Abstract: According to one embodiment, a computer-implemented method for detecting and classifying columns of tables and/or tabular data arrangements within image data includes: detecting one or more tables and/or one or more tabular data arrangements within the image data; extracting the one or more tables and/or the one or more tabular data arrangements from the processed image data; and classifying either: a plurality of columns of the one or more extracted tables; a plurality of columns of the one or more extracted tabular data arrangements; or both the columns of the one or more extracted tables and the columns of the one or more extracted tabular data arrangements. Corresponding systems and computer program products are also disclosed.

Abstract: Arrangements for database schema creation and control are provided. In some aspects, a user may request creation or modification of a database schema. In response to receiving the request, one or more interactive user interfaces may be generated. The one or more interactive user interfaces may enable a user to select one or more selectable options for presenting data, establishing relationships between data, data sources, and the like. The schema, including a schema configuration including the presentational aspects and relationships, may be stored. A user may request to view data and the stored database schema may be retrieved. The schema may be transmitted to a configuration engine that may retrieve the requested data from one or more non-relational database sources. One or more user interfaces may be dynamically generated based on the schema configuration and may include the retrieved data.

Abstract: According to some embodiments, methods and systems may be associated with a cloud computing environment. A workload prediction framework may receive observed workload information associated with a database in the cloud computing environment (e.g., a DataBase as a Service (“DBaaS”)). Based on the observed workload information, a Statement Arrival Rate (“SAR”) prediction may be generated. In addition, a host variable assignment prediction may be generated based on the observed workload information. The workload prediction framework may then use the SAR prediction and the host variable assignment prediction to automatically create a workload prediction for the database. A physical database design advisor (e.g., a table partitioning advisor) may receive the workload prediction and, responsive to the workload prediction, automatically generate a recommended physical layout for the database (e.g., using a cost model, the current physical layout, and an objective function).

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for backing up a data object in blocks. One of the methods includes determining, for a data object of a backup process, whether a size of the data object or an estimated backup time of the data object satisfies a criterion that, when satisfied, indicates that at least two blocks of the data object should be separately fetched from the source system by different workers; determining one or more markers for end points of the at least two blocks using data from a prior backup of the data object; and causing, at least partially concurrently for two or more blocks from the at least two blocks, a respective backup worker to fetch the respective block from a source system using at least one marker from the one or more markers that defines an end of the respective block.

Abstract: An analytic model generated in a data scientist model acquisition toolbench is received. An analytic model is translated using a structuring tool. An execution simulation for testing the translated analytic model is performed for executing the model in the data scientist model acquisition toolbench.

Abstract: Provided herein are systems and methods for a stream object configuration, including query processing of stream objects using stream expansion. For example, a method includes decoding a query to obtain a first data processing operation and a first stream object. The first stream object is associated with a view on a base table. A first stream expansion on the first stream object is performed. The first stream expansion is based on generating a second stream object on the base table. A second stream expansion of the second stream object is performed. The second stream expansion is based on replacing the second stream object with at least a second data processing operation. The query is executed based on completing the first data processing operation and the at least a second data processing operation.

Abstract: A method performed by a data system includes automatically learning relationship(s) among datasets based on one or more of a user query or an observation of a data flow through the data system. The method further includes generating an optimized data structure based on the learned relationships among the datasets. The data system then modifies a query plan to obtain query results that satisfy a query by reading the optimized data structure in lieu of reading the datasets.

Abstract: A method includes receiving instructions to perform order-dependent DataFrame operation on data. In response to receiving the instructions, the framework analyzes the instructions to identify the order-dependent DataFrame operation, and generates an executable query corresponding to the identified order-dependent DataFrame operation. The framework executes the generated executable query on the data stored in the first database in the cloud data platform, creates a row position column that generates for each row a unique integer identifier, reproducible across multiple sessions and/or queries for the same underlying data to make the data accessible via positional indexing, and assigns each row of the first database a unique row index value based on the row position column and the row position column order.

Abstract: Embodiments in the present description provide an operation request processing method and apparatus, and a device, and a readable storage medium, and a system. In the present embodiment, after obtaining an operation request for a data table, a read-write node can return a request processing completion message to a requester in time without waiting for all read-only nodes; a locking operation is performed on the data table, a log of locking the data table is written into a redo log file; and after status information of the log of locking is written into a status record, a monitoring processing process can achieve log playback monitoring of all the read-only nodes by using the status record and the log of locking. Thus, an asynchronous processing mechanism is achieved, and the stability and efficiency of operation request execution are improved.

Abstract: An apparatus for generating a spatial recommendation is disclosed. The apparatus includes at least a processor and a memory connected to the processor. The processor is instructed to receive a user dataset comprising. The processor is instructed to receive a raw data from a plurality of sensors. The processor is instructed to identify a spatial dataset by organizing the raw data. The processor is instructed to store the spatial dataset in an index structure by implementing an indexing system as a function of the organized raw data, wherein the indexing system is further configured to dynamically adjust the index structure in response to additional raw data. The processor is instructed to determine a spatial recommendation by querying the index structure as a function of the user dataset. The processor is instructed to transmit the spatial recommendation to a remote device.

Abstract: Various aspects of the subject technology relate to systems, methods, and machine-readable media for determining a confidence factor for a sensitive type. The method includes applying a set of matching procedures to cells in a structured data set, the structured data set comprising columns and/or rows. The method also includes counting hit counts for the cells, the hit counts corresponding to successful matches. The method also includes counting null counts for the cells, the null counts corresponding to cells having null or invalid values. The method also includes counting mishit counts for the cells, the mishit counts corresponding to cells that are not null and do not result in a match. The method also includes calculating the confidence factor based on the hit counts, the null counts, and the mishit counts, the confidence factor providing an effective probability that cells in the structured data set is of the sensitive type.

Abstract: Recent developments in machine learning (commonly coined “artificial intelligence” or “AI”) have vastly expanded applications for this technology, such as myriad “chat” agents adept at understanding natural human language. While state of the art generative models can parse text queries from a user and provide comprehensive, accurate responses (including generating images depicting desired content), current implementations struggle with understanding all information present in images of documents, especially images of business documents. In particular, generative models fail to understand structured and semi-structured information, e.g., as indicated by graphical information such as lines, geometric relationships (e.g., indicated by tables, graphs, figures, etc.), formatting, and other contextual information that human readers easily and implicitly understand.

Abstract: In accordance with various embodiments, described herein is a system and method for providing a traceable push-based pipeline and monitoring of data quality in extract, transform, load or other enterprise computing environments. The system can include a combination of features including one or more of: an end-to-end push-based pipeline, which uses task-based events to trigger downstream jobs; the application of a table-of-tables or control table, by which the system can trace with detail the performance of each task and corresponding data table changes; a decoupling of pipeline components across several dimensions, for example: task, data, role, and/or time; a user interface or dashboard for monitoring pipeline health or data quality over the pipeline components and dimensions; and an orchestrator that can learn from pipeline health data and task/table changes, and identify actual or potential issues involving the pipeline, including associated root causes.

Abstract: Techniques for configuring event tables include retrieving, by at least one hardware processor of a computing node, log data at a first process of the computing node. The log data is associated with a function executing at a second process of the computing node. The log data is revised using a table stage to generate revised log data. The table stage is configured as a dedicated storage location of the first process. The revising includes a data enrichment process based on metadata associated with execution of the function at the second process. The revised log data is ingested into an event table.

Abstract: The present disclosure relates to recording and tracking a local content enrichment submission via a local content enrichment platform that leverages distributed ledger technology. The system can comprise a memory to store computer executable instructions. Also, the system can comprise one or more processors, operatively coupled to the memory, that execute the computer executable instructions to implement a blockchain layer configured to record a local content enrichment submission to a distributed ledger and track a status of the local content enrichment submission via transactions with the distributed ledger. The local content enrichment submission includes data characterizing a proposal of an entity to comply with local content regulations of a region.

Abstract: In some example embodiments, there is provided a method that includes receiving a query for query plan generation and query plan optimization; detecting whether the query plan includes a restart pipeline, the restart pipeline including one or more first operators that cause a query plan restart; detecting whether the query plan includes a result pipeline, the result pipeline including one or more second operators that provide result streaming; determining whether there is a dependency between execution of the restart pipeline and execution of the result pipeline; in response to determining there is no dependency, reordering the restart pipeline in the query plan such that the restart pipeline will be executed before the result pipeline in order to enable result streaming optimization; and providing the reordered query plan for execution. Related systems and computer program products are also provided.

Abstract: A method for recovering a data center, includes making a determination, by a cloud module, that the data center has experienced a failure; sending, by the cloud module and in response to the determination, an infrastructure file including data center infrastructure information to a client device; receiving, after sending the infrastructure file, a confirmation from the client device that at least a portion of the data center infrastructure is installed; sending, from the cloud module to the data center module, a snapshot in response to the confirmation; deploying, by the data center module, the snapshot at the data center; validating, by the data center module, complete connectivity of data center; sending, by the data center module, a heartbeat signal to the cloud module in response to the validating; and sending, by the cloud module, a standby signal to disaster recovery resources in response to receiving the heartbeat signal.

Abstract: A method for transforming data organized in tabular structure is disclosed. In some embodiments, the method includes assigning a score to each of a plurality of cells within a table based on an associated set of orthogonal features characterizing a set of data. The set of orthogonal features comprises visual features, syntactic features, and language-based features. The method further includes identifying for each of the plurality of cells, a cell type based on the assigned score. The method further includes determining a table type based on the cell type and the set of orthogonal features determined for each of the plurality of cells. The table type comprises one of a row-oriented table, a column-oriented table, or a composite table.

Abstract: One example method includes obtaining respective information concerning each asset in a group of assets, performing a deduplication check to identify an entity that will perform deduplication of backups of the assets, based on the information obtained concerning the group of assets, and based on an outcome of the deduplication check, sizing one or more proxy instances that will be needed to create the backups, spawning the proxy instances, and using the proxy instances to create the backups of the data assets.

Abstract: Information for selecting a food menu item based on an ingredient is provided to a user. In a user terminal device such as a smartphone, when a category is selected on an initial screen, the screen shifts to a large category screen. When “TORI-NIKU (CHICKEN)” is selected, the screen shifts to a sub-category screen. Alternatively, when “AIUEO (JAPANESE ALPHABET)” is selected on the initial screen and “TO” is selected on a character palette screen, a screen including ingredients having names each beginning with “TO” is displayed and, when “TORI-NIKU” is selected, the screen shifts to the sub-category screen. When “SASAMI (CHICKEN FILLET)” is selected on the sub-category screen, the screen shifts to a second ingredient selection screen. Under and on the right side of “SASAMI”, “SALAD HOURENSO (SPINACH)”, “ZA-SAI (PICKLED MUSTARD STEM)”, “NEGI (GREEN ONION)”, “PIMAN (GREEN PEPPER)”, and the like each as only one ingredient or a matching ingredient are displayed.