Abstract: Embodiments facilitate testing of software functions, by elaborating upon a small volume test sample originally provided by a developer. Where the original test sample is determined to be numerical, embodiments perform repetition techniques to generate corpuses of test data of varying scope representing operation over different timescales. For original test samples of greater complexity (e.g., including text and/or hierarchical output states of the software function), embodiments select a relevant Machine Learning (ML) model and apply that ML model to generate the corpuses of test data. These are then available for loading to measure one or more performance parameters (e.g., runtime, memory consumption; processing consumption; bandwidth) of the software function. Broadly reported non-linear behavior of performance parameter(s) may reveal lack of suitability of the generated data corpuses for testing. More specific non-linear behavior may indicate failure of the software function to pass certain tests.

Abstract: Systems and methods include reception, at a first device, of a wireless communication packet from a second device, determination, at the first device, of a received service identifier included in the wireless communication packet, determination, at the first device, of whether the received service identifier matches a first service identifier or a second service identifier, if the received service identifier matches the first service identifier or the second service identifier, establishment, at the first device, of a connection to a server of the second device using a device address included in the wireless communication packet, requesting, using the connection and at the first device, of a user identifier from the server of the second device, if the received service identifier matches the first service identifier, transmission, from the first device to a remote server, of a first indication of proximity of a user of the first device to a user associated with the user identifier and a second indication of pr

Abstract: Technologies are described for managing shared read-only objects in hybrid distributed database systems. For example, data structures can be maintained for managing the shared read-only objects, comprising a data structure storing a “deleted at” value for each shared read-only object, a data structure storing a “deleted at” value for each secondary node, a data structure storing a indication of which secondary nodes are reading which shared read-only objects, and a data structure storing a reference count indicating a number of active read transactions for each shared read-only object. The shared read-only objects that can be garbage collected can be identified using, at least in part, the data structures. The identified shared read-only objects can then be garbage collected.

Abstract: Methods and apparatus are disclosed for efficient factor analysis of a large population of data records, using factors that are categorical variables. Computation is balanced between extracting key factors by training a machine learning classifier on a reduced sample of data records, for computational efficiency, and scoring the categorical values of the key factors on the entire population, for accuracy of results. A joint factor is constructed by combining all proposed root factors, and the sample is generated by stratified sampling on the joint factor. The key factors are selected from candidate factors which can be combinations of the root factors. Original variables of a dataset, whether categorical or not, can be binned to obtain new categorical factors. Variations and user interfaces are also disclosed.

Abstract: Systems and methods include determination of first constraints on columns of database tables of a test tenant system based on metadata associated with the columns of database tables, determination of second constraints on the columns of database tables based on data stored in the columns of database tables of the test tenant system, identification of inconsistent data stored in columns of database tables of a plurality of tenant systems based on the first constraints and the second constraints, adding of records to the database tables of the test tenant system based on the identified inconsistent data, and execution of a test based on the database tables of the test tenant system including the added records.

Abstract: Systems and methods provide reception of a call to a first function, determination of a first configuration file associated with the first function, the first configuration file indicating a primary function, a secondary function and a relationship between a first property of the primary function and a second property of the secondary function, calling of the primary function and reception of a corresponding first result set, calling of the secondary function, the call to the secondary function including a filter on the second property based on values of the first result set associated with the first property, reception of a second result set corresponding to the call to the secondary function, generation of a composed result set based on the first result set, the second result set, and the relationship, and return of the composed result set in response to the call to the first function.

Abstract: Some embodiments provide a non-transitory machine-readable medium that stores a program. The program receives a metadata model definition comprising a set of entity definitions specifying a set of entities, a set of semantic key definitions specifying a set of semantic keys associated with the set of entities, and a set of relationship definitions specifying a set of relationships between the set of entities. The set of semantic keys are configured to be used by an application to refer to the set of entities. The program further determines a set of technical keys for the set of entities. The set of technical keys are configured to be used by the device to refer to the set of entities. The program also stores the metadata model definition and the set of technical keys in a set of records.

Abstract: Disclosed herein are various embodiments a code simplification system. An embodiment operates by determining that a software version of a coordinator node is different from a software version of one or more worker nodes, Commits by the one or more worker nodes to a database are disabled based on the determination that the software versions differ. An update is performed on each of the one or more worker nodes. An acknowledgement that the update on each of the one or more worker nodes has completed is received, and the commits to the database by the one or more worker nodes is enabled.

Abstract: A computer implemented method can receive an event object published by a source entity, parse the event object to retrieve an event message pertaining to an event awaiting processing and one or more target entities authorized to process the event, identify one or more receiving entities having subscribed to the event object from the one or more target entities, create a message queue connected with one or more message routes that directly link the source entity to the respective one or more receiving entities, and post the event message to the message queue.

Abstract: A method may include detecting one or more trigger events at a first data center hosting a software application having a first customer onboarded at the first data center but not a second data center also hosting the software application. In response to the trigger events, a first data associated with the first customer may be replicated at the second data center to enable a collaboration between the first customer and a second customer of the software application onboarded at the second data center but not the first data center. One or more data replication relevant settings may be applied to prevent a second data associated with the first customer from being replicated at the second data center. Furthermore, the data replication relevant settings may be applied to prevent the first data from being replicated at a third data center. Related systems and computer program products are also provided.

Abstract: Techniques for implementing a dynamic intelligent log analysis tool are disclosed. In some embodiments, a computer system performs operations comprising: obtaining a log file comprising a plurality of log entries, each log entry comprising an error message; identifying a set of unique words from the error messages; for each error message, computing a term-frequency vector based on a frequency of occurrence for each unique word of the set of unique words in the error message; for each error message, computing a similarity measure between the term-frequency vectors of the error message and every other error message of the log entries; for each error message, computing a score based on a sum of the similarity measures; and displaying an indication of one or more of the error messages on a computing device based on the scores for the one or more of the error messages.

Abstract: A computer-implemented method includes receiving a request of data records for a data schema, obtaining a schema alignment which maps the data schema to a subset of an ontology of a knowledge graph, generating at least one query based on the subset of the ontology of the knowledge graph, obtaining a list of data records by executing the at least one query on the knowledge graph to obtain a list of data records, and persisting the list of data records to the data schema.

Abstract: A computer implemented method can receive a request for a fallback query execution plan of a query in a database system. The query involves an object of the database system. The object is associated with a captured object. The method can evaluate whether the object and the captured object have the same logical structure. Responsive to an indication that the object and the captured object have the same logical structure, the method can reuse the fallback query execution plan to execute the query. The evaluating includes identifying a logical structure of the captured object stored in an object definition table, obtaining a current logical structure of the object, and comparing the logical structure of the captured object with the current logical structure of the object.

Abstract: In an example embodiment, a machine learning algorithm is used to train a machine-learned model to create a three-dimensional representation of products, with each product mapped into a coordinate in the three-dimensional space. The model selects the coordinates based on the similarity of the product to other products. Coordinates that are closer geometrically in the three-dimensional space represent products that are similar to each other, whereas as the coordinates get further and further away, this implies that the products are less and less similar. This machine-learned model then not only allows for quick analysis of multiple products, as similarity between products or groups of products can be performed using geometric calculations (e.g., cosine distance), but also can then be tied into a three-dimensional representation that can be displayed either on a two-dimensional display or displayed on a three-dimensional display.

Abstract: Access to blockchain data may be removed by deleting an encryption key held in a remote server. Incoming data is stored in the blockchain after being encrypted at the key server. An ordinary blockchain user gains access to the data, after forwarding the encrypted data to the remote key server for decryption. Upon receipt of an input (e.g., time stamp), the key server deletes the key. Thereafter, the encrypted data on the blockchain is rendered inaccessible to the ordinary blockchain data user. At no point, does the ordinary data user have access to the key stored in the remote server. Embodiments may find particular use in removing access to personal data stored in a blockchain following the elapse of a predetermined amount of time, as may be required by privacy laws. Granular control over data access can may be afforded through the use of composite keys and/or key hierarchies.

Abstract: A computer-implemented method includes representing a plurality of database tables as respective vectors in a multi-dimensional vector space, receiving an indication that a first database table represented by a first vector and a second database table represented by a second vector are related to each other, moving positions of the respective vectors representing the plurality of database tables in the multi-dimensional vector space in response to the indication, and grouping the plurality of database tables into one or more table clusters based on positions of the respective vectors representing the plurality of database tables in the multi-dimensional vector space.

Abstract: Methods and apparatus are disclosed for staged query compilation. One or more intermediate plans between a query and its execution plan are stored according to a common specification for plan data structures. Data sources, a plan flow, and annotations can be stored in respective fields of the plan data structure, which can be extended with a field for a serialized execution plan. Plan data structures provide a stable interface for integration of local and external software modules, and decouple plan representation from plan optimization. Use cases are disclosed for integrating offline optimization with inline software modules, for integrating local query handling with external services, for caching intermediate plans, and for archiving plans across version changes of optimizers. A hub architecture flexibly supports numerous compilation workflows. Disclosed technologies are suitable for cloud deployments and for migration of databases into the cloud.

Abstract: Techniques and solutions are described for storing and processing metadata models using a metadata hub that can be accessed by a plurality of computing systems. The metadata hub can receive requests to retrieve or store metadata models. The metadata models can be converted between first and second formats. In some cases, the second format can be a relational format, such as a representation of a metadata model as one or more records in one or more database tables. The metadata hub can be used to compare metadata models, including comparing a metadata model provided by a client system with one or more metadata models stored in a repository maintained by the metadata hub. In at least some cases, the metadata model provided by the client system is not stored. The repository can be queried to find related metadata models, or to find metadata models meeting specified search criteria.

Abstract: In an example embodiment, when a user creates a consignment issue item for a given product in a sales order, the system will reserve/temporarily allocate the available quantity in consignment stock. A synchronous call to lock the inventor makes a tentative update to the inventory stock on the quantity required for the request product on the created issue item. This update is tentative because the locked quantity should be released if the user decides not to save the document, but it also should be locked so that it is not available to another user who is also trying to create an issue item in a different sales order document for the same product in a parallel session. The allocation may also change several times before saving the document by additional synchronous calls that again update the tentative allocations. When the user finally decides the final state of the issue item, it asynchronously sends either a confirmation or compensate message based on whether the document was saved or not saved.

Abstract: Software feature metadata objects and software artifact search, identification, and tracking is provided herein. A software artifact can have an associated software feature metadata object. A software feature metadata object can include metadata describing or defining at least in part the software artifact. The software feature metadata object can include one or more defined queries for identifying the software artifact in applications or programming code. The software feature metadata object can include one or more identifiers for composite software artifacts which integrate the software artifact corresponding to the software feature metadata object. The software feature metadata object can be accessed to obtain metadata about the corresponding software artifact for tracking usage, integration, or other activities, which can be provided for reporting or analysis.