Abstract: Systems and methods include establishment of a first database session with a first database user, determine first session variable values associated with the first database session, the first session variable values including a first tenant value, determine a first workload class based on the first tenant value and on zero or more of the other first session variable values, the first workload class including first parameter values specifying first resource consumption limits, and manage resource consumption of the first database session based on the first parameter values.

Abstract: Systems, methods, and computer media are described for index-based join size estimation. For a join operation between two tables, a filter is applied to the first table, resulting in a filter output. The filter output is then sampled. For each sample, an index for a second table is accessed and counts of records in the second table that match the sample are retrieved. Using the sample size and the retrieved counts from the index of the second table, a data size for the join operation can be efficiently and accurately estimated. Statistical confidence in the estimate can also be assessed using variance-based calculations.

Abstract: Techniques and solutions are described for facilitating the use of machine learning techniques. In some cases, filters can be defined for multiple segments of a training data set. Model segments corresponding to respective segments can be trained using an appropriate subset of the training data set. When a request for a machine learning result is made, filter criteria for the request can be determined and an appropriate model segment can be selected and used for processing the request. One or more hyperparameter values can be defined for a machine learning scenario. When a machine learning scenario is selected for execution, the one or more hyperparameter values for the machine learning scenario can be used to configure a machine learning algorithm used by the machine learning scenario.

Abstract: Methods, systems, and computer-readable storage media for notarized communication between platform systems is provided. A first data object is notarized by storing a first root hash value at a blockchain network. The first root hash value is notarized in response to a transaction request received through a first notarization interface instantiated at a first platform system. A subscription for the first platform system is established to provide notification events in relation to a second data object. The first root hash value comprises an object reference to a second root hash value for the second data object. A notification event is sent for the second data object to the first platform system, comprising a third root hash value of a third data object associated with the second data object. The third data objects is notarized at the blockchain network by the second platform system through a second notarization interface.

Abstract: A system and method of query processing in a multi-level storage system having a unified table architecture. A query is received by a common query execution engine connected with the unified table architecture, the query specifying a data record. The common query execution engine performs a look-up for the data record based on the query at the first level storage structure. If the data record is not present at the first level storage structure, the common query execution engine performs separate look-ups in each of the second level storage structure and the main store.

Abstract: Techniques and solutions are provided for determining changes to networks formed from network objects. Changes to network objects, such as addition or deletion operations are received and associated with timestamps. Last data changes for respective network objects are determined, including a last data change for a particular type associated with a respective networked object, where a networked object may be concurrently associated with a plurality of types. A first network object is removed from a first network, and contributions of the first network object to edges in the first network are removed. A second network object is associated with an addition operation and at least one edge is determined that connects the second network object and a third network object. Sets of disconnected network objects are determined. Network objects are associated with an identifier identifying a network that includes a respective network object.

Abstract: Some embodiments provide a non-transitory machine-readable medium that stores a program. The program receives sensor data from a set of sensors. Each sensor in the set of sensors is configured to sense a physical quantity in an environment. Based on the sensor data, the program further determines a set of configurations for a set of smart devices. The set of smart devices includes a set of smart emergency devices installed in a building. Each smart emergency device in the set of smart emergency devices is configured to provide emergency exit information to guide exiting the building. The program also sends the set of configurations to the set of smart devices.

Abstract: Computer-readable media, methods, and systems are disclosed for processing log entries in an in-memory database system employing tenant-based, group-level encryption for a plurality of tenants. A request to generate a database transaction log record is received. A log entry handle corresponding to the allocated log buffer is provided. In response to determining that the transaction log record to be written into the log buffer contains tenant-specific content, certain content requiring group-level encryption is flagged. An encryption group identifier is received, and the tenant-specific content is encrypted with a corresponding group-level encryption key. The group-level encryption group identifier is appended to the transaction log header, and log data containing the log buffer is encrypted with one or more encryption keys. Finally, the encrypted log data is persisted and subsequently read, unencrypted, and replayed under appropriate circumstances.

Abstract: A method, a medium, and a system for distributing a warehouse task from a warehouse management hub to a programmable logic controller comprise receiving at least one warehouse task at a warehouse management hub including attributes for physically transferring at least one product into a warehouse, out of the warehouse or within the warehouse, determining, by the warehouse management hub and based on the attributes of the warehouse task, one of a plurality of warehouse management devices for processing the warehouse task, sending, by the warehouse management hub, the warehouse task to the determined warehouse management device, identifying, by the determined warehouse management device and based on the attributes of the warehouse task, a first one of a plurality of programmable logic controllers, and sending, by the determined warehouse management device, a message including information for carrying out the warehouse task to the identified programmable logic controllers.

Abstract: Datasets are available from different dataset servers and often lack well-defined metadata. Thus, comparing datasets is difficult. Additionally, there might be different versions of the same dataset which makes the search even more difficult. Using systems and methods described herein, quality scores, dataset versioning, topic identification, and semantic relatedness metadata is stored about datasets stored on dataset servers. A user interface is provided to allow a user to search for datasets by specifying search criteria (e.g., a topic and a minimum quality score) and to be informed of responsive datasets. The user interface may further inform the user of the quality scores of the responsive datasets, the versions of the responsive datasets, or other metadata. From the search results, the user may select and download one or more of the responsive datasets.

Abstract: A diagnostics tool aids in the efficient collection of relevant error data for addressing faults in connected software systems. Context information is collected from a software system that is being displayed. Configuration of the software system is collected, and used to identify relevant connected software systems. Error data is collected via respective log interfaces from the error logs of the software system being displayed, and relevant connected systems. The context, configuration, and error data is stored in a database. Based at least upon the configuration information, a query is formulated and posed to the database. A corresponding query result is received and processed to return an error report to a user interface, for inspection (e.g., by a user or a support staff member). Certain embodiments may further generate an appropriate recommendation based upon the query result. The recommendation may be generated with reference to a stored ruleset and/or artificial intelligence.

Abstract: A system includes reception of a query execution plan associated with a plurality of query execution pipelines, estimated execution costs and estimated intermediate result cardinalities, determination of one or more precedence relationships of the plurality of query execution pipelines, determination of an execution order of the plurality of query execution pipelines based on the estimated execution costs, the estimated intermediate result cardinalities, and the one or more precedence relationships, and providing of the execution order of the plurality of query execution pipelines and the query execution plan to a query execution engine.

Abstract: The present disclosure involves systems, software, and computer implemented methods for configuring graph query parallelism for high system throughput. One example method includes receiving a query to be executed against a graph database. System properties are determined of a system in which the query is to be executed. Algorithmic properties are determined of at least one algorithm to be used to execute the query. Graph data statistics are determined for the graph database. Graph traversal estimations are determined for a first iteration of the graph query and an estimated cost model is determined for the first iteration based on the graph traversal estimations. Estimated thread boundaries are determined for performing parallel execution of the first iteration. Work packages of vertices to be processed during the execution of the first iteration are determined based on the first estimated cost model and the work packages are provided to a work package scheduler.

Abstract: Technologies are described for facilitating database system recovery in a distributed database environment having a master node and at least one slave node. The master node maintains an in-memory reference that includes transaction identifiers and their corresponding commit identifiers. When a slave node requests that a commit identifier be assigned to a transaction, the transaction and commit identifiers for the transaction are added to the in-memory reference. The commit identifier is sent by the master node to the slave node. The slave node writes a log entry to a commit log that includes the transaction identifier and the assigned commit identifier. If the database system is interrupted before the slave node writes the commit log entry, the slave node can request the commit identifier for the transaction from the master node, which retrieves the identifier from the in-memory reference.

Abstract: Provided are a system and method that use machine learning to recommend adjustments to digital documents that are included within a report managed by a software application. The system may also provide a trend analysis as part of the recommendation. In one example, the method may include detecting a request to open a digital document from a user via a user interface, populating the user interface with content from the digital document and executing a machine learning model on values within the digital document to identify a value among the values that is to be adjusted to a different value based on previous adjustments by the user to previous reports, activating a user interface element associated with the identified value within the user interface, and in response to a selection of the user interface element, instantiating a display of the different value on the user interface.

Abstract: The present disclosure involves systems, software, and computer implemented methods for proactively predicting demand based on sparse transaction data. One example method includes receiving a request to predict transaction quantities for a plurality of transaction entities for a future time period. Historical transaction data for the transaction entities is identified for a plurality of categories of transacted items. The plurality of categories are organized using a hierarchy of levels. Multiple levels of the hierarchy are iterated over starting at a lowest level. For each current level in the iteration, features to include in a quantity forecasting model for the current level are identified. The quantity forecasting model is trained using the identified features. Predicted transaction dates are predicted for the current level by a transaction date prediction model. The quantity forecasting model is used to generate predicted quantity information for the current level for the predicted transaction dates.

Abstract: A computer implemented method can execute a first query plan for a query, obtain statistics for internal nodes of a first query tree representing the first query plan, receive a second query tree representing a second query plan for the query, search for a matching internal node of the first query tree for a selected internal node of the second query tree, and responsive to finding the matching internal node of the first query tree, apply the statistics for the matching internal node of the first query tree to the selected internal node of the second query tree for estimating cost of the second query plan during query optimization of the query. Related systems and software for implementing the method are also disclosed.

Abstract: An in-memory computing system for conducting on-line transaction processing and on-line analytical processing includes system tables in main memory to store runtime information. A statistics service can access the runtime information using script procedures stored in the main memory to collect monitoring data, generate historical data, and other system performance metrics while maintaining the runtime data and generated data in the main memory.