Techniques for a database management system to estimate a time needed to refresh a materialized view. This is a followed by an approach that uses estimated refresh duration to determine an optimized schedule for refreshing the materialized view. The approach combines the refresh duration estimate with a query rewrite pattern prediction for the materialized view and a quiet period prediction for the materialized view to determine the optimized refresh schedule for the materialized view.

The present disclosure relates to a method and system for determining goodness of pricing initiative on a digital platform. Said method comprises: (1) identifying, by a processor [102], a first set of products that are low churn products, brand rule independent products and competition independent products; (2) pre-clustering, by a clustering unit [108], the first set of products to identify pre-clusters such that the products within a pre-cluster are highly correlated products and products in different pre-clusters are independent of each other; (4) clustering, by the clustering unit [108], the pre-clusters based on predefined parameters to identify clusters; and (5) determining, by the processor [102], the goodness of the pricing initiative based at least on a testing of said pricing initiative based on the one first cluster.

Systems and methods are provided herein for processing digital content. A registry includes a set of common registry identifiers used to classify metadata. Further, an interchange layer and registry classification service: receives metadata derived from digital content in a content file; and classifies the metadata, by associating the metadata with one of the common registry identifiers, based upon a classification from a set of classification rules that is associated with a metadata type of the metadata.

A novel distributed method for machine learning is described, where the algorithm operates on a plurality of data silos, such that the privacy of the data in each silo is maintained. In some embodiments, the attributes of the data and the features themselves are kept private within the data silos. The method includes a distributed learning algorithm whereby a plurality of data spaces are co-populated with artificial, evenly distributed data, and then the data spaces are carved into smaller portions whereupon the number of real and artificial data points are compared. Through an iterative process, clusters having less than evenly distributed real data are discarded. A plurality of final quality control measurements are used to merge clusters that are too similar to be meaningful. These distributed quality control measures are then combined from each of the data silos to derive an overall quality control metric.

An information processing method implemented by a computer, the information processing method includes: acquiring an image group generated by imaging a data group according to each of a plurality of imaging methods; for each of the acquired image groups, calculating a score of the imaging method used to generate the image group, based on distribution of a first feature value group in a feature value space, and distribution of a second feature value group in the feature value space, the first feature value group being a plurality of feature values output when the image group is input to a trained model outputting feature values corresponding to input images, the second feature value group being a plurality of feature values output when a reference image group is input to the trained model; and outputting the score of the imaging method, the score being calculated for each of the image groups.

Methods, apparatus, systems, computing devices, computing entities, and/or the like for employing machine learning concepts to accurately predict categories for unseen data assets, present the same to a user via a user interface for review, and assign the categories to the data assets responsive to user interaction confirming the same.

A control method for a robot cleaner includes acquiring a plurality of images of surroundings during travel of the robot cleaner in a cleaning area, estimating a plurality of room-specific feature distributions according to a rule defined for each of a plurality of rooms, based on the images acquired while acquiring the plurality of images, acquiring an image of surroundings at a current position of the robot cleaner, obtaining a comparison reference group including a plurality of room feature distributions by applying the rule for each of the plurality of rooms to the image acquired while acquiring the image at the current position, comparing the obtained comparison reference group with the estimated room-specific feature distributions, and determining a room from the plurality of rooms having the robot cleaner currently located therein.

An information processing apparatus includes a processor configured to, in classification of a target document according to at least one classification criterion, extract significant terms from classification-criterion terms on the basis of the degrees of significance of the classification-criterion terms relative to target-document terms, the classification-criterion terms being included in the at least one classification criterion, the target-document terms being included in the target document.

A method for testing a technical system. The method includes: tests are carried out with the aid of a simulation of the system, the tests are evaluated with respect to a fulfillment measure of a quantitative requirement on the system and an error measure of the simulation, on the basis of the fulfillment measure and error measure, a classification of the tests as either reliable or unreliable is carried out.

Systems and methods are disclosed herein for determining a diagnosis based on a base skin tone of a patient. In an embodiment, the system receives a base skin tone image of a patient, generates a calibrated base skin tone image by calibrating the base skin tone image using a reference calibration profile, and determines a base skin tone of the patient based on the calibrated base skin tone image. The system receives a concern image of a portion of the patient's skin, and selects a set of machine learning diagnostic models from a plurality of sets of candidate machine learning diagnostic models based on the base skin tone of the patient, each of the sets of candidate machine learning diagnostic models trained to receive the concern image and output a diagnosis of a condition of the patient.