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: Data is received that specifies a term generated by user input in a graphical user interface. Thereafter, the term is looked up in a dictionary in which there are multiple classes for terms. The term can be classified based on a first class having a top ranked effective count for the term within the dictionary when a ratio of the first class relative to a second class having a second ranked effective count for the term in the dictionary is above a pre-defined threshold. In addition, the term is classified using a machine learning model when the ratio of the first class relative to the second class is below the pre-defined threshold. Data can be provided which characterizes the classifying. Related apparatus, systems, techniques and articles are also described.

Abstract: Methods, systems, and computer-readable storage media for receiving, by a current database system, a query plan file representative of a captured query plan from a source database system, receiving, by the current database system, a set of definitions including one or more definitions, each definition in the set of definitions corresponding to an object that is implicated by the query plan, the object being included in a set of objects, and determining, by the current database system, that each definition in the set of definitions is identical to a respective definition of a corresponding object within the current database system, and in response: executing the captured query plan in the current database system to provide a query result.

Abstract: Computer-readable media, methods, and systems are disclosed for performing rollback recovery with data lineage capture for data pipelines. A middle operator receives ingested input events from a source operator reading data from an external input data source. The middle operator then logs information regarding middle input events to a middle operator input log, designating the logged middle input event information as incomplete. The middle operator then processes data associated with the middle input events and updates the middle input log entries setting them to a completed logging status designation for middle input events that were consumed to produce the one or more middle output events. The middle operator then transmits the middle output events to subsequent operators. Garbage collection is performed to remove completed entries from the middle operator output log. Finally, based on receiving a recovering message from a subsequent operator, corresponding middle output events are re-sent.

Abstract: Disclosed herein are system, method, and computer program product embodiments for correcting extracted document information based on generated confidence and correctness scores. In an embodiment, a document correcting system may receive a document and document information that represents information extracted from the document. The document correcting system may determine the correctness of the document information by processing the document to generate a character grid representing textual information and spatial arrangements for the text within the document. The document correcting system may apply a convolutional neural network on character grid and the document information. The convolutional neural network may output corrected document information, a correctness value indicating the possible errors in the document information, and a confidence value indicating a likelihood of the possible errors.

Abstract: Systems, methods, and techniques to efficiently and effectively verifying and calibrating a machine learning model. The method can include training a machine learning model by at least processing training data with the machine learning model. The method can further include manipulating a first data set of the training data and applying the manipulated first data set to the machine learning model to thereby determine a first matching rate. In addition, the method can include applying the manipulated first data set to a rule engine to thereby determine a second matching rate and determining a difference between the first matching rate and the second matching rate. The method can further include determining whether the difference is within a predefined threshold range and providing an error indication if the determined difference is outside of the predefined threshold range.

Abstract: In some implementations a method may be providing that includes importing database tables from a source system to a target system; obtaining, from a data dictionary, a first metadata about a foreign key association for a first database table to at least one of the database tables; obtaining, from a scan of code, a second metadata about an after import method for the first database table; in response to the second metadata representing that the after import method does not exist for the first database table, storing the first database table in the order table; and in response to the second metadata representing that the after import method does exist for the first database table, scanning for a first indication that the after import method corresponds to a read of at least one other database table before storing the first database table in the order table.

Abstract: Technologies are provided for determining a suitability of data payloads for training a machine learning model. A schema can be generated based on sample data payloads that have different data formats. The sample data payloads (and/or additional data payloads) can be converted to a format that conforms to the schema. Feature vectors can then be generated based on the converted data payloads, and used to determine a suitability of the data payloads for training a machine learning model. If the data payloads are sufficiently suitable, the converted data payloads can be used to train the machine learning mode. Otherwise, the schema may be annotated and new converted payloads may be generated based on the annotated schema. The feature vector generation and suitability analysis can then be repeated.

Abstract: Technologies are described for determining between synchronous and asynchronous modes for computing service requests. Computing service requests are received by a computing service from clients. The computing service dynamically determines whether to use synchronous mode or asynchronous mode for processing the computing service requests. The computing service makes the dynamic determination of which mode to use (synchronous or asynchronous) based on various criteria, which can include synchronous/asynchronous mode recommendations generated by machine learning models and/or synchronous/asynchronous mode recommendations generated by static rules.

Abstract: Methods, systems, and computer-readable storage media for executing, by a policy controller, a policy custom resource that defines a policy to be applied to target resources within a cluster of a cloud computing platform, the policy custom resource including a policy target defining parameters for identifying a resource as a target resource, a policy condition defining conditions of a target resource that are to be evaluated, and a policy action defining actions that are to be executed in response to the conditions evaluating to true, receiving, by the policy controller, a set of target resources based on the policy target of the policy custom resource, evaluating, by the policy controller and for each target resource in the set of target resources, the policy condition, and in response to the policy condition returning as true for a target resource, executing the policy action for the target resource.

Abstract: Systems and methods include determination of a first value to be blinded, determination of a first key value, generation of a first composite value based on the first value and the first key value, performance of a hash operation on the first composite value to generate a first hash value, seeding of a pseudorandom generator with the first hash value to generate a first pseudorandom value, truncation of the first hash value based on the first pseudorandom value to generate a first truncated value, and generation of a blinded value associated with the first value based on a blinding function comprising the first value and the first truncated value.

Abstract: Provided are systems and methods of a compiler that efficiently processes semantic analysis. For example, the compiler may perform semantic analysis on as much of the source code as possible during compile time. For any instructions, such as dynamic expressions, that are not known at compile time, the compiler may encode semantic bytecode for performing the semantic checks on such dynamic expressions, and their dependent expressions, during execution/runtime of the program. In one example, the method may include compiling source code of a program into bytecode, identifying, during the compiling, a dynamic expression that includes one or more dependent static expressions within the source code, generating semantic bytecode for semantic analysis of the one or more dependent static expressions of the dynamic expression, and adding the semantic bytecode to the bytecode of the program.

Abstract: A tenant separation architecture is provided to adopt tenant separation for remote integration of various OEM systems with a cloud-based analytics application. The tenant separation architecture provided herein allows for tenant separation in a wide range of modules in different OEM integration scenarios through a token-base authentication. Application server tenants and database tenants may be accessed for application of the tenant separation. Moreover, the tenant separation implementations disclosed herein may be applied to different modules for metadata, tenant configuration, authorization concepts, and general data protection regulation (GDPR) adoption.

Abstract: The present disclosure describes methods, systems, and computer program products for providing additional stack trace information for time-based sampling (TBS) in asynchronous execution environments. One computer-implemented method includes determining whether time-based sampling is activated to capture a time-based sampling data during execution of a JavaScript function; in response to determining that the time-based sampling is activated to capture the time-based sampling data, determining whether a callback stack trace is active; in response to determining that the callback stack trace is active, loading the callback stack trace; retrieving a current stack trace of the JavaScript function; and saving the loaded callback stack trace and the current stack trace of the JavaScript function as the time-based sampling data.

Abstract: 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 apply, to an instance of an in-memory database running on a cluster of computing nodes, one or more temporary changes to a configuration of the in-memory database. In response to the configuration request, an indication to deprecate the configuration profile may be added to a configuration profile associated with the instance of the in-memory database. The configuration profile may be deprecated based at least on the configuration profile being associated with the indication. The deprecating including restoring, to a state prior to the application of the one or more temporary changes, the configuration of the in-memory database. Related systems and articles of manufacture, including computer program products, are also provided.

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: Provided is a system and method for generating job posting content through machine learning. In one example, the method may include storing text content of previous postings, receiving target attributes of a candidate that is a subject of a new posting, identifying, via a machine learning model, a subset of previous postings from among the previous postings which are most closely related to the new posting based on the target attributes of the candidate with respect to content of the previous postings, and detecting text objects from the identified subset of previous postings and outputting a display of the detected text objects.

Abstract: Provided is a system and method for detecting a SQL command from a natural language input using neural networks which works even when the SQL command has not been seen before by the neural networks. In one example, the method may include storing a candidate set comprising structured query language (SQL) templates paired with respective text values, reducing, via a first predictive network, the candidate set into a subset of candidates based on a natural language input and the text values included in the candidate set, selecting, via a second predictive network, an SQL template from among the subset of candidates based on the natural language input and text values included in the subset of candidates, and determining a SQL command that corresponds to the natural language input based on the selected SQL template and content from the natural language input.

Abstract: Implementations include providing, by a computer-executed migration advisor executing within a run-time of a source database system, a query data set including queries processed by the source database system during production use of the source database system, providing, by the migration advisor, an object data set including data representative of database objects stored within a database of the source database system, generating, by the migration advisor, a list of query-level features and a list of object-level features, each feature in the list of query-level features and each feature in the list of object-level features including a feature that is deprecated in a target database system, resolving one or more issues represented by features of one or more of the list of query-level features and the list of object-level features, and executing migration of the database of the source database system to the database of the target database system.

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.