A method for functionally rewriting iterative queries for a relational database management system (RDBMS) is provided. The method comprises receiving a first iterative query, the first iterative query having a first non-iterative part that defines a first main table and a first iterative part that generates values in rows of a first working table based on values in rows of the first main table, determining that the first iterative part modifies all of the rows of the first working table, and rewriting the first iterative part, including: adding a renaming operation to rename the first working table to a new first main table and to rename the first main table to a new first working table, adding a first Delete operation to delete each row of the new first working table, and adding a first loop operation to repeat the first iterative part until a first termination condition is met.

In an embodiment, a data platform receives a query that includes a two-sided N dimensional interval join of first and second input relations, where N>1. The two-sided N dimensional interval join has an interval-join predicate that, in each of N dimensions, compares intervals determined from the first and second input relations. The data platform implements the interval join at least in part by identifying an intermediate relation that includes all combinations of a row from the first input relation and a row from the second input relation where, in each of the N dimensions, the intervals determined from the first and second input relations both overlap a common N dimensional domain region of an input domain of the first and second input relations. The data platform obtains and returns results of the query.

A digital product network system includes a set of digital products each having a product processor, a product memory, and a product network interface. The digital product network system includes a product network control tower having a control tower processor, a control tower memory, and a control tower network. The product processor and the control tower processor collectively include non-transitory instructions that program the digital product network system to generate product level data at the product processor, transmit the product level data from the product network interface, receive the product level data at the control tower network interface, encode the product level data as a product level data structure configured to convey parameters indicated by the product level data across the set of digital products, and write the product level data structure to at least one of the product memory and the control memory.

A streaming ingest platform can improve latency and expense issues related to uploading data into a cloud data system. The streaming ingest platform can organize the data to be ingested into per-table chunks and per-account blobs. This data may be committed and may be made available for query processing before it is ingested into the target source tables. This significantly improves latency issues. The streaming ingest platform can also accommodate uploading data from various sources with different processing and communication capabilities, such as Internet of Things (IOT) devices.

A search request relating to one or more datasets in the data repository can be received, the search request comprising a display request to display at least a portion of the one or more datasets. In response to the search request, a searchable database can be generated from the one or more datasets in a data repository based on ontological data associated with the one or more datasets. An object view of at least the portion of one or more datasets can be generated from the searchable database, the view being generated based on the ontological data. The generated object view can be provided to be displayed on a display device.

A determination is made whether a received database query is to be processed by either a first database, a second database, or at least in part by both the first and second databases including by determining whether the query meets criteria to split the query for processing across the first and second databases. The first and second databases store shared synchronized records, the first database configured to store the records in a column-oriented format and the second database configured to store the records in a row-oriented format. In response to a determination that the query meets the criteria to split the query, a first and second component query of the database query are generated for the first and second databases, respectively, the second component query based at least in part on a result of the first component query. The execution of the first and second component queries is pipelined.

A method, apparatus, system, and computer program code for retrieving data records. A set of static configuration objects is provided, including: a set of resources that describe available data items, and a set of views that express a serialized transformation of resources objects into a response. In response to receiving a data request, a computer system generates a data fetch execution plan from the set of resources and the set of views. The data fetch execution plan is generated using an executor adapted to a particular data store and set of performance requirements. The computer system retrieves the data records according to the data fetch execution plan.

A method for performing multi-caching on data sources of a same type and different types by using a cluster-based processing system is provided. The method includes steps of: a big data cluster management device (a) determining whether a result set, corresponding to a query result, is present as first cache data in master or worker nodes, (b) if specific part of the result set is absent, (i) establishing an execution plan (ii) acquiring a first subset in the master or the worker nodes, (iii) acquiring a second subset in none of the master and the worker nodes, and (iv) applying joint operation thereto, and (c) applying data processing operation and output operation thereto thus acquiring the result set as the query result.

A database system may include a machine learning model which may be used to perform various data analytics for data stored in the database system. In response to a request to invoke the machine learning model to generate a prediction from data stored in the database system, the database system may perform one or more optimization operations, as part of a query plan, to prepare the data to make it suitable for use by the machine learning model.

A query generation and processing system includes a relational data store, a query generator, and a query processor. The relational data store stores data ingested from data sources in a first and second datasets. The query generator interprets a data expression in a simplified query language to generate a query in a structured query language based on identifying quads corresponding to the first and second datasets in the data expression and determining an implicit join between the quads based on an unambiguous relationship obtainable from a schema of the first and datasets, in which the data expression does not expressly identify a join between the first quad and the second quad. The query processor generates a query pipeline that uses the data of the first and second datasets stored by the relational data store to execute the query generated by the query processor.