Described are methods, systems and computer readable media for data object publishing/subscribing via a multicast key-value protocol.

A processing system including at least one processor may obtain a first set of performance records of a database system, train a machine learning model in accordance with the first set of performance records, where the machine learning model that is trained in accordance with the first set of performance records is configured to predict a latency of a query transaction for a designated time period, present a user interface with a plurality of settings of the database system that are user-adjustable, where the plurality of settings is associated with at least a portion of the first set of performance records, calculate a first predicted latency of a query transaction at the designated time period via the machine learning model in accordance with a set of values of the plurality of settings, and present the first predicted latency via the user interface.

A method for managing the configuration of an in-memory database running in a cluster of computing nodes may include receiving a configuration request to configure an instance of the in-memory database. The configuration request may include an identifier of a configuration profile specifying a value for each of one or more parameters of the in-memory database. The presence of the instance of the in-memory database and the configuration profile specified in the configuration request may be verified. Upon verifying the presence of the in-memory database and the configuration profile, the instance of the in-memory database may be configured based the configuration profile. The configuring may include setting, to the value specified in the configuration profile, the one or more parameters of the in-memory database. Related systems and articles of manufacture, including computer program products, are also provided.

A method for deleting a set of keys from a storage server is provided. The method includes generating a probabilistic data structure for a first set of keys and for each key in a second set of keys, determining whether a key of the second set of keys is found in the probabilistic data structure. The method includes identifying the key as a candidate for deletion if the key is not found in the probabilistic data structure. A system is also provided.

Techniques described herein may support a learned ontology or meaning for user, organization, or customer specific data. According to the techniques described herein, a set of datasets corresponding to an entity may be processed to generate a master dataset including rows that include at least a field name and a value corresponding to the field. The master dataset is processed to generate a corpus of text strings that is input into a word embedding function which generates a set of vectors based on the corpus. Because the configuration of the text string positions values by field names and field values, implicit relationships and contexts are identified within the data using the word embedding function.

Systems and techniques are provided for database table annotation processing. An annotation processing engine on a computing device may receive an instruction to build a database table. The annotation processing engine on the computing device may receive annotations made to the database table from a storage of the computing device. The annotations may include labels identifying features of the database table. The annotation processing engine on the computing device may determine a database server engine compatible with the database table. The annotation processing engine on the computing device may generate a query in a native language of the database server engine compatible with the database table based on the instruction to build the database table and the annotations made to the database table. The annotation processing engine on the computing device may send the query in the native language of the database server engine to the database server engine.

A computing device of a database system includes a plurality of processing modules, a computing device operating system, and an application specific operating system. The application specific operating system includes at least one custom instruction set that configures operation of a configurable set of processing modules of the plurality of processing modules based on generating, for each processing module of the configurable set of processing modules, a corresponding configuration signal indicating a selected instruction set of either the computing device operating system or the application specific operating system. Each processing module of the configurable set of processing modules operates in accordance with the selected instruction set based on the corresponding configuration signal.

A method includes receiving a query expression indicating an expression evaluation for performance upon output of a filtering operator. An operator execution flow indicating performance of the filtering operator serially after the expression evaluation is generated, and execution of this operator execution flow is facilitated by generating an exception column indicating an exception value generated for each of a set of rows based on performing the expression evaluation. The filtering operator of the operator execution flow can be applied by identifying a subset of the set of rows that meet filtering parameters of the filtering operator. When the exception value of at least one row in the subset indicates an error condition, execution of the operator execution flow is aborted. When the exception value of every row in the subset indicates no error, the subset of the set of rows is outputted and execution of the operator execution flow continues.

Techniques support graph pattern matching queries inside a relational database management system (RDBMS) that supports SQL execution. The techniques compile a graph pattern matching query into a SQL query that can then be executed by the relational engine. As a result, techniques enable execution of graph pattern matching queries on top of the relational engine by avoiding any change in the existing SQL engine.

In one embodiment, a mobile client system may determine its location. The mobile client system may store the location in a location history in a memory of the mobile client system, where the location history comprises one or more geographic locations and one or more time stamps corresponding to each of the geographic locations. The mobile client system may detect its current status based at least in part on whether the mobile client system is stationary. The mobile client system may send the location history to a location server of an online social network based at least in part on the current status of the mobile client system and a power requirement for sending the location history to the location server.