Abstract: A method provides an automatic notification manner of data changes. After collecting information related to a target user such as a dataset, a data dashboard, or a data report, the analysis preference of the user can be determined based on the collected information. Then, upon the dataset is updated, a variety of critical data changes in the dataset may be detected as an alert, and a notification related to the alert may be provided to the user via various manners. The method does not require the user to manually configure or create an alert rule for data changes, which makes data-driven alerting much easier for the user, thereby improving the user experience.

Abstract: The present disclosure relates to processing operations configured to uniquely utilize indexing of content to improve content retrieval processing, particularly when working with large data sets. The techniques described herein enables efficient content retrieval when working with large data sets such as those that may be associated with a plurality of tenants of a data storage application/service. Among other technical advantages, the present disclosure is applicable to train a classifier using relevant samples based on text search in tenant-specific scenarios, where accurate searching can be executed for content associated with one or more tenant accounts of an application/service concurrently in milliseconds even in instances where there may be millions of documents to be searched. As an example, exemplary data shards may be generated and managed for efficient and scalable content retrieval processing including training of a classifier (e.g.

Abstract: A method provides an automatic notification manner of data changes. After collecting information related to a target user such as a dataset, a data dashboard, or a data report, the analysis preference of the user can be determined based on the collected information. Then, upon the dataset is updated, a variety of critical data changes in the dataset may be detected as an alert, and a notification related to the alert may be provided to the user via various manners. The method does not require the user to manually configure or create an alert rule for data changes, which makes data-driven alerting much easier for the user, thereby improving the user experience.

Abstract: A method provides an automatic notification manner of data changes. After collecting information related to a target user (202) such as a dataset, a data dashboard, or a data report, the analysis preference of the user can be determined based on the collected information (204). Then, upon the dataset is updated, a variety of critical data changes in the dataset may be detected as an alert (206), and a notification related to the alert may be provided to the user via various manners (208). The method does not require the user to manually configure or create an alert rule for data changes, which makes data-driven alerting much easier for the user, thereby improving the user experience.

Abstract: The present disclosure relates to processing operations configured to uniquely utilize indexing of content to improve content retrieval processing, particularly when working with large data sets. The techniques described herein enables efficient content retrieval when working with large data sets such as those that may be associated with a plurality of tenants of a data storage application/service. Among other technical advantages, the present disclosure is applicable to train a classifier using relevant samples based on text search in tenant-specific scenarios, where accurate searching can be executed for content associated with one or more tenant accounts of an application/service concurrently in milliseconds even in instances where there may be millions of documents to be searched. As an example, exemplary data shards may be generated and managed for efficient and scalable content retrieval processing including training of a classifier (e.g.

Abstract: The present disclosure relates to processing operations configured to uniquely utilize indexing of content to improve content retrieval processing, particularly when working with large data sets. The techniques described herein enables efficient content retrieval when working with large data sets such as those that may be associated with a plurality of tenants of a data storage application/service. Among other technical advantages, the present disclosure is applicable to train a classifier using relevant samples based on text search in tenant-specific scenarios, where accurate searching can be executed for content associated with one or more tenant accounts of an application/service concurrently in milliseconds even in instances where there may be millions of documents to be searched. As an example, exemplary data shards may be generated and managed for efficient and scalable content retrieval processing including training of a classifier (e.g.

Abstract: A method provides an automatic notification manner of data changes. After collecting information related to a target user (202) such as a dataset, a data dashboard, or a data report, the analysis preference of the user can be determined based on the collected information (204). Then, upon the dataset is updated, a variety of critical data changes in the dataset may be detected as an alert (206), and a notification related to the alert may be provided to the user via various manners (208). The method does not require the user to manually configure or create an alert rule for data changes, which makes data-driven alerting much easier for the user, thereby improving the user experience.

Abstract: The present disclosure relates to processing operations configured to uniquely utilize indexing of content to improve content retrieval processing, particularly when working with large data sets. The techniques described herein enables efficient content retrieval when working with large data sets such as those that may be associated with a plurality of tenants of a data storage application/service. Among other technical advantages, the present disclosure is applicable to train a classifier using relevant samples based on text search in tenant-specific scenarios, where accurate searching can be executed for content associated with one or more tenant accounts of an application/service concurrently in milliseconds even in instances where there may be millions of documents to be searched. As an example, exemplary data shards may be generated and managed for efficient and scalable content retrieval processing including training of a classifier (e.g.

Abstract: A digital computer and method for processing data indicative of images of facial and head movements of a subject to recognize at least one of said movements and to determine at least one mental state of said subject is provided. The outputting instructions for providing to a user information relating to at least one said mental state. A further processing data reflective of input from a user, and based at least in part on said input, confirming or modifying said determination and generating with a transducer an output of humanly perceptible stimuli indicative of said at least one mental state.