A processor may receive a request with a column-oriented operation. The query request may be related to updates to a row-oriented database based on the query request previously performed on the row-oriented database. The processor may determine the amount of time for replicating the updates to a column-oriented database. If the amount of time for replicating the updates to the column-oriented database is greater than a threshold amount of time, then the processor may create a transient file with the updates. The transient file may be in a format that is recognized by the column-oriented database. The processor may query the column-oriented database based on the query request and the transient file.

A method executes at a computing device that includes a display. The device displays a user interface that includes a data flow pane and a calculation pane, the data flow pane including a node/link diagram for a data prep flow. A user selects a node in the diagram, and the device populates affordances in the calculation pane according to data fields of a data set associated with the selected node. A first user input specifies grouping on a first data field, and a second user input specifies an aggregation function on a second data field. In response to the user inputs, for each distinct value of the first data field, the device aggregates corresponding values of the second data field according to the aggregation function. Calculated data values are displayed in the calculation pane. The device saves rows of data displayed in the calculation pane as a new data source.

Various methods, apparatuses/systems, and media for automatically mapping physical data models or objects to logical data models which in turn are automatically mapped to business terms are disclosed. A database stores a raw physical data model of an application. A processor extracts the raw physical data model of the application from the database. The processor also converts physical object names associated with the raw physical data model into English terms based on a taxonomy expansion list; applies a plurality of standardization and contextualization rules to the English terms generated from converting the physical object names; outputs names based on applying the plurality of standardization and contextualization rules to the English terms; applies fuzzy logic and machine learning routines and matching algorithms for matching the names to predefined logical terms; and automatically generates a mapping of physical objects or elements in the application with logical attributes and related business terms.

A method for transforming a statement into at least one hierarchical data space operation may include analyzing the statement to produce at least one transformed statement. The method may further include selecting at least one element of the transformed statement. The method may further include determining at least one column for the at least one element. The method may further include generating at least one hierarchical data space operation for the at least one element. The method may further include extracting at least one value from the at least one element for the at least one column. The method may further include determining at least one first logical hierarchical data space for the at least one hierarchical data space operation. The method may further include generating at least one first hierarchical path identifier for the at least one value that preserves the hierarchy of its first hierarchical data space, wherein each value corresponds to at least one subdivision of its first hierarchical data space. The method may further include associating the at least one first hierarchical path identifier with the at least one hierarchical data space operation.

An example method of displaying a data visualization includes displaying a plurality of selectable fields and receiving user selections of two different fields from the plurality of selectable fields. The method also includes generating, in accordance with the received user selections, data marks to be displayed in a data visualization, each data mark corresponding to a respective retrieved tuple of data from a multidimensional database, where (i) each data mark has an x-position defined according to data for a first field in the respective tuple and (ii) each data mark has a y-position defined according to data for a second field in the respective tuple. The method also includes displaying the data visualization that includes the generated data marks.

An in-memory database server hosting a tenant of a multi-tenant software architecture can receive a definition of a custom data field that is unique to an organization having isolated access to the tenant. The custom data field can extend a standard table defined by central metadata stored at a system tenant of the multi-tenant software architecture. Tenant private metadata that includes the definition can be stored in memory accessible only to the tenant. A tenant-dependent table that includes the custom data field can be formed, for example by retrieving central metadata defining the standard table from the system tenant and adding the custom data field using the definition. The tenant-dependent table can be presented for access via a database client at the organization. Related systems, articles of manufacture, and computer-implemented methods are disclosed.

Various embodiments for a data management system for managing inferences are described herein. An embodiment operates by detecting a signal to perform a replication task. Parameters corresponding to the replication task are identified. The replication task is configured to at least partially replicate a source data table from a source repository to a target data table in a target repository, based on the parameters. It is determined that a columnar structure of a first column of the source data table including data to be replicated matches a columnar structure of a second column of the target data table where the data is to be replicated to. The replication task is executed responsive to the determination of the matching structures, the replication task including performing one or more arithmetic operations on the data.

A data pre-processing architecture may include an interface and a pruning logic configured to receive, via the interface, at least one filter value from a query processor; use the at least one filter value to scan rows or columns of a data table stored in a memory; generate a selection indicator identifying a set of rows or columns of the data table where the at least one filter value resides; and provide to the query processor a filtered output based on the selection indicator.

An apparatus and method of performing a time-based operation on a row in a database table includes receiving a function to be applied to a particular row in a table within the database management system. The method may also include, in response to receiving the function, adding a new column to the table. A new cell in the new column includes a timer indicating a time that the processing device will call the function, and the new cell references the function. The method may also include, upon expiration of the timer, determining that the database management system is available for execution of the function, and executing the function on the particular row in the table.

Various aspects of the subject technology relate to methods, systems, and machine-readable media for generating histograms according to a set-theoretic approach. The method includes receiving a command to generate at least one histogram from selected data, the selected data comprising a plurality of column groups. The method also includes identifying a superset from the plurality of column groups. The method also includes materializing, in a memory, a superset multi-column group for the superset. The method also includes identifying at least one subset of the superset from the plurality of column groups, the at least one subset comprising at least one column group. The method also includes processing the at least one subset of the superset to form a grouped resultset. The method also includes generating the at least one histogram for the grouped resultset of the at least one subset of the superset.