Abstract: Using an action registry to edit data across users and devices is described. In one or more implementations, a user editing data in an application requests to share the data with another user or another device for further editing. Responsive simply to the request or based on an exchange of content transfer information, the data is communicated to the selected user or device. Using information maintained by an action registry, a list of applications capable of editing the data is presented to the selected user or via the selected device. Upon selection of an application from the list, the selected application is launched. Once changes are made to the data by the selected user or device, those changes can be saved and the changed data automatically returned to the original user and/or device. The changed data can then be automatically presented to the original user or on the original device.

Abstract: Database environments may choose to schedule complex analytics processing to be performed by specialized processing environments by caching source datasets or other data needed for the analytics and then outputting results back to customer datasets. It is complex to schedule user database operations, such as running dataflows, recipes, scripts, rules, or the like that may rely on output from the analytics, if the user database operations are on one schedule, while the analytics is on another schedule. User/source datasets may become out of sync and one or both environments may operate on stale data. One way to resolve this problem is to define triggers that, for example, monitor for changes to datasets (or other items of interest) by analytics or other activity and automatically run dataflows, recipes, or the like that are related to the changed datasets (or other items of interest).

Abstract: A technique for managing data objects in a storage cluster includes splitting a data object into multiple portions at boundaries within the data object. The technique further includes transforming the portions of the data object into segments that provide individually processable units, and distributing the segments among multiple computing nodes of the storage cluster for storage therein.

Abstract: Techniques for configuring managed event tables include generating at a first process of an execution node, log data associated with execution of user-defined function (UDF) code. The log data is provided from the first process to a second process of the execution node. The first process is configured as a sub-process of the second process. The log data is formatted using the second process of the execution node, to generate formatted log data. The formatting is based on a configuration of a managed event table that is external to the execution node. The formatted log data is communicated from the second process of the execution node into a managed event table maintained at a computing node that is external to the execution node.

Abstract: Herein is acceleration for JavaScript object notation (JSON) documents in a relational database based on multilevel, multifield, multivalued indexing and querying. In an embodiment, into a table in a relational database, a computer stores many hierarchical data objects that respectively contain multiple levels that respectively contain one or more fields. An index is generated for indexed values in fields in at least two of the multiple levels. Based on the index, a database statement that references the table in the relational database and at least one of the indexed fields is executed. Indices herein provide matching to more fields in more levels of hierarchical data objects thereby decreasing or eliminating further filtering by brute force after index access. Matching ordinal positions within an array field needs no further filtration after index access. A single index provides increased spatial locality of index entries for acceleration. Index maintenance is minimized for acceleration.

Abstract: Methods that provide dynamic index management for a set of computing storage resources are disclosed herein. One method includes collecting, by a processor, a set of current performance data for a set of storage resources storing data and implementing a set of indexes for the data stored on the set of storage resources based on an optimized performance predicted for the set of storage resources based on the collected set of current performance data and a set of predicted performance data that identifies the set of indexes. Also disclosed herein are apparatus, systems, and computer program products that can include, perform, and/or implement the methods for providing dynamic index management for a set of computing storage resources.

Abstract: The subject technology receives first metadata corresponding to a set of micro-partitions. The subject technology stores a first data structure and a second data structure in storage as a first file and a second file, first data structure including the first metadata and a second data structure including second metadata, the first metadata corresponding to a set of micro-partitions, the second metadata for a grouping of the first metadata, the second data structure including information associating the second metadata to the first metadata. The subject technology stores third metadata for a table, the third metadata comprising information about data stored in a micro-partition of the table.

Abstract: A system according to certain aspects may include a client computing device including: a database application configured to output a database file in a primary storage device(s), the database application outputting the database file as a series of application-level blocks; and a data agent configured to divide the database file into a plurality of first blocks having a first granularity larger than a second granularity of the application-level blocks such that each of the first blocks spans a plurality of the application-level blocks. The system may include a secondary storage controller computer(s) configured to: in response to instructions to create a secondary copy of the database file: copy the plurality of first blocks to a secondary storage device(s) to create a secondary copy of the database file; and create a table that provides a mapping between the copied plurality of first blocks and corresponding locations on the secondary storage device(s).

Abstract: Described is an improved approach to implement an offline learning approach for machine learning that employs a window-based technique for predicting values within the window, and where outliers are identified and discarded from consideration. This approach efficiently permits offline learning to be employed in a manner that minimizes false positives, while also improving the quality of the data should retaining be required.

Abstract: Consumers often desire to compare themselves to other individuals. Disclosed herein are systems and methods of generating and displaying comparison information to users so that they can determine if they are “keeping up with the Joneses.

Abstract: Embodiments of the present disclosure provide methods, systems, and apparatuses for rapid geographic search in an actor-based geographic search network.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable storage media for implementing a scalable, secure, efficient, and adaptable distributed digital ledger transaction network. Indeed, the disclosed systems can reduce storage and processing requirements, improve security of implementing computing devices and underlying digital assets, accommodate a wide variety of different digital programs (or “smart contracts”), and scale to accommodate billions of users and associated digital transactions. For example, the disclosed systems can utilize a host of features that improve storage, account/address management, digital transaction execution, consensus, and synchronization processes. The disclosed systems can also utilize a new programming language that improves efficiency and security of the distributed digital ledger transaction network.

Abstract: Techniques for indexing large scale datasets are described. A method for indexing large scale datasets can include receiving, by an indexing service, a request to generate an index for a dataset stored in an data storage service, the request including indexing information for the dataset, determining, by the indexing service, an index type based at least on the dataset, generating, by the indexing service, the index based at least on the indexing information and the index type, and receiving, by the indexing service, a request from a query service to identify a subset of the dataset using the index.

Abstract: A method is provided for processing data in a database, wherein the database includes a first table and a second table. Each of the tables have a plurality of rows of data, wherein a key identifies one or more rows of data in the tables. There is a plurality of matching key pairs among the tables. Each key pair includes a key of a first table, and a matching key and a unique row of a second table. In operation, the method involves simultaneously processing row data of key pairs associated with a first row of the second table, and then simultaneously processing row data of key pairs associated with any remaining rows of the second table in sequential row order.

Abstract: A method is provided for batch processing data that is stored in multiple tables and is organized in the tables as a plurality of rows of data and a plurality of columns. Each row is identified by a key, and each column represents a field having a unique field name. The batch processing is performed using batch processing logic. In operation, the batch processing is performed by reading out data from only a portion of a row from each of the tables that is to be used for the batch processing logic by specifying the key of the row from the respective table, and the unique field names in the row of the respective table to be used for the batch processing logic. The remaining portion of the data in the row of the respective table is not read out from the row. Batch processing is then performed on the read out data using the batch processing logic. The batch processed data is then written back into the same row of the respective table that the data was read out from.

Abstract: A recommendation method includes generating a feature sequence based on to-be-predicted data of a user for a target object and according to a preset encoding rule, obtaining probability distribution information corresponding to each feature in the feature sequence, and obtaining, through calculation, a feature vector corresponding to each feature, obtaining a predicted score of the user for the target object based on values of N features and a feature vector corresponding to each of the N features, and recommending the target object to the user when the predicted score is greater than or equal to a preset threshold.

Abstract: The present disclosure provides a method of user clustering, and the method includes: acquiring a clustering condition for a predetermined user group, wherein the clustering condition includes a time selecting condition and an event selecting condition; determining at least one target time period for each user behavior data in a user behavior database based on the time selecting condition; determining association data indicating a relationship between the each user behavior data and each target time period based on the each user behavior data and the each target time period; and selecting target association data for a time period to be monitored based on the time period to be monitored and the event selecting condition, so as to determine a target user belonging to the predetermined user group according to the target association data. The present disclosure also provides an apparatus of user clustering, a computer device and a non-transitory medium.

Abstract: Two streams of data items are received. A first estimated processing time for the first stream of data items and a second estimated processing time for the second stream of data items are determined. Data items of the first stream and data items of the second stream are dynamically maintained in a first buffer and a second buffer respectively. The data items of the second stream maintained in the second buffer have associated event times that are within a first join window based on the first estimated processing time for the first stream. A selected data item of the first stream maintained in the first buffer is joined with one or more data items of the second stream maintained in the second buffer that have associated event times that are within a second join window based on an event time associated with the selected data item of the first stream.

Abstract: The present invention relates to the field of database management systems, and in particular to a method and system for executing queries in a runtime. Embodiments of the invention provide a computer-implemented method, computer program product, and system for database management. The system exposes the underlying storage structures used to manage the data. The system also supports running a query native to a language runtime.

Abstract: Techniques for partitioning data from a data stream into batches and inferring schema for individual batches based on the field values of each batch are disclosed. The system may infer different schemas corresponding to different batches of data records even though the batches are received from a common data stream or a common data source. The system may infer a schema by determining whether a field contains single values or multiple values. Then the system determines the field type(s) associated with the values. These determinations are then stored in a dictionary generated for each batch.