prediction model to network data features for an unlabeled message; and generating an output of the prediction model indicating a likelihood that the unlabeled message is spam.

Abstract: Tracking lineage of data. A method may be practiced in a network computing environment including a plurality of interconnected systems where data is shared between the systems. A method includes accessing a dataset. The dataset is associated with lineage metadata. The lineage metadata includes data indicating the original source of the data, one or more intermediary entities that have performed operations on the dataset, and the nature of operations performed on the dataset. A first entity performs an operation on the dataset. As a result of performing a first operation on the dataset, the method includes updating the lineage metadata to indicate that the first entity performed the operation on the dataset. The method further includes providing functionality for determining if the lineage metadata has been compromised in that the lineage metadata has been at least one of removed from association with the dataset, is corrupted, or is incomplete.

Abstract: In an example embodiment, a computer-implemented method comprises obtaining labels from messages associated with an email service provider, wherein the labels indicate for each message IP how many spam and non-spam messages have been received; obtaining network data features from a cloud service provider; providing the labels and network data features to a machine learning application; generating a prediction model representing an algorithm for determining whether a particular set of network data features are spam or not; applying the prediction model to network data features for an unlabeled message; and generating an output of the prediction model indicating a likelihood that the unlabeled message is spam.

Abstract: Tracking lineage of data. A method may be practiced in a network computing environment including a plurality of interconnected systems where data is shared between the systems. A method includes accessing a dataset. The dataset is associated with lineage metadata. The lineage metadata includes data indicating the original source of the data, one or more intermediary entities that have performed operations on the dataset, and the nature of operations performed on the dataset. A first entity performs an operation on the dataset. As a result of performing a first operation on the dataset, the method includes updating the lineage metadata to indicate that the first entity performed the operation on the dataset. The method further includes providing functionality for determining if the lineage metadata has been compromised in that the lineage metadata has been at least one of removed from association with the dataset, is corrupted, or is incomplete.