Techniques and solutions are described for facilitating the use of machine learning techniques. In some cases, filters can be defined for multiple segments of a training data set. Model segments corresponding to respective segments can be trained using an appropriate subset of the training data set. When a request for a machine learning result is made, filter criteria for the request can be determined and an appropriate model segment can be selected and used for processing the request. One or more hyperparameter values can be defined for a machine learning scenario. When a machine learning scenario is selected for execution, the one or more hyperparameter values for the machine learning scenario can be used to configure a machine learning algorithm used by the machine learning scenario.

A system and method including receiving sets of sensor data associated with sensors configured to monitor one or more systems. Sensor fingerprints are generated for each set of sensor data based on the sensor data. At least one proximity value is computed for each sensor by comparing the fingerprint of that sensor with another fingerprint. Clusters of similar sensors are identified based at least upon the proximity values of the sensors.

A system and method for visualizing and querying high dimensional data to a user. The system includes a user device, a data-pearls visualization and querying server. The server obtains the high dimensional data from the user device associated with user. The server generates data clusters and sub-divides the data clusters into non-overlapping subsets of data-pearls using a clustering technique. The server selects a shape for each data-pearl by comparing a distance between centroid of a data-pearl and a farthest point from a determined centroid using L.sub.p norm distance measures. The server configures each data-pearl in a three-dimensional plot. The server enables the user to visualize the data-pearls on a screen of the user device. The server queries data based on a query using data dimension technique. The server dimensions data related to the query through determined classifiers based on filtered data after pruning unrelated data to the query.

Provided are methods, systems, and computer readable media for user interaction with database methods and systems. In an aspect, a user interface can be generated to permit dynamic display generation to view data. The system can comprise a visualization component to dynamically generate one or more visual representations of the data to present in the state space.

Systems and methods for automated and explainable machine learning to generate seamlessly actionable insights by generating explainable personas directly from customer relationship management systems are disclosed. The personas are defined as a collection of segments, scored by likelihood to generate good opportunities, accompanied ranked profile attribute importance, with descriptive names and summaries, associated human and database readable queries which have been generated to optimally find cluster candidates in a broader data universe. Such a system would effectively and accurately model the composition of past clients, perform the categorization in an explainable way such that actions can be taken on the information to have predictable results. What is further required are the mean to categorize small text components, trained over dependent and independent model sets, to enable a cleaner and more explicit representation of information rich short-strings, in order to facilitate a more meaningful representation of the user profiles.

Aspects of the subject disclosure may include, for example, observing a plurality of objects viewed through a smart lens, wherein the plurality of objects are in a frame of an image viewed by the smart lens, determining an identification for an object of the plurality of objects, assigning tag information for the object based on the identification, storing the tag information for the object and the frame in which the object was observed, receiving a recall request for the object, retrieving the tag information for the object and the frame responsive to the receiving the recall request, and displaying the tag information and the frame. Other embodiments are disclosed.

Systems and methods for automated data curation and presentation are disclosed herein. The system can include a memory including a structured database and a plurality of storage bins. The system can include at least one server that can receive a packetized data file generated from a data file. This packetized data file can include a first packet generated from a content file of the data file, a second packet generated from metadata of the data file, and a third packet generated from a payload of the data file. The at least one server can automatically generate at least one tag for the packetized data file, which at least one tag is automatically generated based at least one key phrase identified in at least the targeted portion of the content file. The at least one server can index the packetized data file according to the at least one tag into a predetermined taxonomy, receive a data request including a plurality of parameters identifying attributes of packetized data, and deliver curated data selected according to the at least some of the plurality of parameters of the data request.

In one example, a computer implemented method may include retrieving resource definition data corresponding to an endpoint. The resource definition data includes resource type information. Further, an API response may be obtained from the endpoint by querying the endpoint using an API call. Furthermore, the API response may be parsed and a resource model corresponding to the resource definition data may be populated using the parsed API response. The resource model may include resource information and associated metric information correspond to a resource type in the resource type information. Further, a resource and/or metric data associated with the resource may be determined using the populated resource model. The resource may be associated with an application being executed in the endpoint.

Example object storage systems, bookkeeping engines, and methods provide quota usage monitoring for control entities, such as accounts, users, and buckets. An object data store is configured to enable control entities to access data objects associated with each control entity. Data objects are mapped to the control entities and the data objects are processed to identify object usage values corresponding to each combination of data object and control entity. Total usage values are calculated for each control entity and used to determine a data object access response for a target data object and associated control entities.

In one example, a computer implemented method may include retrieving resource definition data corresponding to an endpoint. The resource definition data includes adapter information and resource type information. Further, an adapter instance may be generated using the adapter information to establish communication with the endpoint. Furthermore, an API response may be obtained, via the adapter instance, from the endpoint by querying the endpoint using an API call. Further, the API response may be parsed. Further, a resource model corresponding to the resource definition data may be populated using the parsed API response. The resource model may include resource information and associated metric information corresponding to a resource type in the resource type information. Furthermore, a resource and/or metric data associated with the resource may be determined using the populated resource model. The resource may be associated with an application being executed in the endpoint.