Abstract: In an example embodiment, a solution is provided that allows a user to submit a document. Information can be obtained from the document using optical character recognition (OCR) or other techniques. This information can then be used to identify one or more workflows that pertain to the document. The one or more workflows may be ranked using machine learning techniques and presented to the user. Once the user selects a desired workflow, the information obtained from the document can then be used to automatically complete at least a portion of the workflow, for example by prefilling one or more fields in a form.

Abstract: Methods, systems, and computer-readable storage media for receiving, by an application studio, user input indicating two or more artifacts of an enterprise application developed using the application studio, generating, by an enterprise application artifact composer of the application studio, an enterprise application artifact as a universal tag that provides an abstract representation of the enterprise application, and during deployment of the enterprise application: retrieving the enterprise application artifact, determining a target runtime that the enterprise application is to be deployed to, determining a set of dependencies for each artifact of the enterprise application, and for any dependency in the set of dependencies that is absent from the target runtime, retrieving binary code of the dependency from a library to deploy the dependency to the target runtime.

Abstract: In an example embodiment, a combination of machine learning and rule-based techniques are used to automatically detect social engineering attacks in a computer system. More particularly, three phases of detection are utilized on communications in a thread or stream of communications: attack contextualization, intention classification, and security policy violation detection. Each phase of detection causes a score to be generated that is reflective of the degree of danger in the thread or stream of communications, and these scores may then be combined into a single global social engineering attack score, which then may be used to determined appropriate actions to deal with the attack if it transgresses a threshold.

Abstract: Some embodiments provide a non-transitory machine-readable medium that stores a program. The program receives a request to execute a task for re-encrypting a set of data associated with an application that has been encrypted with a first encryption key. The task is for re-encrypting the set of data using a second encryption key. The program further determines an amount of work to complete the task. The program also divides the task into a set of subtasks based on the amount of work. The program further assigns each subtask in the set of subtasks to a node in a plurality of nodes for execution of the subtask. The plurality of nodes are configured to implement the application.

Abstract: Mechanisms are disclosed for estimating cardinality of group-by queries. A probability of occurrence of values is obtained for columns that satisfy the query occurring in tables from a trained machine learning model. A range selectivity is calculated based on a conditional probability of occurrence of the values. A set of valid generated sample tuples is generated from the trained machine learning model. A group-by selectivity is calculated by keeping the conditional probability of occurrence to obtain probabilities that a result set will have specific group-by column values associated with the tables while proceeding with progressive sampling. A sampling probability is calculated by normalizing the group-by selectivity by dividing the group-by selectivity by the range selectivity. The samples are filtered such that the samples having a sampling probability below a sampling probability threshold are filtered out. A sampling-based estimator is applied to the filtered samples set to estimate the cardinality.

Abstract: Various embodiments for an error message replacement system are described herein. An embodiment operates by receiving, at a computing device, a request from a browser to access a website external to a domain of the computing device. The request is provided to a server associated with the website and a response is received. Within the response, an error code indicating that the request was not properly executed by the server is identified. The response includes an identification of a first error page. A second error page corresponding to the error code is identified. The second error page is provide for display, at the browser, in lieu of the first error page responsive to identifying the error code.

Abstract: In an example embodiment, a technique is disclosed to describe generic capabilities in the ORD standard. In addition, this new ORD resource type may be explicitly supported by a Central ORD aggregator (or Central Management Plane), which can be used to discover and retrieve the information centrally. Having a central metadata service also eliminates the need for many point-to-point integrations.

Abstract: Gradient Boosting Decision Tree (GBDT) successively stacks many decision trees which at each step try to fix the residual errors from the previous steps. The final score produced by the GBDT is the sum of the individual scores obtained by the decision trees for an input vector. Overfitting in GBDT can be reduced by removing the input values that have the least impact on the output from the training data. One way to determine which input variable has the lowest predictive value is to determine the input variable that is used for the first time in the latest decision tree in the GBDT. This method of identifying the low-predictive features to be removed does not require that earlier trees be regenerated to generate the new GBDT. Since the removed feature was already not used in the earlier trees, those trees already ignore the removed feature.

Abstract: Disclosed herein are system, method, and computer program product embodiments for managing data storage devices. In some embodiments, a server receives a request to store data in a first storage device. The server determines the storage space remaining in the first data storage device based on historical data associated with the first data storage device. The server further determines that the first data storage device will exceed its storage capacity based on a size of the data and the storage space remaining in the first data storage device. The server transfers a set of data stored in the first data storage device to a second storage device. Transferring the set of data causes the storage space remaining in the first storage device to be greater than or equal to the size of the data. The server stores the data in the first data storage device.

Abstract: Techniques and solutions are described for compressing data and facilitating access to compressed data. Compression can be applied to proper data subsets of a data set, such as to columns of a table. Using various methods, the proper data subsets can be evaluated to be included in a group of proper data subsets to be compressed using a first compression technique, where unselected proper data subsets are not compressed using the first compression technique. Data in the data set can be reordered based on a reordering sequence for the proper data subsets. Reordering data in the data set can improve compression when at least a portion of the proper data subsets are compressed. A data structure is provided that facilitates accessing specified data stored in a compressed format.

Abstract: The present disclosure relates to computer-implemented methods, software, and systems for exchanging metadata between applications. One example method includes providing a metadata service defining a service metadata model for exposing metadata of data objects defined at one or more applications. A connection to a data source associated with a first application is established from a separate application to acquire the metadata from the data source for one or more data objects related to the first application. In response to establishing the connection, a view of content from the data source is provided that includes a list of entities from the service metadata model. A query defined based on the list of entities is received for acquiring metadata associated with the one or more identified entities. The query is evaluated at a backend of the first application and the metadata for the identified entities from the data source is provided.

Abstract: The present disclosure relates to computer-implemented methods, software, and systems for obtaining incoming alerts associated with tickets generated for health status issues identified by applications for central components of a cloud platform environment. The obtained incoming alerts are aggregated to determine a root cause ticket. A set of incoming alerts is associated with a first central component running at a first data center from one or more data centers of the cloud platform environment. The set of incoming alerts are generated based on a set of tickets logged at a first ticketing system of the first data center by one or more applications running on the first data center. A root cause ticket is generated to combine the set of tickets corresponding to the set of incoming alerts, and the root cause ticket identifies the first central component as associated with a first health status issue.

Abstract: A work center in a manufacturing setup includes a machine learning model that uses a decision tree to facilitate the work of a supervisor on the production line to choose a machine to perform a particular operation on a particular part. The decision tree outputs a ranking of machines indicating the suitability of the ranked machines for performing the particular operation on the particular part.

Abstract: The present disclosure involves systems, software, and computer implemented methods for efficient memory leak detection in database systems. One example method includes receiving a query at a database system. Memory allocations and deallocations are traced during processing of the query. Each memory allocation entry in a tracing file can be processed, including determining, for each allocation, whether a memory deallocation entry exists in the tracing file. A determination can be made as to whether a memory leak has occurred in response to determining whether a memory deallocation entry corresponding to a memory allocation entry exists in the tracing file. For example, a determination can be made that a memory leak has occurred in response to determining that no memory deallocation entry corresponding to an allocated memory address exists in the tracing file. One or more actions can be performed in response to determining that a memory leak has occurred.

Abstract: The present disclosure relates to computer-implemented methods, software, and systems for data processing to monitor and trigger alerts based on identified technical issues at instances running in a cloud platform environment. Availability data for the cloud platform can be collected. Based on evaluation of the availability data, a determination that at least a portion of a set of services are experiencing issues can be made. A detailed check is triggered to collect data logs, metrics, and dependencies data for at least the portion of the set of services. An overall status of performance of the cloud platform based on obtained service status data from the detailed check can be determined. Based on analysis of the overall status of performance of the cloud platform, execution of causal engine logic to identify an issue at the cloud platform can be performed, and a notification for the issue can be provided.

Abstract: Some embodiments provide a program that determines a plurality of data objects. Each data object in the plurality of data objects includes a first attribute and a second attribute. The program further sorts values of the first attribute of the plurality of data objects. The program also sorts values of the second attribute of the plurality of data objects. The program further determines a first distance value based on the sorted values of the first attribute of the plurality of data objects. The program also determines a second distance value based on the sorted values of the second attribute of the plurality of data objects. The program further defines a plurality of clusters based on the sorted values of the first attribute of the plurality of data objects, the first distance value, the sorted values of the second attribute of the plurality of data objects, and the second distance value.

Abstract: Systems and methods are provided for dynamic data masking for graphical and textual content in robotic process automation (RPA). Textual and/or graphical content to be obfuscated in an RPA project is indicated. Textual and graphical elements of RPA artifacts and other RPA content of the RPA project corresponding to the indicated textual and/or graphical content are identified and obfuscated. The RPA project is then updated with the obfuscated content and stored. The provided systems and methods for dynamic data masking of graphical and textual content are highly customizable and reduce the possibility of errors. Further, once data obfuscation is established, sensitive data in projects and artifacts are automatically masked as the projects and artifacts are updated or edited.

Abstract: Techniques and solutions are provided for improved data modelling, including techniques that facilitate data model comparison, integration, or deployment. A core data model is created that has a plurality of elements, which can represent semantic concepts, including relationships between semantic concepts. A plurality of domain models are mapped to core data model elements. In particular, when a domain model element is to be created, the element is mapped to a core data model element. When a relation is to be established between two domain model elements, the relation is mapped to a relation type represented in the core data model. Mapping to a core data model can be enforced as part of domain model creation, allowing different domain models to be compared, since domain elements in different domain models that represent a common semantic will be mapped to the same core data model element.

Abstract: In an example embodiment, a solution is provided for a container-orchestration service that allows a custom resource to reflect an entire software application while still splitting the actual work out into independent microservices. Specifically, the concepts of an extendable controller and controller extensions are introduced. An extendable controller defines an extendable custom resource. This custom resource is still the main resource describing the entire application, but does so in a way that extensions are referenced that can be defined in their own resources (called extension resources). The extendable controller itself is surrounded by extension controllers, which are responsible for certain aspects of the system that need to be considered in atomic transactions, such as high-availability configuration or scale-out.

Abstract: The present disclosure involves systems, software, and computer implemented methods for transaction auditing. One example method includes training at least one machine learning model to determine features that can be used to determine whether an image is an authentic image of a document or an automatically generated document image, using a training set of authentic images and a training set of automatically generated document images. A request to classify an image as either an authentic image of a document or an automatically generated document image is received. The machine learning model(s) are used to classify the image as either an authentic image of a document or an automatically generated document image, based on features included in the image that are identified by the machine learning model(s). A classification of the image is provided. The machine learning model(s) are updated based on the image and the classification of the image.