Abstract: Methods and systems are disclosed for remote code execution between computing environments having various dissimilarities. Client requests at a source environment lead to calls of various types lacking immediate support within the source environment. Calls are delegated to the target environment, where function schemas, data schemas, or copies of source-side schemas are variously used to reconcile called source-side entities with their target-side counterparts. Inconsistencies between name and data representations and datatypes are also addressed. Serially communicated (JSON) parameters are resolved into required target-side multi-level parameter structures, and vice versa. Expedited bulk data access to/from a database layer is supported. A use case is described for phased migration of a software application. Generic and extensible remote code execution allows rapid migration, adaptation, or deployment of software applications across diverse computing environments.

Abstract: The present disclosure pertains to validation of runtime objects for a software deployment using a certificate. After creating the runtime objects during a build process, a certificate may be generated based on the runtime objects. The certificate may include a fingerprint of the runtime objects that may be used before deployment to determine whether the runtime objects have been changed. Before deployment, the runtime objects and the certificate may be obtained and the certificate may be validated. In addition, the runtime objects may be validated using the fingerprint included in the certificate. For instance, the fingerprint may be re-generating based on the runtime objects for deployment. The runtime objects may be validated by comparing the re-generated fingerprint to the fingerprint in the certificate. The runtime objects may be deployed if the certificate and the runtime objects are valid.

Abstract: Disclosed herein are system, method, and computer program product embodiments for replicating data from table in a source database to a target database. In some embodiments, data replication includes access plan delimitation and access plan calculation steps and is performed on a table having multiple partitions. A table may be divided into one or more partitions and each partition may be further divided into one or more access plans. Access plan delimitation may involve calculating, in parallel, boundaries of access plans within partitions of the table. Access plan calculation may be initiated on the first partition that has completed the access plan delimitation steps, and may involve transferring data from each delimited partition from the table in the source database to the target database.

Abstract: The present disclosure involves systems, software, and computer implemented methods for executing integration scenario regression tests in customer landscapes. One example method includes identifying a request to create a test case for an integration scenario for a cloud platform customer. The test case is created for the scenario, including enabling the test case to run in an isolated customer environment specific to the customer. An update to the cloud platform is identified. The update is provisionally applied to the cloud platform for the customer. The test case is executed in the isolated customer environment, to test the scenario for the customer. A determination is made as to whether execution of the test case succeeded. In response to determining successful test case execution, the update to the cloud platform is finalized for the customer. In response to determining unsuccessful test case execution, the update is rolled back for the customer.

Abstract: Methods, systems, and computer-readable storage media for receiving, by an anomalous operation detection service, current signal data representing a driving current applied to a device over a time period, processing, by an anomalous operation detection service, the current signal data through a deep neural network (DNN) module, a frequency spectrum analysis (FSA) module, and a time series classifier (TSC) module to provide a set of indications, each indication in the set of indications indicating one of normal operation of the device and anomalous operation of the device, processing, by an anomalous operation detection service, the set of indications through a voting gate to provide an output indication, the output indication indicating one of normal operation of the device and anomalous operation of the device, and selectively transmitting one or more of an alert and a message based on the output indication.

Abstract: Embodiments provide automated security scanning of incoming images for use in creating containers such as a Virtual Machines. Based upon attribute(s) of metadata of the incoming image, a security engine chooses from amongst stored scripts for mounting and execution by installation logic. Such scripts can relate to the scanning itself, and/or pre-scanning considerations (such as scan frequency). In one example, the meta data attribute identifies a relevant Operating System (OS) of the incoming image. Other meta data attributes such as •scan frequency, •most recent passed scan, •log information, and •contact information (for issuance of a possible alert), may also be considered as part of the processing of an incoming image. Embodiments may enhance security by avoiding introduction of vulnerabilities through image instantiation. Embodiments may also impart flexibility to conserve resources by selectively scanning according to a frequency and/or date of last successful passage of the image.

Abstract: A system includes storage of data into a target memory location allocated to a target leaf node of a tree-based index structure, the target leaf node being a child node of a parent node of the tree-based index structure, where the tree-based index structure comprises one or more other leaf nodes which are child nodes of the parent node, and each of the target leaf node and the one or more other leaf nodes is associated with a plurality of allocated memory locations, incremental identification of all unused allocated memory locations between a first allocated memory location of a left-most one of the target leaf node and the one or more other leaf nodes and a last used allocated memory location of a right-most one of the target leaf node and the one or more other leaf nodes, and movement of data stored in the target leaf node and the one or more other leaf nodes into the identified unused allocated memory locations.

Abstract: Methods, systems, and computer-readable storage media for receiving, by a conversation-based search system (CSS) of an analytics system, verbal input from a user, providing, by the CSS, text input based on the verbal input, processing, by the CSS, the text input to determine a set of contexts, each context in the set of context representing one or more operations of an enterprise, determining, by the CSS, one or more insights based on the set of contexts, each insight representative of a performance of the enterprise, and displaying, by the analytics system, a story comprising one or more visualizations, each visualization depicting at least one insight.

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: The present disclosure involves systems, software, and computer implemented methods for aligned purpose disassociation in a multi-system landscape. One example method includes receiving, from multiple systems, a can-disassociate status for a purpose for an object instance. The status from a respective system can be an affirmative status that indicates that the system can disassociate the purpose from the instance or a negative status that indicates that the system cannot disassociate the purpose from the instance. The received statuses are evaluated to determine a central disassociate purpose decision for the purpose for the instance. The central disassociate purpose decision can be to disassociate the purpose from the instance when no system has the negative status and to not disassociate the purpose from the instance when at least one system has the negative status. The central disassociate purpose decision is provided to at least some of the multiple systems.

Abstract: A method, a system, and a computer program product for generating and/or configuring a computing instance. A request to configure a computing instance for integration with at least one first computing system is received. At least one first computing component associated with the first computing system required for integration of the configured computing instance with the first computing system is identified. At least one second computing component for inclusion into the computing instance is identified. The second computing component is not required for integration of the configured computing instance with the first computing system. A configuration of the computing instance is generated using the identified first and second computing components. The configuration of the computing instance is applied in the first computing system. The configuration of the computing instance is executed by the first computing system and stored.

Abstract: A computer implemented method can receive a metadata definition of a restricted measure pertaining to a database including a plurality of database tables. The restricted measure has a label, and the metadata definition includes one or more filter criteria configured to filter values contained in the plurality of database tables. In a report designer user interface for a report, the method can present the label of the restricted measure as an option based on the metadata definition. The method can receive a selection of the label of the restricted measure in the report designer user interface. Responsive to the selection, the method can link the metadata definition of the restricted measure to the report. When generated, the report requests access to the values contained in the plurality of database tables via application of the one or more filter criteria of the metadata definition.

Abstract: A method may include receiving, at a recommendation engine, a first indication to create a first sourcing event that includes a first object and a second object. The recommendation engine may respond to the first indication by updating a learning model to increment a first frequency of the first object being included in a sourcing event, a second frequency of the second object being included in a sourcing event, and/or a third frequency of the first object and the second object being included simultaneously in a sourcing event. The recommendation engine may receive a second indication to create a second sourcing event. In response to the second indication, the recommendation engine may apply the learning model to generate a recommendation to add, to the second sourcing event, the first object instead of the second object. Related systems and articles of manufacture are also provided.

Abstract: Techniques and solutions are described for improving user interfaces, such as by analyzing user interactions with a user interface with a machine learning component. The machine learning component can be trained with user interaction data that includes an interaction identifier and a timestamp. The identifiers and timestamps can be used to determine the duration of an interaction with a user interface element, as well as patterns of interactions. Training data can be used to establish baseline or threshold values or ranges for particular user interface elements or types of user interface elements. Test data can be obtained that includes identifiers and timestamps. The time taken to complete an interaction with a user interface element, and optionally an interaction pattern, can be analyzed. If the machine learning component determines that an interaction time or pattern is abnormal, various actions can be taken, such as providing a report or user interface guidance.

Abstract: Embodiments relate to file-based sharing of content through a content distribution network. A request is received from a source to disseminate content to a target consumer. The request does not include target consumer URL information. Based upon the request, content metadata stored in a database with content data, is referenced to generate a file including metadata comprising a portion of the content metadata. The file does not include the content data. The file is exported to the source. The source delivers the file to the target consumer through a communication channel (e.g., shared folder, email) located outside of the distribution network. The file is imported from the target consumer. The metadata of the file is processed to examine its integrity. The metadata may comprise a key for reference during this process. Upon confirming integrity of the file, the content data and the content metadata are distributed to the target consumer.

Abstract: Software User Assistance (UA) is afforded from captured User Interface (UI) screen images, with reference to persisted Machine Learning (ML) models. The captured screen images are processed—e.g., using rasterization, Optical Character Recognition (OCR), and/or establishment of a coordinate system—with individual UI elements being determined therefrom. Referencing the persisted ML models, the software application/application state for the captured image is identified. UA data relevant to that application/application state is generated from the model, and then provided to the user (e.g., in a text box overlying the UI screen). Through the capture and processing of UI screen images, embodiments afford a homogenous UA experience for installation, maintenance, and/or upgrade of heterogeneous members of a larger overall landscape, over software lifecycles. Embodiments may be deployed locally on a frontend computer, in order to avoid exporting UI images due to privacy and/or security concerns.

Abstract: Methods, systems, and computer-readable storage media for receiving a capture file, the capture file holding data representative of a workload executed in a source database system, processing the capture file to provide a replay file, the replay file being in a format that is executable by a replayer to replay the workload in a target database system, the workload including a set of requests represented within the replay file, providing a set of tags associated with the replay file, the set of tags including one or more tags, each tag associated with a request in the set of requests, and during replay of the workload in the target database system: pausing replay of the workload in response to a tag, executing a request associated with the tag, providing replay results specific to the request, and selectively resuming replay of the workload in the target database system.

Abstract: An open source library rating is generated for an open source library based on dependencies of the library, vulnerabilities of the library, an age of the library, a popularity of the library, a history of the library, or any suitable combination thereof. The rating of a specific version of a library may be generated based on a base score for all versions of the library and a version score for the specific version of the library. An authorization system receives a request from a developer to add a library to a software application. In response, the authorization system accesses a rating for the library. Based on the rating, the authorization system approves the request, denies the request, or recommends an alternative library.

Abstract: Systems and methods include reception of a first request to check code associated with a first service for compliance with one or more criteria, determination of a plurality of code components associated with the first service, execution of a code check of each of the plurality of code components, generation of a first service compliance statement associated with the first service based on results of the executed code checks, determination of a definition of the first product from a product repository, the definition listing a plurality of services on which the product depends, the plurality of services including the first service, identification of a compliance statement associated with each of the plurality services, and determination of a product compliance statement based on each of the identified compliance statements.

Abstract: Techniques for testing a networked system using simulated abnormal node failure are disclosed. In some embodiments, a computer system performs operations comprising: repeatedly transmitting simulated requests to a networked system on which a software application is implemented using a plurality of nodes, the networked system being configured to respond to the simulated requests using the plurality of nodes; randomly selecting one or more nodes from the plurality of nodes; terminating the randomly selected one or more nodes; restarting the terminated randomly selected one or more nodes; repeating the randomly selecting one or more nodes, the terminating the randomly selected one or more nodes, and the restarting the terminated randomly selected one or more nodes until each one of the plurality of nodes has been terminated and restarted at least once during the first period of time; and determining response times of the networked system in responding to the simulated requests.