Abstract: Techniques and solutions are described for providing flexible access to data during execution of a data access request. Multiple instances of a data artifact are created, where different instances of the data artifact provide access to different data sources having data associated with the data access request. When a data access request is executed, a particular data artifact instance can be used during execution of the data access request. In some cases, switching logic can be used to determine which data artifact instance is to be used in executing the data access request. Also described are technologies for facilitating creation of data artifact instances corresponding to a modelling artifact.

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: 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: 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: 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: 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: 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: 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: 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: 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: 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: Disclosed herein are various embodiments for a sensitive data management system. An embodiment operates by receiving an HTTP request for an interface. A plurality of tiles, including both tiles associated with sensitive data and non-sensitive data, are identified for display on the interface. An access profile associated with providing access to the sensitive data is identified, the access profile including one or more requirements, associated with the HTTP request. Request information in the HTTP request corresponding to the one or more requirements of the access profile is identified. The identified request information is compared to the one more requirements of the access profile. A determination is made whether the identified request information satisfies the one more requirements of the access profile based on the comparing. At least one of: the second tile or the first tile and the second tile are provided for display on the interface based on the determination.

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: Various embodiments for a data management system for managing inferences are described herein. An embodiment operates by detecting a signal to perform a replication task. Parameters corresponding to the replication task are identified. The replication task is configured to at least partially replicate a source data table from a source repository to a target data table in a target repository, based on the parameters. It is determined that a columnar structure of a first column of the source data table including data to be replicated matches a columnar structure of a second column of the target data table where the data is to be replicated to. The replication task is executed responsive to the determination of the matching structures, the replication task including performing one or more arithmetic operations on the data.

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 identifying trends in the behavior of execution of services in a cloud platform environment and support alert triggering for expected outages prior their occurrence. Metrics data for performance of the cloud platform is continuously obtained. Based on evaluation of the obtained metrics data, the performance of the cloud platform is tracked over time to identify a trend in a performance of a first service on the cloud platform. The identified trend in the performance is compared with a current performance rate of the first service. Based on an evaluated difference between the current performance rate and the identified trend, the difference is classified into an issue-reporting level associated with a prediction for an outage at the first service. A notification for the trend is reported.

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.