Abstract: A horizontal toolbar presents a hierarchical path terminating at a currently-selected element, beginning at a root element, and including intermediate elements from a hierarchically-related subset of elements having parent-child relationships. A tree representation, provided simultaneously with the horizontal toolbar, includes nodes matching the elements included in the hierarchical path. The tree representation uses branches connecting adjacent parent-child nodes to indicate parent-child relationships and highlights a current node-of-interest in relation to other displayed nodes and matching the currently-selected element in the horizontal toolbar.

Abstract: Techniques and solutions are described for determining relationships in data with improved efficiency, including computing resource use. A plurality of attributes are selected for analysis. The attributes can be processed, such as to facilitate relationship determination. Relationships between attribute values are determined. Redundant relationships can be removed. Distances are determined between relationships and used to select a sample of relationships. The sample is labelled by a user and used to train a machine learning classifier. The machine learning classifier labels determined relationships.

Abstract: Technologies are described for facilitating data workload capture, workload replay, and comparing the performance of a plurality of database systems. A plurality of requests for database operations are received from one or more database clients. The requests for database operations are executed. For the plurality of requests for database operations, execution context information is generated for a plurality of execution contexts. For the plurality of requests for database operations, a plurality of performance measures are generated. The execution context information and the performance measures are stored in a workload capture store, such as a file. The workload capture file is useable to replay the captured workload at a second database system using at least a portion of the execution context information and at least a portion of the performance measures.

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: Methods and apparatus are disclosed for serializing software objects for loading onto a cloud-hosted tenant, or for other serialized operations. Nodes and edges of a directed graph represent respective software objects and the dependencies among the software objects. A search for strongly connected components reveals cyclic dependencies. Each strongly connected component can be replaced by a contracted node to obtain a condensation of the original directed graph. A topological ordering can be determined for the condensation, and the software objects can be processed or loaded according to the topological ordering. Such ordering provides that software objects of a strongly connected component are processed consecutively while, for all other dependencies, a prerequisite object is processed (loaded) before its dependent object(s). Examples of SAP Analytics Cloud and Data Warehouse Cloud are provided. Exemplary, push and pull loading procedures, topological ordering, and validation are disclosed.

Abstract: Provided is a system and method for verifying a blockchain using an external audit chain. The system may include an engine which facilitates communication between two or more independent blockchains. In one example, the method may include reading block data of a source data block that is stored among a hash-linked chain of data blocks on a blockchain, verifying a hash of the source block based on the block data that is read, generating a token based on the hash verification of the source block and storing the generated token in a verification block among the hash-linked chain of blocks on the blockchain to create a verification point on the blockchain, and storing a copy of the generated token in a block among a hash-linked chain of blocks on an audit blockchain that is independent from the blockchain.

Abstract: Described herein is computing device for machine learning based risk analysis and decision automation in intelligent underwriting. In accordance with one aspect of the framework, input data of underwriting cases is preprocessed and used to train a predictive model. The predictive model may be cross-validated and tested for accuracy. The predictive model may then be applied to a current underwriting case in order to automatically identify and assess associated risks and provide a decision recommendation.

Abstract: Disclosed herein are system, method, and computer program product embodiments for transitioning virtual systems from one run-level to a next run-level. In an embodiment, a controller may receive a run-level request to transition a computing cluster (e.g., vSystem) from a first run-level to a second run-level. The controller persists a current run-level of the computing cluster as the second run-level in a configuration file. The controller executes a set of actions corresponding to the second run-level to transition the computing cluster from the first run-level to the second-level. A gateway of the computing cluster receives a request to access the computing cluster. The request includes a user's authentication details. The gateway grants or restricts access to the computing cluster based on the type of user attempting to access the computing cluster and the current run-level of the computing cluster as indicated in the configuration file.

Abstract: Methods, systems, and computer-readable storage media for providing a simulated graph processing accelerator representative of a hardware-based graph processing accelerator, the simulated graph processing accelerator including a controller component, a set of producer components, and a final merge component; triggering execution of the simulated graph processing accelerator as a simulation of processing of a graph for one or more of breadth-first search (BFS), single source shortest path (SSSP), weakly connected components (WCC), sparse matrix-vector multiplication (SpMV), and PageRank (PR), execution including: generating request streams from each producer component, merging request streams to provide a merged request stream, inputting the merged request stream to a memory simulator, and processing, by the memory simulator, the merged request stream to simulate handling of requests in memory.

Abstract: The present disclosure involves systems, software, and computer implemented methods for a verifiable communication-efficient secret shuffle protocol for encrypted data based on homomorphic encryption. A service provider and multiple clients participate in a secret shuffle protocol of randomly shuffling encrypted client-specific secret input values. The protocol includes generation and exchange of random numbers, random permutations, different blinding values, and use of random secret-shares. A protocol step includes homomorphic operations to shuffle encrypted secret input values so that resulting encrypted secret input values are rerandomized and in a shuffled sequence that is unmapped to an order of receipt by the service provider of the encrypted secret input values.

Abstract: Provided is a system and method for generating a subset of optimal variations of a software program which allow some statements of the control flow to be exposed to side channels. Furthermore, the subset of optimal variations may be selected based on a security and a performance trade-off analysis. In one example, the method may include identifying a set of statements within a control flow of a software program, generating a plurality of variations of the software program which comprise different subsets of statements which are exposed to side channels, respectively, determining one or more pareto-optimal variations of the software program based on side channel leakage values and performance values of the plurality of variations of the software program, and outputting information about the one or more pareto-optimal variations of the software program to a user device.

Abstract: A database system receives a mass insert operation command specifying a plurality of entries. Thereafter, the entries are iteratively inserted into a plurality of preliminary pages associated with a container provided by a persistence layer of the database system. Thereafter, the preliminary pages are registered to the container once they are full.

Abstract: Methods, systems, and computer-readable storage media for retrieving metadata associated with data stored within a database system of an enterprise, the metadata being provided in a first format and being used by the first system to store and access the data, providing a document including the metadata provided in an interoperable format, processing, by a deployer, the document to provide analytics engine metadata in a second format, the analytics metadata being stored in the second system and being consumable by the DB-based analytics engine to access the data from the database system of the enterprise, and retrieving, by the DB-based analytics engine, the data from the database system of the enterprise based on the analytics metadata to provide analytics data based on the data.

Abstract: Transaction data is received from a remote client computing device that includes user-generated entries in each of a plurality of fields. Thereafter, it can be determined, using an outlier detection algorithm, that values for one or more of the entries is an outlier. Data can then be provided (e.g., displayed in a visual display, loaded into memory, stored in physical persistence, transmitted to a remote computing system, etc.). The outlier algorithm can be based on a number of similar records g, a number of distinct values d(g) in the similar records, and a number of same values s in the similar records. Related apparatus, systems, techniques and articles are also described.

Abstract: Techniques are provided for identifying and encrypting fields of an application object at an application layer in a multi-tenant cloud architecture, using an object metadata structure of the application object. Accordingly, transparent, per-tenant encryption capabilities are provided, while enabling transfer of encrypted object data between the application layer and a storage layer.

Abstract: Technologies are provided for automated exploratory testing using machine learning. In response to receiving an identifier for an application to be tested, a machine learning model can be generated that can be used to automate exploratory testing of the application. The machine learning model can be generated based on test scenarios associated with the application and user interface controls of the application. The machine learning model can comprise one or more data structures that model relationships between user interface control values and application functionality defined by the test scenarios. The machine learning model can be used to generate exploratory testing operations targeting the application. In at least some embodiments, the machine learning model comprises an artificial neural network comprising input layer nodes associated with user interface controls and/or hidden layer nodes associated with application test scenarios.

Abstract: An integrated code inspection framework using check variants is provided herein. Integrated code inspection can include associating a check variant with a development object in a development project. Additionally, a check variant can be associated with a development project. A check variant associated with a development project can cascade to development objects in the development project. A check variant associated with a development object can cascade to related development objects, based on their hierarchical relationship. A check variant can be executed against a development object to identify code which triggers check rules of the check variant. The check variants executed can come from the development project or related development objects based on the cascade of check variants. Results of the code inspection via the check variants can be aggregated and displayed in a user interface, and integrated with the code of a development object.

Abstract: A request is received to create a graph database from one or more relational databases. For each relational database, data objects in the relational database are identified. For each data object, a graph data object corresponding to the data object is created. The graph data object is linked to the data object. A set of associated data objects in the relational database are determined, and for each associated data object, an associated graph data object is created if a graph data object corresponding to the data object does not exist. For each created graph data object, a graph data relation object is created that represents a relationship between the graph data object and the associated graph data object. Created graph data objects, associated graph data objects, and graph data relation objects are stored in the graph database. The graph database is provided to one or more applications.

Abstract: Technologies are provided for iterative machine learning and relearning. A training dataset can be divided into a plurality of training data blocks which can be used to generate separate machine learning models. The accuracy of the machine learning models can be assessed using a test dataset. Training data blocks which result in models with good accuracy can be combined into larger training data blocks which can then be used to generate new machine learning models. The process of combining training data blocks can be repeated as long as the resulting machine learning model has acceptable accuracy. However, if a model for a combined training data block has a poorer accuracy than the machine learning models for its component training data blocks, then the combined training data block and its machine learning model can be forgotten and its component training data blocks (and their associated machine learning models) can be relearned.

Abstract: System, method, and various embodiments for providing a metadata based memory management system are described herein. An embodiment operates by determining metadata of a table of a database indicating whether data of the table is to be stored in memory or disk storage, wherein the table comprises a plurality of columns and a plurality of rows. A first column of the table is identified. A hierarchy of the metadata for the table including a column value, partition value, and table value is determined, wherein the column value is prioritized over the partition value, and wherein the partition value is prioritized over the table value. The data of the table stored in the column is loaded from the disk storage into the memory based on the hierarchy. A query is processed based on the data loaded into the memory.