Abstract: In an event-driven architecture, at design time a central catalog allows event consumer(s) to reconstruct an event structure for later processing during runtime. The central event catalog receives from a publisher (of an event producer), an event description document specifying an event identifier, source code of an event class extracted from an event, and an annotation. This event description document is stored by the catalog as a central instance in a database. Upon receiving a query from an event consumer regarding the event, the event description document is returned as a query result. A code generator (of the event consumer) creates the event structure from the event description document, allowing processing of the event. Embodiments are particularly suited to centrally locate a single, up-to-date instance of an event description document, for reference in allowing multiple consumers of an event to accurately reproduce the latest version the structure of that event.

Abstract: Search assistance personalized to a user, may be afforded by combining the application of scoring functions, with the training a Machine Learning (ML) model based upon feedback from that user. A plurality of scoring functions are applied to query results to calculate a plurality of score vectors. The ML model is then applied to generate a ranked list of vectors. Feedback from the user, is incorporated to develop an evolving memory. The feedback may be explicit, or may be implicit—e.g., based upon user selection of particular score vector(s) from the list or based upon user selection of particular query results corresponding to the score vectors. Embodiments may enhance relevance of search assistance by removing past feedback from the evolving memory that is used to retrain the ML model. Embodiments can provide assistance to search text corpuses utilizing scoring functions considering frequency of occurrence of particular words or terms.

Abstract: Programs, systems, and methods are described for providing on-demand applications to users by infrastructure as a service platforms. A request catcher may receive a request to start an application that runs on a managed system that is scheduled to be off. The request catcher or a management application may verify that the user has authorization to access the application and request to the management application to start the managed system. The management application may start the managed system and the managed system may then provide access to the application to the user.

Abstract: Techniques and solutions are described that can be used to determine data subsets of a data set that may be responsive to a query. In one aspect, imprints are created for the data subsets, where an imprint indicates whether, for a plurality of bins, a data subset contains values within respective bins. The bins are defined at least in part using data for a set of queries. In another aspect, at least one bin of a plurality of bins is designated to be associated with values of the data set satisfying a threshold frequency. In a further aspect, imprints are created for a plurality of bins, where an imprint indicates whether the plurality of data subsets comprise a value for a given bin. A query imprint can be compared with the data subset imprints or bin imprints to determine data subsets that may be responsive to an associated query.

Abstract: The present disclosure involves systems, software, and computer implemented methods for improved design and implementation of data access metrics for automated physical database design. An example method includes identifying a database workload for which index advisor access counters are to be tracked. Each SQL statement in the database workload is executed. For each SQL statement, attribute sets are determined for which a selection predicate filters a result for an SQL statement. An output cardinality of each selection predicate is determined. A logarithmic counter for an attribute set corresponding to the selection predicate is determined based on the output cardinality of the selection predicate. The determined logarithmic counter is incremented. Respective values for logarithmic counters of the determined attributes are provided to an index advisor. The index advisor determines attribute sets for which to propose an index based on the logarithmic counters of the respective attribute sets.

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: The present disclosure pertains to parsing addresses using a deep learning model. The deep learning model may be trained using a plurality of reference address records and a plurality of modifications to the plurality of reference address records. In some implementations, the model can be a multi-layer bi-directional long short-term memory recurrent neural network model. The output of the model may include indications of an address component type for each character of the address. The output can be parsed based on the indications of the address component type to obtain a plurality of labeled address components for the address. The labeled address components can be compared to reference address records to determine a matched address record identifying a geographic location.

Abstract: Some embodiments provide a non-transitory machine-readable medium that stores a program. The program parses a knowledge graph comprising a plurality of nodes and a plurality of edges to identify metadata associated with the plurality of nodes and the plurality of edges. Each edge in the plurality of edges connects a pair of nodes in the plurality of nodes. Each node in the plurality of nodes represents an entity. Each edge in the plurality of edges connecting a particular pair of nodes in the plurality of nodes represents a relationship between two entities represented by the particular pair of nodes. The program further determines a set of token definitions. The program also generates a set of syntax rules based on the identified metadata and the set of token definitions.

Abstract: Techniques for implementing a systematic installation of software extensions are disclosed. In some embodiments, a computer system performs operations comprising: detecting a request to open a target file of a base computer program on a client machine, the target file residing on a server machine; determining that the target file includes a target version of a software extension; determining that a user of the client machine is part of a user group having administrative permission for the base computer program; determining that a systematic installation criterion from a group of systematic installation criteria is satisfied; and installing the target version of the software extension on the client machine based on the determining that the target file includes the target version, the determining that the user is part of the user group, and the determining that the systematic installation criterion is satisfied.

Abstract: A computer system may obtain reference ticket data for reference tickets of an online system, with the reference ticket data indicating a reference issue for a reference component and a reference solution, and also obtain learning content items from a learning management system. The computer system may then create a mapping between the reference issues indicated by the reference ticket data for the reference tickets and the learning content items using an unsupervised machine learning algorithm. The computer system may detect target ticket data that has been provided by a user to the online system via a computing device, with the target ticket data indicating a target issue for a target component, identify a target solution for the target issue based on the target ticket data using the mapping, and cause a recommendation of the target solution to be displayed on the computing device.

Abstract: Extensions to a source software application are migrated to a target. Template records are constructed containing corresponding sets of attributes for source and target applications. Analysis of a source extension yields source extension attributes. A matching template record is used to map the source extension attributes to corresponding target extension attributes. A proposal for a migrated target extension is formulated, simulated in a sandbox, and committed to the target application. Client interaction supports selection or approval of application versions, proposals, or simulation output. Template records, constructed without knowledge of specific extensions implemented on a particular application instance, are provided to the source application environment, enabling remaining migration operations to be performed locally in source and target environments, maintaining data privacy. Examples are provided for a front-end software application in a database environment.

Abstract: In an example embodiment, a solution is used to provide for a highly efficient image dispatch system for container-based in-memory databases in a database-as-a-service framework that provides storage and bandwidth cost savings and introduces advanced features of container techniques. This solution makes best use of the advantages of the concurrent and distributed capabilities of in-memory databases in a database-as-a-service framework. A container operator manager provides operators for various uses, such as the “diff” operator and the “merge” operator. A dispatch manager acts as a centralized service for diff content maintenance, patch publication and management of dispatch worker nodes. A patch worker on each node has the capabilities to execute the specified patching and validation between the images located on each side of the in-memory database in the database-as-a-service framework.

Abstract: Technologies are described for managing database queries based on bill of materials (BOM). For example, a set of database queries can be optimized (e.g., in terms of time and sequence of execution) based on a BOM explosion. In some implementations, database queries are managed by generating a BOM for an application programming interface (API) call. Various data structures can be created from the BOM, which can comprise procedure information, application and action information, and/or hierarchy information. A logical sequence of execution can be determined based, at least in part, on one or more of the data structures, where the logical sequence of execution comprises a plurality of database queries to execute the API call. The logical sequence of execution can be optimized to produce an optimized sequence of execution. The API call can be executed according to the optimized sequence of execution.

Abstract: Methods, systems, and computer-readable storage media for providing a VM vector representative of resources that are to be consumed by the VM within the cloud computing environment, comparing the VM vector to each physical node vector in a set of physical node vectors to determine a distance, each physical node vector representative of resources available by a respective physical node, and selecting a physical node from a set of physical nodes based on a respective distance between the VM vector and a physical node vector representative of the physical node, and in response, automatically: deploying the VM to the physical node within the cloud computing environment at least partially by installing the VM on the physical node for execution thereon.

Abstract: Embodiments may be associated with database access. In some embodiments, a machine learning linear regression training platform determines a set of database properties (e.g., direct and/or indirect properties such as a document type, a type of process, a number of items, etc.) associated with Online Transaction Processing (“OLTP”) database table access. The machine learning linear regression training platform may then train a linear regression model based on the set of database properties and prior locking information (e.g., locking and unlocking timestamps) that represent access to the OLTP database table. Information about the linear regression model may be output to a wait time estimation platform that adapted to use the linear regression model to generate, in substantially real-time, an estimated wait time for an OLTP database table access based on the database properties of the OLTP database table access.

Abstract: Computer-implemented systems and computer-implemented methods include the following. A request to train a machine-learning (ML) model is received at a training broker. Anonymized data for training the model is obtained by the training broker from each individual data source of a plurality of data sources. The anonymized data is accessed through a data science schema being provided by anonymization of sensitive information of production data from each individual data source. Access to the anonymized data is provided to a data vendor for training the ML model using the anonymized data.

Abstract: A method, a system, and computer program product for application function library installation for cloud-based systems. An application function in a plurality of application functions is executed. The application function is executed using a server instance running in a container. A determination is made that execution of the application function requires an application function library. The application function library for executing of the application function is obtained. The obtained application function library is loaded during runtime of the application function.

Abstract: A data processing pipeline may be generated to include an orchestrator node, a preparator node, and an executor node. The preparator node may generate a training dataset. The executor node may execute machine learning trials by applying, to the training dataset, a machine learning model and/or a different set of trial parameters. The orchestrator node may identify, based on a result of the machine learning trials, a machine learning model for performing a task. The execution of the data processing pipeline may be optimized. Examples of optimizations include pooling multiple machine learning trials for execution at a single executor node, executing at least some machine learning trials using a sub-sample of the training dataset, and adjusting a proportion of trial parameters sampled from a uniform distribution to avoid a premature convergence to a local minima within the hyper-parameter space for generating the machine learning model.

Abstract: Systems and methods are provided for generating a rank vector comprising an array of values for each term position of a document and setting a value at a position of each term identifier in the document to a distance score for each term identifier, calculating snippet frame quality scores for each frame of a plurality of frames in the rank vector, determining whether a length of the character string associated with the frame with the highest frame quality score is similar to a requested length for a snippet, if necessary, adjusting the size of the character string to be similar to the requested length for the snippet and, once the length of a character string for one or more frames is similar to the requested length for the snippet, generating the character string to be provided as a snippet.

Abstract: In an example embodiment, an additional interface customer resource definition (CRD), which operates in conjunction with the normal CRD, is utilized. The interface CRD may be called a service CRD. The service CRD provides an abstraction of the original CRD by abstracting away all technical details that no other services should depend upon. The service CRD provides a façade to the original CRD. Both are kept in sync by a component called an operator, which infers the specification of the original CRD on the basis of the specification of a given service CRD. Furthermore, status updates sent to the original CRD that are relevant to the dependent services are mirrored back to the corresponding service CRD. Correspondingly, status updates with technical details that are too specific for the dependent services are not mirrored back.