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: The present disclosure relates to computer-implemented methods, software, and systems for lifecycle processing of declarative artifacts. Declarative artifacts defining a target state for application content related to a software application are read. When running, the software application includes runtime artifacts executing in a containerized environment. Model definition objects for processing during runtime of the software application based on the declarative artifacts are created and stored in a model repository at a container associated with the software application. The model repository is scanned as well as the runtime artifacts executing as part of the software application in the containerized runtime environment to identify a model definition object from the model repository for processing at runtime of the software application.

Abstract: Techniques and solutions are described for converting data models between formats, such as between a conceptual data model and a physical data model for a database system, or between a conceptual data model and artefacts to be implemented in the database system. The conceptual data model is annotated with annotations that allow the physical data model or database artefacts to be automatically generated from the conceptual data model. The annotations can reflect relationships between entity types in the physical data model, such as inheritance relationships, header/item relationships, or one-to-one cardinality relationships. Annotations can also indicate attributes that should be added to entity types in the conceptual data model, such as attributes for versioning or data governance, that may not be used in the conceptual data model. Annotations can be used to determine how entity types in the conceptual data model will be denormalized in the physical data model.

Abstract: Provided is a system and method which perform an antivirus scan of incoming files via a file management application of a file system. Infected files can be prevented from being stored to the file system. In one example, the method may include receiving, via a first application contained in a first data container, a data file that is uploaded for storage to a file system, storing the data file in a temporary storage, transmitting a location of the data file in the temporary storage to a second application contained in a second data container, and receiving, via the first application contained in the first data container, a response from the second application contained in the second container, indicating results of a security scan performed on the data file.

Abstract: Embodiments facilitate selection and assignment of a known user model, based upon input comprising table images of original data. A table engine receives the image and performs pre-processing (e.g., rasterization, Optical Character Recognition, coordinate representation) thereupon to identify image entities. After filtering original numerical data, a similarity (e.g., a distance) is calculated between an image entity and a dimension member of the known user model. Based upon this similarity, the table engine selects and assigns the known user model to the incoming tables images, generating a file representing table columns and rows. This file is received at the UI of an analytics platform, which in turn populates the model with data of the user (rather than the original data) via an API. Embodiments may be particularly valuable in allowing a user to rapidly generate multi-dimensional tables comprising their own data, based upon raw table images received from an external party.

Abstract: The present disclosure relates to computer-implemented methods, software, and systems for generating a hierarchy of metadata tables for a database comprising containers including tables. The tables are identified by table names and assigned to containers. A first table is assigned to two containers and may define two table instances of the first table. The hierarchy of metadata tables includes a first metadata table defining mappings between container identifiers, table names, table sections, and unique identifiers for corresponding data within table sections of table instances defined with the table names mapped to the container identifiers. In response to receiving a request to generate a replication of table content, a second metadata table is generated to identify a unique set of table instances from the set of the containers based on evaluating the first metadata table. The unique set of table instances comprises data from the database storage without repetition.

Abstract: Provided is a system and method for automated recommendation of new features for addition to an item based on user-generated feedback. In one example, the method may include receiving, via a user interface, a search query associated with an object, retrieving user-generated content that describes the object based on the received search query, identifying, via a machine learning model, one or more features to be added to the object based on the retrieved user-generated content, and outputting identifiers of the one or more features to be added to the object via the user interface.

Abstract: Computer-readable media, methods, and systems are disclosed for producing updated software documentation for a software product. A plurality of versioned source code change indications are received corresponding to changes in a plurality of source code files, which are associated with a target version of the software product. A plurality of documentation sources and associated documentation metadata is received corresponding to the plurality of versioned source code change indications. The plurality of documentation sources is merged based on the plurality of versioned source code change indications and the target version of the software product. Based on determining a set of impacted software documentation outputs, a current version of documentation build tools is fetched based on the merged plurality of merged documentation sources. Software documentation output components are built with the current version of documentation build tools.

Abstract: Disclosed herein are system, method, and computer program product embodiments for generating a single transaction data stream from multiple database logs. An embodiment operates by assigning a first unique identifier to a first log entry associated with a disk store, a second unique identifier to a second log entry associated with in an in memory row store, and a third unique identifier to a third log entry based on the second unique identifier. The embodiment further operates by determining a first stream package based on the first log entry, a second stream package based on the third log entry, and a replay order for the first stream package and the second stream package based on the unique identifiers. In some embodiments, the operation further includes sending the first stream package and the second stream package to a replicant server in accordance with the replay order.

Abstract: In an example embodiment, machine learning is utilized to automatically identify pain points of an entity. More particularly, a random forest search algorithm receives inputs from a number of different outside-in data engines. These inputs are segregated in terms of key performance indexes (KPIs), which is then fed to a random forest decision tree. The random forest search algorithm calculates a p-value for each KPI. This p-value may then be used to identify the most relevant KPIs (such as by ranking the KPIs by their respective p-values). Rules are utilized not to identify the pain points themselves, but instead to define sorting preferences (refine the ranking). Based on a combination of the p-values and the evaluation of the rules, the top-N pain points may be identified.

Abstract: Natural language processing systems and methods are disclosed herein. In some embodiments, digital document information comprising text is received. The digital document information may be processed through word and character encoding operations to generate word and character vectors while retaining document location information for the words and characters. The data may be then be processed by a series of convolution and maximum pooling operations to obtain maximum valued elements from the data. The document location information as well as the maximum values element data may be further processed for semantic classification of the data using a semantic classifier and bounding box regression.

Abstract: Various examples are directed to systems and methods for generating a contract form using a contract library data structure. A computing device may receive, from a user, a contract form request comprising a set of contract classifiers. The computing device may access a contract library data structure and executes a filter routine to traverse the contract library data structure to identify at least one contract term associated with one or more filter tags corresponding to the set of contract classifiers. The filter routine may return an indication of the first contract term associated with the first node. The computing device may generate a contract form comprising the first contract term.

Abstract: Systems and methods for event-triggered analysis for high fidelity simulation. The method may comprise collecting event data produced by one or more event monitors in operational relationship with one or more elements of an event-producing system, wherein at least one of the one or more elements generates a plurality of events. The event data may be clustered into one or more groups, according to one or more event attributes. The event data may be selectively transmittable to one or more subscribers, by way of the one or more subscribers subscribing to one or more channels associated with the one or more groups. A publication mechanism may be implemented to classify the clustered event data into the one or more channels, the event data associated with at least one attribute being classified for transmission over at least one of the one or more channels.

Abstract: Devices, methods and systems for processing database queries formatted differently than the database storage model being queried are disclosed. Processing database queries independent of the storage model of the queried database may be performed by receiving a query for one or more data items stored in a database, determining whether to use at least one query operator that uses data having a format different from the storage model format of at least one of one or more data items stored in the database and converting the format of the data used by the at least one query operator to a format that matches the storage model format of at least one of one or more data items stored in the database. Related systems, methods, and articles of manufacture are also described.

Abstract: Various examples are directed to systems and methods for administering data model metadata for a plurality of data models. A metadata service may receive a first retrieval request from a requesting system. The first retrieval request may comprise an indication of a first local object identifier referencing a definition of a first local object from a first data model and an indication of a target data model. The metadata service may retrieve a first record from a metadata identifier registry, where the first record comprises an indication of the first local object identifier and an indication of a first global object identifier corresponding to the first local object identifier. The metadata service may determine a second local object identifier referencing a definition of a second local object identifier referencing a definition of a second local object in the target data model and return the second local object identifier.

Abstract: The present disclosure relates to computer-implemented methods, software, and systems for generating access tokens at an authentication server based on authorization codes. A first authorization server from a set of authorization servers receives a request for authorization of a request to access a resource by a resource owner. The first authorization server validates the request for authorization of the request to generate an authorization code. In response to successful validation of the request for authorization to generate the authorizaction code, the first authorization server generates a single-use authorization code by signing the generated authorization code with a unique private key. A unique public key is maintained for verifying the signed authorization code. The single-use authorization code is generated in a self-contained format.

Abstract: In some example embodiments, a computer-implemented method may include training a machine learning model in a first database instance using a machine learning algorithm and a training dataset in response to receiving a request to train, serializing the trained machine learning model into a binary file in response to the training of the machine learning model, recreating the trained machine learning model in a second database instance using the binary file in response to receiving a request to apply the machine learning model, and generating an inference result by applying the recreated trained machine learning model on the inference dataset in the second database instance.

Abstract: A system including at least one data processor and at least one memory is provided. The at least one memory may store instructions that cause operations when executed by the at least one data processor. The operations may include determining a processing or memory storage threshold of a database system has been satisfied, the database system including a cloud infrastructure, a plurality of virtual machines, and a plurality of containers deployed on the plurality of virtual machines, the database system having a Non-Uniform Memory Access (NUMA) topology. The operations may include requesting provisioning of a virtual machine to the database system. The operations may include obtaining information regarding the NUMA topology and a performance of the database system. The operations may include determining a virtual machine for placement of the container based on the information. The operations may include deploying the container on the determined virtual machine.

Abstract: A system and method are disclosed associated with a multi-tenant cloud computing environment. The system may receive information about a serverless function workload (e.g., a NodeJS, Java function or ABAP workload) to be launched in the cloud computing environment. A tenant associated with the serverless function workload to be launched may be identified and, based at least in part on the identified tenant, an objective function (e.g., throughput, latency, cost, etc.) for the serverless function workload to be launched may be identified. A recommendation service platform may then iteratively configure tuning parameters of the cloud computing environment using Bayesian optimization (e.g., to reach a global optimum using a Gaussian process) and the determined objective function. The system may then arrange for the serverless function workload to be executed in the cloud computing environment in accordance with the configured tuning parameters.

Abstract: Techniques and solutions are described for copying data from a source client to a target client. It may be selected, such as by a user, whether a full copy or a partial copy of a set of source client data should be made. If a partial copy is selected, a set of file types is provided. The file types can be used to define files that should be included in, or excluded from, the copy. A data source of the source client is analyzed to determine data associated with a file type that is to be included in a copy, or is associated with a file type that is not indicated as to be excluded from the copy. The determined data is copied from the source client to the target client.