Abstract: Synchronized multi-system template provisioning is provided for networked computer systems, such as connected in a cloud environment. Each template includes information about corresponding templates and databases in other connected systems. When a template is deployed, user customization information is sent to the related systems to automatically deploy corresponding templates using the same or similar user customization information. Accordingly, when a template is deployed, the system checks for compatible templates in related systems and sends all information necessary for deploying the corresponding customizable templates in the other systems.

Abstract: Despite the increase of memory capacity and CPU computing power, memory performance remains the bottleneck of in-memory database management systems due to ever-increasing data volumes and application demands. Because the scale of data workloads has out-paced traditional CPU caches and memory bandwidth, one can improve data movement from memory to computing units to improve performance in in-memory database scenarios. A near-memory database accelerator framework offloads data-intensive database operations via or to a near-memory computation engine. The database accelerator's system architecture can include a database accelerator software module/driver and a memory module with a database accelerator engine. An application programming interface (API) can be provided to support database accelerator functionality. Memory of the database accelerator can be directly accessible by the CPU.

Abstract: Innovations in the area of server-side processing when committing transactions to disk in a distributed database system can improve computational efficiency at database nodes and/or reduce network bandwidth utilization. For example, when transactions are committed in a database system, at a master node of the database system, a server uses different threads for certain processor-intensive operations and certain I/O-intensive operations.

Abstract: A method for machine learning based database management is provided. The method may include training a machine learning model to detect an anomaly that is present and/or developing in a database system. The anomaly in the database system may be detected by at least processing, with a trained machine learning model, one or more performance metrics for the database system. In response to detecting the presence of the anomaly at the database system, one or more remedial actions may be determined for correcting and/or preventing the anomaly at the database system. The one or more remedial actions may further be sent to a database management system associated with the database system. Related systems and articles of manufacture are also provided.

Abstract: Methods, systems, and computer-readable storage media for receiving a first identifier associated with an incident arising in a first software system, transmitting a first query to an architecture metadata repository, receiving, from the architecture metadata repository, first architecture metadata at least partially including a first architecture graph that is a computer-readable data representation of the first software system and cross-link data representative of one or more dependencies between the first software system and a second software system, transmitting a second query to the architecture metadata repository, the second query including a second identifier assigned to the second software system, receiving second architecture metadata representative of the second software system, and displaying at least a portion of the first architecture metadata and at least a portion of the second architecture metadata in a display.

Abstract: Various examples are directed to systems and methods for utilizing local identifiers in a distributed database system. A database management system server may receive first execution unit data describing a first execution unit of a first database query. The first execution unit data may comprise first operand data describing a set of operands for the first execution unit. The database management system server may determine that the first operand data describes at least one operand from a first partition and at least one operand from a second partition. The database management system server may generate a first set of local identifiers describing rows at the first partition and a first set of local identifiers describing rows at the second partition. The database management system server may execute the first execution unit based at least in part on the first set of local identifiers describing rows at the first partition and the first set of local identifiers describing rows at the second partition.

Abstract: Execution of an exclusive lock of a page in a database is initiated. Thereafter, subsequent to the initiation of the execution of the exclusive lock, at least one non-exclusive lock for the page in the database is received. The execution of the exclusive lock is selectively timed out based on at least one savepoint statistic. While the exclusive lock is timed out, at least one of the received non-exclusive locks is executed. Further, the exclusive lock can be restarted after the execution of the at least one of the received non-exclusive locks.

Abstract: Provided is a system and method for retrieving a translation of a source word based on context. For example, the context may include other words in a same file as the source word. The context can be used to identify the correct semantic meaning of the source word when the word has multiple contextual meanings. In one example, the method may include identifying a source word from a data file, determining a plurality of translation candidates for the source word which translate the source word from a source language into a different language, retrieving a target translation candidate for the source word from among the plurality of translation candidates based on context of the source word included in the data file, and outputting the translated target translation candidate for display via a display device.

Abstract: Methods, systems, and computer program products for accessing a database element are described. A local schema comprising a table link is created and the table link is used to access a database table via a persistency interface.

Abstract: Techniques and solutions are described for defining transformation specifications in a programming-language independent language and converting such specifications to one or more executable formats. The language can provide for defining rules and actions. Rules can refer to (e.g., be based at least in part on) data targets, such as attributes of a schema, whose identifiers are to be read or updated, or to other rules. Rules can be reused, and can recursively refer to one another, such that a large number of complex schema transformations can be accomplished using a series of first order logic statements. Actions can define what, and how, values will be changed when a predicate rule is satisfied. A transformation specification in the language can be parsed and selectively complied to one or more executable formats, including in programming languages such as the structured query language. Disclosed technologies can facilitate data transformations by non-technical users.

Abstract: Linguistic expressions for training a chatbot can be generated in an automated system via linguistic expression templates that are associated with intents. The pre-categorized linguistic expressions can then be used for training and validation. Chatbot development can thus be improved by having a large number of expressions for development, leading to a more robust chatbot. In practice, the process can iterate with modifications to the templates until a suitable benchmark is met. The technique can be applied across human languages to generate chatbots conversant in any number of languages and is applicable to a variety of domains.

Abstract: A query that requests a filter attribute is received by a database server from a remote application server that is associated with at least one calculation scenario that includes calculation nodes having relational operators and calculation nodes having non-relational operators. The calculation nodes having relational or non-relational operators are optimized by a calculation engine model optimizer and the calculation nodes having relational operators are further optimized by a relational database optimizer. Thereafter, the operations defined by the optimized calculation nodes can be executed to result in a responsive data set. Next, the data set is provided to the application server by the database server.

Abstract: An application file defines a first data object that identifies a fast healthcare interoperability resources (FHIR) server and a second data object that identifies a data type associated with the FHIR server. Based on the first data object and the second data object, a FHIR model requests data of the data type from the FHIR server. The FHIR server provides the requested data, which is received by the FHIR model. Based on the received data, the FHIR model generates data in a user interface (UI) control format and provides the generated data to a UI module that causes a UI to be presented. The FHIR model supports binding of complete entities such as patients or organizations to a UI. This binding enables the requesting of whole entities from a FHIR endpoint, provision of the entity data to the UI, and updating the FHIR data.

Abstract: A method, a system and a computer program product for executing management of ownership of data. An index in a plurality of indexes is selected. The index corresponds to a plurality of ranges of data values stored in a plurality of database slices of a database. The index further corresponds to a partitioning structure that includes a plurality of hierarchically arranged nodes. Each node corresponds to a range of data values stored in at least one database slice. The structure is replicated across a plurality of computing systems. A computing system executes an update to one or more ranges of data values. The system replicates at least one of a database slice including the updated ranges of data values and a node that includes the updated ranges of data values to another computing system for storage of a replicate of the updated ranges of data values.

Abstract: Methods and apparatus, including computer program products, are provided for adapting user interfaces. In one aspect, there is provided a method. The method may include monitoring at least one action at a user interface including a page to enable a determination of a response time representative of an amount of time for an application to respond to the at least one action at the user interface including the page; determining, based on at least the response time, a frustration index; and adapting, based on the determined frustration index, at least a portion of the user interface including the page. Related apparatus, systems, methods, and articles are also described.

Abstract: Techniques are described for efficient execution of analytical queries on large amounts of data in a parallel database cluster while making maximal use of the available hardware.

Abstract: In one aspect, a method is described that includes receiving, at a data warehouse, a query associated with a view for a report being presented at a client device user interface; processing, by the data warehouse, the query to identify data responsive to the query; evaluating the identified responsive data to determine whether there is corresponding metadata indicating a portion of the identified responsive data includes blocked natural persons data; when the blocked natural persons data is determined, evaluating the corresponding metadata to determine whether the block is a total block or a partial block; generating, based on the evaluated metadata, a result set including a predetermined value to mask the blocked natural persons data; and providing the generated result set including the predetermined result set to a server handling the view. Related systems, methods, and articles of manufacture are also disclosed.

Abstract: A method, a system and a computer program product for execution of queries. A first query is executed using a first query execution plan to generate a first result data by applying a first query operator to a first data stored in a first storage location. The first result data is provided to a second query execution plan for execution of a second query. The second query is executed on a second data stored in a second storage location in accordance with the second query execution plan. The second query execution plan includes a second query operator. Based on the execution of the second query, a second result data is generated in accordance with the second query execution plan by at least applying the second query operator to at least the provided first result data.

Abstract: Techniques and solutions are described for detecting malicious database activity, such as SQL injection attempts. A first machine learning classifier can be trained by comparing processed and unprocessed user input, where a difference between the two can indicate suspicious or malicious activity. The trained classifier can be used to analyze user input before query execution. A second machine learning classifier is trained with a data set that includes call stack information for an application requesting execution of a dynamic query and query statistics associated with processing of the query at the database. The query of the application can be correlated with a corresponding database query by hashing the application query and the database query and comparing the hash values, where matching hash value indicate a common query. The trained classifier can monitor execution of future queries to identify queries having anomalous patterns, which may indicate malicious or suspicious activity.

Abstract: Techniques and solutions are described for facilitating the use of machine learning techniques. In some cases, a system suitable for providing a machine learning analysis can be different from a remote computer system on which training data for a machine learning model is located. A machine learning task can be defined that includes an identifier for at least one data source on the remote computer system. Data for the at least one data source is received from the remote computer system. At least a portion of the data is processed using a machine learning algorithm to provide a trained model, which can be stored for later use. Data on the remote computing system can be unstructured or structured. Particularly in the case of structured data, a remote computer system can make updated data available to the machine learning task.