Methods, systems, and apparatus, including computer programs encoded on computer-readable storage media, for providing information cards using semantic graph data. In some implementations, semantic graph data for a semantic graph is stored, where the semantic graph data indicates objects and relationships among the objects, and the objects include a card object that represents characteristics of an information card. A request is received from a client device, and the request is processed using the semantic graph data. Data for the information card is provided to the client device based on the card object indicated by the semantic graph data.

Systems, computer-implemented methods, and computer program products to facilitate using multiple quota trees in resource scheduling are provided. According to an embodiment, a system can comprise a processor that executes computer executable components stored in memory. The computer executable components comprise an evaluation component that executes admissibility of a job request based on a scope property of one or more quota trees that apply to the job request.

Example implementations include a system to generate a user object, with a data processing system comprising memory and one or more processors to identify a role attribute of a user object, a geography attribute of the user object, and a temporal attribute of the user object, determine, based on the role attribute, the geography attribute, and the temporal attribute, a benefits group corresponding to the user object, generate a dynamic association of the user object to the benefits group, the dynamic association from the role attribute, the geography attribute, and the temporal attribute, and modifiable in response to a modification of one or more of the role attribute, the geography attribute, and the temporal attribute, and generate a dynamic tree structure from the dynamic association, the tree structure modifiable in response to the modification of one or more of the role attribute, the geography attribute, and the temporal attribute.

A query processing system applies transforms to convert a collection of predicate statements to two parts, a matrix and a set of reduced predicate statements, and evaluates the matrix and reduced predicate statements when querying data. The query processing system applies the transforms to extract conjunctive predicates or disjunctive predicates from the predicate statements to provide the set of reduced predicate statements and generates the matrix using the extracted conjunctive predicates or disjunctive predicates. When data is received for querying, the query processing system evaluates the matrix and reduced predicate statements for the data to provide search results. The query processing system may first identify candidate predicate statements that may be satisfied for the data from the matrix and then evaluates the reduced predicate statements for those candidate predicate statements.

A learning device is configured to perform learning of a decision tree. The learning device includes a branch score calculator, and a scaling unit. The branch score calculator is configured to calculate a branch score used for determining a branch condition for a node of the decision tree based on a cumulative sum of gradient information corresponding to each value of a feature amount of learning data. The scaling unit is configured to perform scaling on a value related to the cumulative sum used for calculating the branch score by the branch score calculator to fall within a numerical range with which the branch score is capable of being calculated.

Machine data reflecting operation of a monitored system is ingested and made available for search by a data intake and query system (DIQS). Monitoring includes obtaining a subset of ordered events that are assigned to a task. In a graphical user interface on a display, a chart for the task is displayed. The chart includes an event identifier for each event of the subset of the ordered events, a confidence level value related to each event identifier of each event of the subset of ordered events, the confidence level value indicating the confidence level that the event is in the task. The chart further includes a time reference value identifying a time of each event.

Systems and methods are provided for tracking sharing of an electronic content. An exemplary method may include receiving a request to access content associated with a web address by a user. Based on a unique identity assigned to the user and the web address, a unique tracking web address may be generated. This tracking web address may be shared with additional users. As other users request content associated with the tracking web address, information regarding the sharing of the electronic content may be determined and stored, allowing for tracking of sharing behavior of users.

A Fisher's exact test calculation apparatus includes: a condition storage 1 that has stored therein a condition for determining whether a result of Fisher's exact test corresponding to input is significant or not, the input being frequencies in a summary table; and a calculation unit 2 that obtains the result of Fisher's exact test corresponding to the frequencies in the summary table by inputting the frequencies in the summary table to the condition read from the condition storage 1.

Provided are a method and apparatus for constructing a compact translation model that may be installed on a terminal on the basis of a pre-built reference model, in which a pre-built reference model is miniaturized through a parameter imitation learning and is efficiently compressed through a tree search structure imitation learning without degrading the translation performance. The compact translation model provides translation accuracy and speed in a terminal environment that is limited in network, memory, and computation performance.

A machine learning system for training a data model to predict data states in medical orders is described. The machine learning system is configured to train a data model to predict whether a medical order requires prior authorization (“PA”) for medical orders within a medical order data set so that related systems may process incoming medical orders with PA determinations predicted by the data model. The machine learning system includes a first data warehouse system. The first prescription processing system generates a data model of historical orders and payer responses, apply a predictive machine learning model to the data model to generate a trained predictor of whether a medical order requires PA, associated with order data, apply the trained predictor to a plurality of production orders to determine PA for each of the plurality of production orders, and process the plurality of production orders with each associated PA determination.