A system and method for calculating and organizing a plurality of document objects with associated workflow objects for a plurality of user devices whereby a deadline calculator and an object manager calculate missing information based on historical records and corresponding rules and user settings. The plurality of user devices capable of managing, document object actions such as accept, deny, or pause document objects or actions, driven by pre-configured parameters and internal system rules related to single or ongoing deadlines and/or other triggers. A proximity manager to manage deadline triggers based on a plurality of rules based on date distances from computed deadlines.

A method comprises: generating, at a first computing device, a first set of gradient values associated with a data block processed by nodes of a machine learning model, the first set of gradient values being in a first data format; determining a first shared factor from the first set of gradient values, the first shared factor being in a second data format of a lower a precision than that of the first data format; and scaling the first set of gradient values with the first shared factor, to obtain a second set of gradient values having the second data format. In addition, the method comprises sending the second set of gradient values and the first shared factor to a second computing device; and, in response to receiving a third set of gradient values and a second shared factor from the second computing device, adjusting parameters of the machine learning model.

Systems and methods for maintaining a project schedule in a dataset management system are provided. For example, a progress update comprising an indication of a status of training of a first machine learning algorithm may be received using at least one communication device from an external device involved in the training the first machine learning algorithm. A project schedule record may be accessed. An expected running time of the training of the first machine learning algorithm may be updated in the project schedule record based on the status of the training of the first machine learning algorithm. Information related to training of a second machine learning algorithm may be updated in the project schedule record based on the status of the training of the first machine learning algorithm.

In managing multiple versions of triggers, a database system creates a first version of a trigger to apply a first set of actions in response to a first triggering event, which includes: creating a first package for the first version of the trigger to include the first set of actions; linking the first package to the database object; and setting the first package as a current version of the trigger. The database system creates a second version of the trigger to apply a second set of actions to the database object in response to a second triggering event, which includes: creating a second package for the second version of the trigger to include the second set of actions; and linking the second package to the database object. In response to a command, the database system sets the current version of the trigger to the second package.

In a method of controlling access to secured data, a repository operatively coupled to one or more databases storing secure data is employed to intercept a user query of one database of the one or more databases. A user who generated the user query and a user role assigned to the user is automatically determined from the intercepted query. The intercepted query is parsed. Security information of the identified objects is looked up in a metamodel stored in the one or more databases. Based on the determined user role and the identified objects to be filtered out of the user query, an expression tree to filter out secure data is automatically built and the user query is modified by appending the expression tree to the user query. The modified query is applied to the one database.

Methods and apparatus for conditional master election in a distributed database are described. A plurality of replicas of a database object are stored by a distributed database service. Some types of operations corresponding to client requests directed at the database object are to be coordinated by a master replica. Client access to the database object is enabled prior to election of a master replica. In response to a triggering condition, a particular replica is elected master. The master coordinates implementation of operations with one or more other replicas in response to client requests.

A method includes receiving data collected from a plurality of managed devices in a plurality of data collections. The data collections are performed using a plurality of collection protocols. A trigger that generated each of given ones of the data collections is determined. The method further includes identifying a collection protocol of the plurality of collection protocols used for each of the given ones of the data collections, and determining one or more attributes of a plurality of attributes of the plurality of managed devices that have been collected using given ones of the collection protocols. A mapping is generated between the triggers, the collection protocols and the attributes using one or more machine learning algorithms. The generated mapping is used to predict one or more collection protocols of the plurality of collection protocols to use to collect data from one or more of the managed devices.

A computer-implemented technique is described herein for facilitating a user's repeated execution of the same computer-implemented actions. The technique performs this task by determining patterns in the manner in which the user repeats requests associated with certain computer-implemented actions. For example, the technique determines context-dependent patterns in the manner in which the user submits search requests to a search system. The technique then leverages those patterns by proactively providing a request-assistance tool to the user in those context-specific circumstances in which the user is likely to perform the repetitive computer-implemented actions. The digital action-assistance tool provides various kinds of assistance to the user in performing the repetitive computer-implemented actions.

The subject technology tracks a plurality of queries corresponding to a plurality of query plans based on join operations contained in each of the plurality of queries and a previous time of executing each query. The subject technology selects a first query plan among the plurality of query plans. The subject technology determines a value indicating an estimated improvement in execution time of the first query plan in comparison to a previous execution time of a previous query plan. The subject technology attempts to execute a first query using the first query plan. The subject technology determines that a second query plan selected among the plurality of query plans has a second estimated execution time that is less than an estimated execution time of the first query plan. The subject technology executes the first query corresponding to the first query plan at a subsequent time using the second query plan.

A computer-implemented method for generating by a query engine a graph of operators for a SPARQL query over an RDF graph. The method includes obtaining a graph of operators executable by the query engine, the graph comprising a plurality of basic operators, at least two of said operators being of a first type each configured to find RDF triples of the RDF graph that match a respective basic graph pattern. The method further comprises identifying a group of operators among the at least two basic operators of the graph which are of the first type. The respective basic graph patterns of the group of operators have same subject and/or predicate and/or object and the identified group of operators is replaced in the graph by an equivalent operator configured to find RDF triples of the RDF graph that match the respective basic graph patterns of the group of operators.