Abstract: Techniques for integrating applications using a container image of an integration flow are disclosed.

Abstract: Methods, systems, and computer-readable storage media for providing a first instance of a database system within the cloud computing environment, determining that scale out of the database system is to be executed, and in response, providing a second instance of the database system within the cloud computing environment, the first instance and the second instance collectively defining a cluster, establish a remote direct memory access (RDMA) connection between the first instance and the second instance, copying data stored within the first instance to a data store of the second instance over the RDMA connection, and configuring the first instance to replicate a first transaction log to the second instance over the RDMA connection, and receiving, by each of the first instance and the second instance, requests from one or more client devices.

Abstract: A method includes receiving a message query from an entity identifier participating in a social network. The message query specifies one or more entities, one or more requirements, and one or more constraints. A set of message query parameters is generated based on the message query. A set of queries for a semantic graph of the social network is generated based on the set of message query parameters. The set of queries is applied to the semantic graph to obtain a set of query results. A message context of the entity identifier is determined based on the set of query results and the set of message query parameters. A set of messages from a message repository is determined based on the message context. The set of messages can be presented on a client computer associated with the entity identifier.

Abstract: Computer-readable media, methods, and systems are disclosed for generating and debugging bytecode for a rule including embedded debug functions. One or more predefined breakpoints are included in the embedded debug functions. When a breakpoint is reached, an execution state for the rule is stored including one or more local variables for the rule. Execution may be resumed by retrieving the stored execution state.

Abstract: Disclosed herein are system, method, and computer program product embodiments for remotely executing a robotic process automation (RPA) bot as a function. An embodiment operates by generating an RPA bot function in a first network environment and transmitting the RPA bot function to an application located in a second network environment. The embodiment further operates by receiving results data from the application based on an execution of the RPA bot function in the second network environment. Subsequently, the embodiment operates by generating results user interface (UI) data based on the results data and transmitting the results UI data to a user input/output (I/O) device located in the first network environment.

Abstract: Disclosed herein are system, method, and computer program product embodiments for validating input requests. In an embodiment, an application program interface (API) can receive a input request from a client device or server. The API can validate the input requests based on client-side or server-side technology implemented by the client device or server. In response to validating a input request from the client device and server, the database's framework can validate the database request before processing the input request. In response to failing to validate an input request at the client device, server, or database level, a machine-learning algorithm can be used to determine how to resolve a possible error, which may have caused the failure to validate the input request.

Abstract: Techniques and solutions are described for implementing virtual table schemas. A virtual table schema includes a logical pointer that is used to target a table that includes data and is defined according to the virtual table schema. Values assigned to the logical pointer can be used to target tables at different locations, such as in-memory storage or in dynamic storage of a database management system. The dynamic storage includes an in-memory buffer cache and persistent storage. The logical pointer can be updated to point to the in-memory storage or the dynamic storage, including based on use statistics. Similarly, a table having data for the virtual table can be transitioned between the in-memory buffer cache and the persistent storage, including based on use statistics for the virtual table. A value for the logical pointer can be updated when the table is transferred between the in-memory buffer cache and the dynamic storage.

Abstract: A system may include a nested probabilistic data structure that contains nested tables, each table having electronic records associated with different hash functions. A data analytics engine may receive information about a detected action, including action characteristics. In response to the detected action, insertions are executed into the nested probabilistic data structure at multiple levels based on the action characteristics (each insertion incrementing an entry in the data structure selected using the hash functions). The engine then continues to monitor for detected actions. Upon receipt of a query associated with the monitored action (including a query type), a key and level of nesting associated with the query is determined based on the query type. The hash functions are executed on the determined key to retrieve a value from each electronic record in an appropriate nested table, and a minimum of the retrieved values is output in response to the query.

Abstract: Provided is a system and method for dynamic configuration of a multi-objective optimization function and identifying an optimal set of records based thereon. In one example, the method may include receiving a set of data records and priority values to be applied to the set of data records, generating an objective function from an objective function template stored in a memory device, wherein the generating comprises dynamically configuring parameter values of the objective function based on the priority values, executing the objective function on the set of data records and identifying an optimal subset of data records from among the set of data records based on the dynamically configured parameter values of the executing objective function, and displaying identifiers of the identified optimal subset of data records.

Abstract: This application provides an example method, system, and computer-readable medium for identifying potential account take over fraud attacks through monitoring of user credential login attempts across a network of websites. One example method includes identifying a login attempt to a particular website. The method further includes determining whether the login user credentials correspond to site-specific user credentials for the particular website. The method also includes in response to determining that the login user credentials correspond to the site-specific user credentials, determining whether the login attempt to the particular website is allowed by a first allowance rule associated with the first RTW, and in response to determining that the login attempt to the particular website is allowed by the first allowance rule, setting a first allowance indicator to indicate that the login attempt to the particular website is to be allowed by the first allowance rule.

Abstract: In an example embodiment, a process is introduced into a machine learned model where additional results are output by the machine learned model in addition to those results that would be obtained through use of the trained model itself. In some example embodiments, these additional results may be random or semi-random to introduce results that might otherwise not have been recommended by the machine learned model. By introducing such additional results in a controlled way, it becomes possible to reduce biases caused by a self-reinforcing feedback loop while still presenting users with accurate machine learned model results.

Abstract: A data consistency analyzer identifies data inconsistencies proactively, providing insights into the financial, business, and technical impact to enable users to make decisions regarding resolution of the inconsistencies. The data consistency analyzer may provide additional insights such as criticality of inconsistencies. Data inconsistencies may be caused by configuration changes to master data. As described herein, data consistency is improved by keeping a constant watch on various master data changes which is one of the major sources towards causing inconsistency and taking necessary actions based on further assessments. Using master data change as initial lead, the inconsistencies would be identified that are not mere database or technical inconsistencies. Along with the primary option of inconsistency check based on master data change, additional options to scan the system based on overall run and run per selection, or application level specific checks will be provided.

Abstract: A computer implemented method can receive a parameterized query written in a declarative language. The parameterized query comprises a parameter which can be assigned different values. The method can perform a first compilation session of the parameterized query in which the parameter has no assigned value. Performing the first compilation session can generate an intermediate representation of the parameterized query. The intermediate representation describes a relational algebra expression to implement the parameterized query. The method can perform a second compilation session of the parameterized query in which parameter has an assigned value. Performing the second compilation session reuses the intermediate representation of the parameterized query.

Abstract: A request is received to perform an exploration in a predictive and maintenance service application. A sequence of explorations is added in an evidence package. The evidence package includes the list of micro services. The sequence of explorations in the evidence package are analyzed. Based on the analysis, a user behavior corresponding to the performed exploration is identified. The user behavior is provided as input to the machine learning algorithm. Configuration data corresponding to the exploration and the evidence package is stored in a configuration database. The machine learning algorithm is executed in an execution engine. The execution engine is a micro service. Based on execution of the machine learning algorithm, the list of micro services is automatically identified as recommendations. The list of micro services is displayed as recommendations in the predictive and maintenance service application.

Abstract: A method, a system, and a computer program product for generating and/or using one or more workflow managers in computing systems. A request to generate a computing workflow is received. The workflow includes a plurality of computing functions. A plurality of configuration parameters associated with the workflow are determined based on the received request. The configuration parameters define a configuration of execution of one or more functions. One or more connection objects for connecting the functions to one or more data sources storing data required for execution of the functions are identified. The plurality of functions are arranged for execution in a predetermined order using the determining plurality of configuration parameters and identified connection objects. The predetermined order is specified using one or more configuration parameters. The workflow is generated, compiled and executed.

Abstract: In an example embodiment, machine learning models are trained and used to predict a growth classification of time fields and category fields of application tables of Enterprise Resource Planning (ERP) software databases. These predictions can then be used to forecast future technological needs or the future table size more precisely.

Abstract: A computer system includes one or more database search models configured to search data contained in a plurality of database tables. The one or more database search models can include a plurality of structural containers and one or more search enabling containers. The plurality of structural containers can represent objects having a structural relationship and contain property data of the objects. The property data of the objects can be obtained from the plurality of database tables. The plurality of structural containers can be shared by the one or more database search models. The one or more search enabling containers can correspond to the one or more database search models and specify a scope for searching data and a format for presenting search results.

Abstract: The present disclosure relates to computer-implemented methods, software, and systems for providing extension application mechanisms. Memory is allocated for a virtual environment to run in an address space of an application that is to be extended with extension logic in a secure manner. The virtual environment is configured for execution of commands related to an extension functionality of the application. A virtual processor for an execution of a command of the commands is initialized at the virtual environment. The virtual processor is operable to manage one or more guest operating systems (OS). A first guest OS is loaded at the allocated memory and application logic of the extension functionality is copied into the allocated memory. The virtual environment is started to execute the first guest OS and the application logic of the extension functionality in relation to associated data of the application in the allocated memory.

Abstract: Disclosed herein are system, method, and computer program product embodiments for accessing a database collection. In some embodiments, an application generates a database collection. The database collection is configured to store documents (e.g., records). The application generates a database table definition. The database table definition is a proxy or adapter for the database collection. The application receives a request to be executed to be executed on the database collection from a client device. The request specifies the database table definition. The application accesses the database collection, via the database, to process the request using the database table definition.