Abstract: A system and method for faster access for compressed time series data. A set of blocks are generated based on a table stored in a database of the data platform. The table stores data associated with multiple sources of data provided as consecutive values, each block containing index vectors having a range of the consecutive values. A block index is generated for each block having a field start vector representing a starting position of the block relative to the range of consecutive values, and a starting value vector representing a value of the block at the starting position. The field start vector of the block index is accessed to obtain the starting position of a field corresponding to a first block and to the range of the consecutive values of the first block. The starting value vector is then determined from the block index to determine an end and a length of the field of the first block.

Abstract: Text-to-speech translation is used to generate a transcript for an audio file. Text segments are associated with time segments in the transcript. A trained machine learning model determines, based on the text in the transcript, one or more topics for the audio file. The transcript is modified to include the determined one or more topics. A user interface may be presented that allows a user to search for portions of an audio file that relate to a particular topic. In response to the selected or entered topic, the user interface presents segments having a matching topic. The user may use voice or other user interface commands to modify the annotation of the audio file. User commands may also be used to extract data from the transcript and copy the data to a clipboard or to another application.

Abstract: Disclosed herein are system, method, and computer program product embodiments for utilizing non-RAM memory to implement machine learning configured with a meta-learning training set (small dataset), to create a common-sense predictive language model, thus boosting the performance for downstream tasks. An embodiment operates by receiving a base sentence and perturbation sentences as an input and tokenizing the input to generate a sequence of tokens. Tokens of the semantic perturbation sentences are embedded with tokens of the base sentence as contextually similar tokens pairs to generate training data and classified to capture relationships of the base sentence and the perturbation sentences to generate a classification, which is used to train a language model.

Abstract: According to a disclosed embodiment, data analysis is secured with a microservice architecture and data anonymization in a multitenant application. Tenant data is received by a first microservice in a multitenant application. The tenant data is isolated from other tenant data in the first microservice and stored separately from other tenant data in a tenant database. The tenant data is anonymized in the first microservice and thereafter provided to a second microservice. The second microservice stores the anonymized tenant data in an analytics database. The second microservice, upon request, analyzes anonymized tenant data from a plurality of tenants from the analytics database and provides an analytics result to the first microservice.

Abstract: Technologies described herein can be used for the bulk lazy loading of structured data from a database. A request can be received to initialize an application data structure (such as a structured data object, a hierarchical data structure, an object graph, etc.). The data structure can be analyzed to identify a plurality of child objects of the data structure. Database records associated with the plurality of child objects can then be identified. A loaded child record table can be inspected to determine which of the identified database records are not stored in a cache. A request can be generated, comprising one or more queries to retrieve the uncached subset of database records from the database. Once the uncached subset of records are received from the database, these records can be used, along with the cached subset of the identified database records, to initialize the plurality of child objects of the application data structure.

Abstract: The present disclosure relates to computer-implemented methods, software, and systems for identifying potential attacks through monitoring of user credential login attempts across a network of websites. One example method includes monitoring login attempts associated with a plurality of websites and identifying a first login attempt at a first website associated with a set of user credentials. In response to determining that the set of user credentials do not correspond to a valid set of credentials, a count value associated with an entry in a failed credential log associated with the user credentials is incremented. If the count threshold associated with a compromised user credential rule is exceeded by the current count value, then the first set of credentials is identified as a set of compromised credentials and at least one protective action is initiated.

Abstract: In an example embodiment, a request is received, via a graphical user interface, to add a new object to a directory of objects, the new object having a first category in a hierarchical taxonomy of categories and objects. Then one or more questions previously assigned to the first category and/or one or more existing objects within the first category are retrieved. Each of the retrieved one or more questions and information about the new object are then fed into a first machine learned model trained to output a probability that a question is applicable to an object. One or more questions are generated for the new object based on the probability for each of the retrieved one or more questions. At least one of the one or more generated questions is then assigned to the new object.

Abstract: The present disclosure relates to computer-implemented methods, software, and systems for detecting outages in a multiple availability zone cloud environment. Structured data defining network connectivity statuses of network segments is received. Multiple availability zones of the first cloud platform are defined in a multiple availability zone cloud architecture. External structure data defining inbound connectivity statuses of the network segments correspondingly defined for the availability zones of the first cloud platform is iteratively collecting. The inbound connectivity statuses define availability for an entity running at an external cloud platform to the first cloud platform to connect to at least one entity running at the first cloud platform.

Abstract: A method may include receiving a first transaction inserting a record into a database and a second transaction deleting the record from the database. A validity period for the record may be determined based on a first commit time at which the first transaction is committed and a second commit time at which the second transaction is committed. A current table and/or a history table of a system versioned table may be updated to include the record based on the validity period of the record. One or more temporal operations may be performed based on the system versioned table. For example, a time travel operation may be performed to retrieve, based on the system versioned table, one or more records that are valid at a given point in time. Related systems and computer program products are also provided.

Abstract: According to some embodiments, a system, method and non-transitory, computer readable medium are provided comprising a memory storing processor-executable program code; and a processing unit to cause the system to: receive a range of persistent volume disk size values from a cloud service provider; receive a value for an upper bound of a number of persistent volume disks attachable to a virtual machine per the cloud service; determine a number of persistent volume disks based on the upper bound, wherein the number is at least two; generate a plurality of configurations of the determined number of persistent volume disks, wherein a configuration is provided for each persistent volume disk size value, and each configuration divides the persistent volume disk size value between two or more persistent volume disks; and aggregate the generated plurality of configurations in a decision table. Numerous other aspects are provided.

Abstract: An enterprise database server may store enterprise information. An application layer server, coupled to the enterprise database server, may use an application programming language (e.g., ABAP) to access the enterprise information. The application layer server may execute a plurality of domain runtimes, each associated with a domain service (e.g., transactional, SQL, analytical, search, etc.). The application layer server may also access, by a first domain runtime, a first data service entity in an infrastructure for data modelling (e.g., CDS) that enables data models to be defined and consumed on the enterprise database server. Similarly, the application layer server may access, by a second domain runtime, a second data service entity in the infrastructure for data modelling. According to some embodiments, the first data service entity includes a first provider contract and the second data service entity includes a second provider contract (different than the first provider contract).

Abstract: Data for processing by a machine learning model may be prepared by encoding a first portion of the data including a spatial data. The spatial data may include a spatial coordinate including one or more values identifying a geographical location. The encoding of the first portion of the data may include mapping, to a cell in a grid system, the spatial coordinate such that the spatial coordinate is represented by an identifier of the cell instead of the one or more values. The data may be further prepared by embedding a second portion of the data including textual data, preparing a third portion of the data including hierarchical data, and/or preparing a fourth portion of the data including numerical data. The machine learning model may be applied to the prepared data in order to train, validate, test, and/or deploy the machine learning model to perform a cognitive task.

Abstract: Methods, systems, and computer-readable storage media for receiving initial input from a user of the application, the initial input indicating that a UI element is to be generated for the user, receiving context data representative of a context of the application when the input is received, the context data at least partially including a data schema definition of data accessed by the application, prompting the user for input at least partially based on the context data, receiving additional input responsive to the prompting, defining dynamic metadata based on at least a portion of the initial input, at least a portion of the additional input, and at least a portion of the data schema definition, the dynamic metadata including one or more actions that can be executed on data accessed through the UI element, and processing the dynamic metadata to generate the UI element for display in the application.

Abstract: Systems and methods provide identification of a first configuration specifying a first column of a first data source, acquisition, based on the first configuration, of a first sequence of values stored in consecutive rows of the first column, and storage of the first sequence of values in a storage device in association with an identifier of the first data source and the first column.

Abstract: Techniques and solutions are provided for improving query execution. Data models can be complex, which is often reflected in queries against such data models. The present disclosure provides a query refactoring technique where a complex query, such as a query expressed as a single select statement, can be formulated as a series of less complex queries. The workload of a database can be reduced by combining results of the less complex queries outside of the database. The present disclosure provides a framework for implementing these techniques, where the framework includes a virtual cube, a calculation engine, and one or more operations, which can all be implemented as classes in a programming language, and where a generic class or interface can help guide users in developing subclasses that provide a reformulation or refactoring of a complex query.

Abstract: Automated management of tasks in a preventive maintenance context supports associating preventive maintenance targets with a preventive maintenance task. A trained machine learning model can predict which targets are most likely to be appropriate for a given header preventive maintenance target. A user interface can assist in target selection. Data integrity can be improved, and unnecessary expenditure of preventive maintenance resources can be avoided. A trained machine learning model can support features such as filtering and identifying outliers.

Abstract: Techniques and solutions are provided for updating or augmenting consolidated data that is produced using base data. The consolidated data can include data that is aggregated by various grouping criteria. After a set of consolidated data is determined, the base data may change, one or more rules used to calculate the consolidated data may change, or it may be desired to see data that is more granular than that included in the consolidated data. After consolidated data is provided to a user, a user issues a data augmentation request. The data augmentation request causes the base data, which may have been updated, to be processed to provide updated data, where the processing includes grouping operations used in producing the consolidated data. The updated data is provided to a client in response to the data augmentation request.

Abstract: Disclosed herein are system, method, and computer program product embodiments for extending HTTP clients to capture network traces in applications. The application may initiate a network request, and if the network request fail or generates an error message, the application may export a listening record for analysis. The analysis may understand the failed network request and improve network communication of the application and the web server.

Abstract: Techniques and solutions are provided for determining changes to networks formed from network objects. Changes to network objects, such as addition or deletion operations are received and associated with timestamps. Last data changes for respective network objects are determined, including a last data change for a particular type associated with a respective networked object, where a networked object may be concurrently associated with a plurality of types. A first network object is removed from a first network, and contributions of the first network object to edges in the first network are removed. A second network object is associated with an addition operation and at least one edge is determined that connects the second network object and a third network object. Sets of disconnected network objects are determined. Network objects are associated with an identifier identifying a network that includes a respective network object.