Input means 731 receives an input of a coupling table, a candidate column, and a base table. Coupling means 732 couples a column corresponding to the candidate column in the coupling table to the base table. Feature selection means 733 selects a feature that improves performance of a task based on data included in the coupled candidate column. Integrated table output means 734 outputs an integrated table obtained by coupling a column including the selected feature and the base table.

A system and method of query processing in a multi-level storage system having a unified table architecture. A query is received by a common query execution engine connected with the unified table architecture, the query specifying a data record. The common query execution engine performs a look-up for the data record based on the query at the first level storage structure. If the data record is not present at the first level storage structure, the common query execution engine performs separate look-ups in each of the second level storage structure and the main store.

A method and system for accessing updated data from a database in response to a user query has been developed. First, multiple transaction logs are generated for a database. Each transaction log contains a record of actions executed by a database management system and referenced according to the specified date of the actions. Data updates are received and stored with the database. An incremental database partition is created for each data update. Each incremental database partition is stored with reference to a corresponding transaction log for the date of the data update. The updated data is accessed through the incremental database partition in response to an outdated user query. The outdated user query contains a data access request for a date earlier than the receipt of data updates.

There is a need for more effective and efficient detection of cross-data-column relationships. This need can be addressed by, for example, techniques for detecting cross-data-column data relationships that utilize at least one of feature-based similarity models and deep-learning-based similarity models. The cross-data-column data relationships may be displayed to an end-user using a cross-column relationship detection user interface.

Methods and systems for analyzing data are described. In one embodiment, a method comprises a processor receiving a data analysis algorithm over a network and executing the data analysis algorithm, the data analysis algorithm analyzing data stored in a database using machine learning to identify a database organizational format, the data analysis algorithm identifying one or more locations for a set of data stored on the database based on identifying the database organizational format, the data analysis algorithm parsing the set of data to identify whether any entries in the database associated with the set of data includes a particular value, and the data analysis algorithm communicating over the network at least a first number of entries in the database that include the particular value and a second number of entries in the database that do not include the particular value.

Embodiments of the present disclosure provide a system for accelerated data search of a database storage system. The system includes a host device including a database storage engine; and a memory system including a controller and a memory device, which includes a plurality of pages storing multiple records. The controller includes a page processing accelerator configured to: read, from the plurality of pages, multiple pages in response to a filtered read command; filter particular pages among the multiple pages based on a column full search condition, the filtered pages including entries satisfying the column full search condition; and transfer, to the host device, information regarding the filtered pages.

Methods and apparatus for executing a data operation are described herein. The methods and systems may include determining at least one subdivision of at least one logical hierarchical data space. The at least one logical hierarchical data space may have a plurality of subdivisions. The method may further include determining at least one file corresponding to the at least one subdivision of the at least one logical hierarchical data space. The method may further include reading at least one tuple from the at least one file.

Herein are spatially scalable techniques for anisotropic compression of shared entropy between alternate representations of same data. In an embodiment, a computer compresses an uncompressed independent column into a compressed independent column. Based on the compressed independent column, an uncompressed dependent column is compressed into a compressed dependent column. The compressed independent column and the compressed dependent column are stored in a same file. In an embodiment, a computer stores, in metadata, an encrypted private key for decrypting an encrypted column. The encrypted column and the metadata are stored in a file. A request to read the encrypted column is received. Based on a public key and the file, the encrypted private key is decrypted into a decrypted private key. The public key is contained in the request and/or the file. The request is executed by decrypting, based on the decrypted private key and the file, the encrypted column.

Disclosed herein are example embodiments of an improved narrative generation system where an analysis service that executes data analysis logic that supports story generation is segregated from an authoring service that executes authoring logic for story generation through an interface. Accordingly, when the authoring service needs analysis from the analysis service, it can invoke the analysis service through the interface. By exposing the analysis service to the authoring service through the shared interface, the details of the logic underlying the analysis service are shielded from the authoring service (and vice versa where the details of the authoring service are shielded from the analysis service). Through parameterization of operating variables, the analysis service can thus be designed as a generalized data analysis service that can operate in a number of different content verticals with respect to a variety of different story types.

The present disclosure related to techniques for analyzing data from multiple different data sources to determine a relationship between the data (also referred to herein a “data relationship discovery”). The relationships between any two compared datasets may be used to determine one or more recommendations for merging (e.g., joining), or “blending,” the data sets together. Relationship discovery may include determining a relationship between a subset of data, such as a relationship between a pair of columns, or column pair, each column in a different dataset of the datasets that are compared. Given two datasets to process for relationship discovery, relationship discovery may identify and recommends a ranked subset of column pairs between two compared datasets. The ranked column pairs identified as a relationship may be useful for blending the datasets with respect to those column pairs.