Techniques for request throttling in a provider network environment are described. A throttle handler controls whether requests will be processed through maintaining a token-based record, per type of request, having a token value indicative of a number of requests that can be processed over a time period. For a request, the token value of the token-based record corresponding to the request type is updated based on calculating an elapsed time between a last update time of the token-based record and the current time, calculating an intermediate token value as the existing token value plus a value of the elapsed time multiplied by a rate, and updating the token value to be the minimum between the intermediate token value and a burst value. The request is serviced when the updated token value is determined to be greater than or equal to a number of tokens needed to perform the request.

A computer generates and displays a data visualization in a data visualization user interface according to placement of data fields, from a data source. The data visualization includes visual data marks representing data from the data source. The computer detects a user input to select a visual data mark. In response to detecting the user input, the computer obtains a data model encoding the data source as a tree of logical tables. The computer identifies one or more aggregated data values for the visual data mark, each of the aggregated data values corresponding to a respective data field in the data model. For each of the aggregated data values, the computer retrieves a respective disaggregated set of data rows from a respective logical table containing the respective data field. The computer displays a summary grid, with a respective tab corresponding to each of the retrieved disaggregated sets of data rows.

A network storage volume stores first entries in a first-mode storage bucket and a second entries in a second-mode storage bucket. The first-mode storage bucket has first bucket metadata, and the second-mode storage bucket has second bucket metadata. A computer-implemented method includes comparing a utilized capacity of the network storage volume to a target capacity information of the network storage volume to obtain a comparison result. Based on the comparison result, at least one bucket is selected to be purged from the buckets of the network storage volume based at least in part on bucket metadata of the buckets. The method further includes causing a purge of the at least one selected bucket from the network storage volume.

An illustrative computing system for a weblink content scanning system scans an electronic message for the presence of one or more weblinks. The computing system accesses, in a sandbox computing environment, content linked to the one or more weblinks. The computing system generates a hash of the accessed content and/or content linked to weblinks accessible via the accessed content. The computing system scans the content accessed via the one or more weblinks for a presence of malicious content and categorizes the scanned content accessed via the one or more weblinks (e.g., safe, malicious, and the like), associates the categorization with each corresponding hash, and saves such information to a data store for future analysis. Based on a result of this analysis, the computing system allows delivery of the original electronic message or generates a modified electronic message for delivery to a recipient device.

A system and method are presented in which known genetic attributes associated with a condition are used to seed the determination of additional attributes which are associated with the condition. Based on the learning, the additional attributes (genetic, behavioral, or both) provide for an increased correlation between the combined attributes and the condition. For behavioral attributes, a measure of the impact of the behavioral attribute on the risk of the condition can be transmitted to another device or system.

Artificial intelligence decision making neuro network core system and information processing method using the same include an electronic device linking to a unsupervised neural network interface module, a asymmetric hidden layers input module linking to the unsupervised neural network interface module and a neuron module formed with tree-structured data, a layered weight parameter module linking to the neuron module formed with tree-structured data and an non-linear PCA (Principal Component Analysis) module, an input module of the lead backpropagation unit linking to the non-linear PCA module and a tuning module, an output module of the lead backpropagation unit linking to tuning module and the non-linear PCA module; when the electronic device receives raw data, processing and learning the raw data via all the modules, and updating programs to generate decision results that accommodate a variety of scenarios, in order to elevate the reference value and practicality of the decision result.

A diamond asset comprising one or more diamonds and an encryption chip is used to asset-back a cryptographic token that can be used to conduct transactions. The cryptographic token is written to a blockchain using a smart contract that is configured to enable a transaction associated with the token in response to two or more of: a signature by the encryption chip, a signature by the owner of the diamond asset, and a validation of a visual layout of the diamond asset.

Distributed pseudo-random subset generation includes obtaining a data-query indicating a first table having a first column including unique values, a second table having a second column including unique values, a join clause joining the first table and the second table on the first column and the second column, and a limit value, pseudo-random filtering the first table to obtain left intermediate data and left filtering criteria, pseudo-random filtering the second table to obtain right intermediate data and right filtering criteria, obtaining intermediate results data by full outer joining the left intermediate data and the right intermediate data, obtaining results data by filtering the intermediate results data using most-restrictive filtering criteria among the left filtering criteria and the right filtering criteria, and outputting the results data, wherein outputting the results data includes limiting the cardinality of rows of the results data to be at most the limit value.

Systems and methods for processing natural language inputs to determine user intents using an insights repository are provided. An insights repository system is configured to build an insights repository as a data structure representing a plurality of entities and relationships among those various entities. The insights repository system may receive information from various sources via an event stream, and may process the information using event rules. Based on the application of the event rules, the system may configure an insights repository data structure representing various entities, relationships between various entities, and the strengths of relationships between various entities. After the insights repository is created, consumers may execute queries against the insights repository. Furthermore, the insights repository system may automatically query the insights repository to generate insight information to be published to an insight feed to which consumer systems may subscribe to receive automatic updates.

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.