A system uses a machine learning model to identify anomalies and modify parameters of a computing environment. The system modifies parameters of a computing environment based on the presence and absence of anomalies in the computing system while avoiding modifying parameters as a result of brief spikes in computing environment attributes. The system uses a machine learning model to generate predictions of anomalies for data points of computing environment attributes. The system compiles sets of predictions into batches. The system determines whether each batch includes enough anomalous-labeled data points to be considered an anomalous batch. The system compiles the batches into sets. The system determines whether the sets of batches include enough anomalous batches to be considered an anomalous set of batches. The system modifies the parameters of the computing environment based on determining whether or not the sets of batches are anomalous.

The present disclosure relates to systems and methods for matching electronic activities with record objects based on entity relationships. The method can include accessing a plurality of electronic activities, identifying an electronic activity, identifying a first participant associated with a first entity and a second participant associated with a second entity, determining whether a record object identifier is included in the electronic activity, identifying a first record object of the system of record that includes an instance of the record object identifier, and storing an association between the electronic activity and the first record object. The method can include determining a second record object corresponding to the second entity, identifying, using a matching policy, a third record object linked to the second record object and identifying a third entity, and storing, by the one or more processors, an association between the electronic activity and the third record object.

Systems and methods are described for linking records from different databases. A search may be performed for each record of a received record set for similar records based on having similar field values. Recommended records of the record set may be assigned with the identified similar records to sub-groups. Pairs of records may be formed for each record of the sub-group, and comparative and identifying features may be extracted from each field of the pairs of records. Then, a trained model may be applied to the differences to determine a similarity score. Cluster identifiers may be applied to records within each sub-group having similarity scores greater than a predetermined threshold. In response to a query for a requested record, all records having the same cluster identifier may be output on a graphical interface, allowing users to observe linked records for a person in the different databases.

A knowledge management system and method. The knowledge management system and method can have multi-industry applications with special suitability for program-based initiatives, research projects, healthcare sector programs, educational system programs, workforce development initiatives, and social service outcomes.

Embodiments of this application disclose a data labeling system and method, and a data labeling manager. The system includes a data labeling manager, a labeling model storage repository, and a basic computing unit storage repository. The data labeling manager receives a data labeling request, obtains a target basic computing unit, allocates a hardware resource to the target basic computing unit, establishes a target computing unit, obtains first storage path information of basic parameter data of a first labeling model, and sends the first storage path information to the target computing unit. The target computing unit obtains the basic parameter data of the to-be-used labeling model by using the first storage path information, combines a target model inference framework and the basic parameter data of the first labeling model to obtain the first labeling model, and labels to-be-labeled data by using the first labeling model.

A computer-implemented method includes representing a plurality of database tables as respective vectors in a multi-dimensional vector space, receiving an indication that a first database table represented by a first vector and a second database table represented by a second vector are related to each other, moving the respective vectors representing the plurality of database tables in the multi-dimensional vector space in response to the indication, and grouping the plurality of database tables into one or more table clusters based on positions of the respective vectors representing the plurality of database tables in the multi-dimensional vector space.

An embodiment includes parsing conversation data to extract a message dataset and a user dataset. The embodiment classifies the message dataset into a category using machine learning processing and identifies the category as a top category based at least in part on an amount of the conversation data associated with the category. The embodiment generates impact data associated with the user dataset based on actions in the conversation data by the user. The embodiment generates role data associated with the user by applying a rule to the conversation data for the user. The embodiment generates key index data associated with the message dataset by identifying interactions with a message represented by the message dataset. The embodiment generates output data arranged according to a specified data format that is compatible with a user interface.

Embodiments for a data protection method of grouping assets for protection policy assignment based on asset metadata by defining a set of metrics characterizing each asset in the system and comparing each metric of an asset with corresponding metrics of other asset groups each containing one or more other assets. A unique protection policy is assigned to each group for application to each asset within a respective group. An overall affinity percentage of the metrics of asset with the corresponding metrics of each group is determined, and the asset is automatically grouped into the group based with the highest overall affinity percentage. The user is prompted to confirm the automatic grouping or to select a different group for assigning to the asset.

Some embodiments of the present application include obtaining datasets including a plurality of features and computing a correlation score between each of the features. Based on the correlation scores, the features may be clustered together such that each cluster includes features that are correlated with one another, and features included in different feature clusters lack correlation with one another. A machine learning model may be selected based on a set of input features for the model and the plurality of clusters such that each input feature is included in one of the feature clusters and no feature cluster includes more than one of the input features. Datasets may then be selected based on the set of input features, which may be used to generate training data for training the machine learning model.

Presented herein is a transportable, distributed, edge-based, cloud-centered broadband command, control and communications infrastructure with built-in resilience that can be employed in data collection and integration, intelligence processing, mission planning, cognitive decision support and operational command, control and communications system. Specifically, a Modular Mission Systems (MIMS), composed of Modular Load Units (MLU), and tactical communications gateways integrated onto air platforms of opportunity in podded configurations (PODS) that can bring cloud-based, broadband communications and processing architectures to the tactical edge.