Abstract: Disclosed herein are system, method, and computer program product embodiments for management and monitoring of a blockchain network. An embodiment operates by configuring a node within a blockchain network to determine metadata about the blockchain network, wherein the blockchain network maintains a blockchain. The metadata is generated at the node, and provided to a solution manager. Using such a solution manager, it is possible to monitor, alert, and even react to changing conditions within the blockchain network.

Abstract: A system and method to receive, from a database service executing on a cloud infrastructure, information indicating metrics regarding backups for the database service, the information including at least an indication of an age of a last complete backup for the database service, an indication of a size of changed data since the last complete backup, and an indication of a number of data units changed since the last complete backup; determine a type of backup strategy to instruct the database service to perform based on the received information, the type of backup strategy being one of a complete backup of the database service, a delta backup of the database service, and no backup of the database service; and issue, in response to the determination, an instruction to the database service to execute the determined type of backup.

Abstract: A global filter allows data filtering using attributes across multiple Analysis Tools (ATs), by broadcasting semantic filter context objects. Upon selecting object attribute values, the filter context object is created with attribute names and values. A processing engine resolves the filter context object to a data object, and then subsequently to target data. A lateral filter finds related entities in a relational database, without having to maintain and/or duplicate all of the data into a graph database. The processing engine resolves lateral filters using an entity graph path calculation conducted in conjunction with the generation of a bootstrapped graph structure. That graph structure is constructed (bootstrapped) utilizing available database schematic information—e.g., pre-calculated (key) relations and metadata read from the relational database. From that information, relationships in the bootstrapped graph structure are determined.

Abstract: Methods, systems, and computer-readable storage media for receiving, by an IEE and from a target service, user action metadata and target information, the user action metadata representing an action of a user to the target service, determining that an integration recommendation is to be issued to the source service at least partially in response to the user action metadata, the integration recommendation recommending a service integration of the target service with the source service; and automatically executing, by the IEE, integration of the target service with the source service by calling an extension framework to generate an extension, the extension being installed in the source service, and calling a configuration framework to configure the source service having the extension installed therein, wherein, in response to the service integration, a user interface of the source service is populated with one or more fields of a user interface of the target service.

Abstract: Systems and methods include a set of delta copies received from cluster node replicas of a replica set and stored on a main data storage on the cloud. A cloud storage service internally replicates the data from the delta copies and provides fault-tolerance and high availability against storage failures. All cluster node replicas participate in a delta copies merge. Each replica writes their deltas to an independent location in a shared storage on the cloud. Then, the delta merge includes deltas from all replicas when building a new main storage. This ensures that the data from all replicas are included in the delta merge.

Abstract: Methods, systems, and computer-readable storage media for receiving a first storage request indicating a first type of data and a first size, identifying a first sub-space based on the first type, the first sub-space including a dense group of dense segments and a sparse group of sparse segments, and determining that the dense group of the first sub-space is absent a dense segment to store data of the first storage request based on the first size, and in response: determining whether the sparse group of the first sub-space includes a sparse segment to store the data of the first storage request based on the first size, and storing the data of the first storage request to the sparse group of the first sub-space in response to determining that the sparse group of the first sub-space includes a sparse segment to store the data of the first storage request.

Abstract: In an intelligent integration error handling in enterprise systems, an integration error is logged by a sender system or a receiver system in an error monitoring application. The integration error occurred in a transaction between the integrated sender system and the receiver system. Parsing the log in real-time by a worker cloak agent, a mode of integration error correction is determined based on inputs from an intelligent correction rule service. Upon determining that the mode of integration error correction is autonomous, the integration error is automatically fixed in real-time without manual intervention by the worker cloak agent. Upon determining that the mode of integration error correction is semi-autonomous, inputs from a business user is received along with a consent to fix the integration error in real-time. Correction rules are dynamically updated in a dynamic decision table. While performing correction the sender system and receiver systems are notified.

Abstract: The present disclosure involves systems, software, and computer implemented methods for access control for object instances. A method includes receiving, at a cloud application, a user request associated with a user. The user request corresponds to an instance of a first application artifact type. Role assignments for the user are retrieved from a cloud platform and a determination is made that the role assignments grant permission to the first application artifact type to the user. A determination is made that a first instance-based access policy exists for the first application artifact type. A determination is made regarding whether the first instance-based access policy grants permission for the user to access the instance. The user request is serviced in response to determining that the first instance-based access policy grants permission for the user to access the instance.

Abstract: Technologies are provided for generating executable asset bundles using a plug-in module loaded in an integrated development environment (IDE). The IDE can be used to create and edit source code assets and three-dimensional (3D) model assets that can be compiled into an executable program. The plug-in module can be used to generate an executable asset bundle based on a subset of the source code assets. Optionally, the executable asset bundle can include a subset of the 3D model assets. The IDE can be used to generate an executable program based on the remaining source code assets and 3D model assets. The executable program and the executable asset bundle can be distributed separately. The executable program can be executed by a client computing device and used to load the executable asset bundle on the client device. Loading the executable asset bundle can comprise downloading it from a remote server.

Abstract: Methods, systems, and computer-readable storage media for receiving, by an automated review system, a legal document as a computer-readable file, and determining, by the automated review system, that the legal document is of a first type, and in response: converting the legal document to a set of images, extracting text data from one or more images in the set of images, the text data including sub-sets of text data, each sub-set of text data representing text in a respective clause of a set of clauses of the legal document, for each sub-set of text data receiving a prediction from a machine learning (ML) model in a set of ML models, the ML model being specific to a clause in the set of clauses, and outputting a set of predictions and respective prediction values for display in a user interface (UI).

Abstract: Implementations include receiving, by an application programming interface (API) server of a plurality of API servers, a prediction request from a client system, each of the plurality of API servers including a stateless server, selecting, by the API server, a model server from a plurality of model servers based on the prediction request, each of the plurality of model servers including a stateful server, calling, by the API server, the model server to execute inference using a ML model loaded to memory of the model server, receiving, by the API server, an inference result from the ML model, and sending, by the API server, the inference result to the client system.

Abstract: Provided herein are a system, apparatus, device, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for identifying attributes in unstructured data files using a machine-learning model. In an embodiment, a server can receive a request to identify an attribute associated with a set of unstructured data files. The server can extract a first and second subset of features from each unstructured data file of the set of unstructured data files. The server can identify the attribute in the set of unstructured data files request based on each of the first and second subset of features using the machine-learning model.

Abstract: A system includes a relational database management system component and a column-oriented data processing component. The relational database system component stores database information in a row format. The column-oriented data processing component stores the database information in a column format. In response to a database update request, the relational database management system component updates the database information stored in the row format; the relational database management system component notifies the column-oriented data processing component of the database update request; and the column-oriented data processing component updates the database information stored in said column format. In response to a query request, the column-oriented data processing component generates a query response based on the database information stored in said column format. In this manner, the system is able to generate up-to-date reports without the need for extraction, translation and loading procedures.

Abstract: A system includes reception of a set of time-series data from each of a plurality of sensors, each of the plurality of sensors associated with one of a plurality of hardware devices, determination of a plurality of clusters based on the sets of time-series data, assignment of each set of time-series data to one of the plurality of clusters, and determination of associations between the plurality of hardware devices based on the assignments of time-series data to clusters.

Abstract: Technologies are described for correcting master data in an unsupervised manner using supervised machine learning. Correction of master data can involve receiving a table containing unlabeled master data. Machine learning models are applied to the fields of one or more columns of the table to predict values of the fields, and the machine learning models use unsupervised learning. For example, a machine learning model can be applied to a particular field of a particular column to predict the value of the particular field. The machine learning model uses the fields of other columns as features. Results of applying the machine learning models include indications of recommended values, indications of probabilities of the recommended values, and indications of which original values do not match their respective recommended values. The results can be used to perform manual and/or automatic correction of the master data.

Abstract: Techniques and solutions are described for determining a set of elements of a second set that may correspond to a given element of a first set of elements. The elements can be, in specific examples, components of a database system, such as tables (or entities), attributes, or records. Significance metric values are calculated for elements in the first and second sets. The significance metric values can be a number of records in an entity or a number of read or write access operations for an entity or for a record of an entity. A significance metric value for the given element can be used at least in part to select elements of the second set as potential match candidates, based at least in part on significance metric values for elements of the second set. Selecting elements can include selecting elements based on a window of elements of the second set or a range of significance metric values.

Abstract: A system includes determination of a plurality of queries of a workload, determination of a data source comprising a plurality of data rows, and determination of a sample data source based on a cardinality of each of the plurality of queries with respect to the data source and an estimated cardinality of each of the plurality of queries with respect to the data source, wherein the estimated cardinality of a query with respect to the data source is determined based on the sample data source.

Abstract: A predictive cache system learns data access patterns to build a graph of related data items. A main cache hosted by a cache server is accessed by a cache client having a local cache. A request to the cache server for one of the related data items generates a response including some or all of the related data items, which are stored in the local cache and can be served locally for subsequent requests. Predictive cache behavior coexists with legacy non-predictive cache operation. Load on the cache server and aggregate latency are considerably reduced. Graphs can be specific to a particular context. Locally cached content can be distributed and reused by other cache clients. The predictive cache system is suitable for large-scale web applications. Variations are described.

Abstract: Methods, systems, and computer-readable storage media for determining that a first transaction results in a first version of data stored in a page in a database system, the first version being stored in a version space and the data being stored in a table space, in response to the first version, providing a version table for the page, the version table including a set of cells each cell corresponding to a record in the page, setting a version table pointer of the page to point to the version table, and setting a version pointer of a first cell to point to the first version in the version space, the first cell corresponding to a record of the data in the page.

Abstract: Techniques for implementing unsupervised universal anomaly detection for situation handling are disclosed. In some example embodiments, a computer-implemented method comprises detecting an anomaly in a new data point that has corresponding manifestation values for variable categories based on a restriction index for the corresponding manifestation value for at least one of the variable categories in the new data point, and causing a notification of the anomaly in the new data point to be displayed on a computing device based on the detecting of the anomaly. The restriction index for the corresponding manifestation value for the at least one of the variable categories in the new data point may be calculated for the corresponding manifestation value for each other variable category in the plurality of variable categories based on a manifestation space value and a prediction space value that are based on historical data points.