Generally discussed herein are devices, systems, and methods for machine learning (ML) by flowing a dataset towards a target dataset. A method can include receiving a request to operate on a first dataset including first feature, label pairs, identifying a second dataset from multiple datasets, the second dataset including second feature, label pairs, determining a distance between the first feature, label and the second feature, label pairs, and flowing the first dataset using a dataset objective that operates based on the determined distance to generate an optimized dataset.

A central database system allows users to access and use data stored in a relational database. In order to ensure that the stored data is not detrimentally impacted and that the security of the stored data is maintained, the central database system generates a sanitized copy of the database. The central database system stores and accesses annotation files associated with data tables of the database and a schema identifying the structure of the database. Based on the schema, for each data table, the central database system validates the annotation file. A copy of the data table is created and sanitized corresponding to the annotation file. The sanitized copies of the data tables are used to generate a sanitized copy of the database. The sanitized copy of the database may then be accessed and used by users of the central database system without impacting the relational database.

In accordance with embodiments, there are provided mechanisms and methods for creating, exporting, viewing and testing, and importing custom applications in a multitenant database environment. These mechanisms and methods can enable embodiments to provide a vehicle for sharing applications across organizational boundaries. The ability to share applications across organizational boundaries can enable tenants in a multi-tenant database system, for example, to easily and efficiently import and export, and thus share, applications with other tenants in the multi-tenant environment.

Systems and methods for searching data are disclosed. For example, the system may include one or more memory units storing instructions and one or more processors configured to execute the instructions to perform operations. The operations may include receiving a sample dataset and identifying a data schema of the sample dataset. The operations may include generating a sample data vector that includes statistical metrics of the sample dataset and information based on the data schema of the sample dataset. The operations may include searching a data index comprising a plurality of stored data vectors corresponding to a plurality of reference datasets. The stored data vectors may include statistical metrics of the reference datasets and information based on corresponding data schema. The operations may include generating, based on the search and the sample data vector, one or more similarity metrics of the sample dataset to individual ones of the reference datasets.

Dynamically normalizing intervals in a table including receiving, from a client computing system, a request to normalize intervals for a data set on a cloud-based data warehouse, wherein the request comprises a reference to the data set and a data range; generating, on the cloud-based data warehouse, an interval table using the data range; joining, into a joined table on the cloud-based data warehouse, the interval table and the data set; receiving the joined table from the cloud-based data warehouse; and presenting, via a graphical user interface on the client computing system, the joined table as a worksheet.

Techniques for customizing knowledge representation systems including identifying, based on a plurality of concepts in a knowledge representation (KR), a group of one or more concepts relevant to user context information, and providing the identified group of one more concepts to a user. The KR may include a combination of modules. The modules may include a kernel and a customized module customized for the user. The kernel may accessible via a second KR.

Systems and methods of maintaining a policy implementation for an object across different storage systems are disclosed. The method includes determining, for an object to be copied from a first storage system, one or more object policies that are applicable to the object; generating metadata that triggers application of the one or more policies at an other storage system that is different from the first storage system; and including the metadata with the object during copying of the object.

Technology for logging application usage based on monitoring of read or change operations on database elements and then using the information from log data to determine application usage states (for example, active, inactive) in time.

According to one embodiment, a method that supports queries deploying operators based on multiple programming languages is described. A sequence of operators associated with a query is identified, where the sequence of operators includes at least two neighboring operators including a first operator based on a first programming language and a second operator based on a second programming language that is different from the first programming language. Thereafter, a schema associated with the first operator and a schema associated with the second operator is determined along with the compatibility between the schema of the first operator and the schema of the second operator. A query error message is generated in response to incompatibility between the first operator schema and the second operator schema. Compatibility is determined when an output generated by execution of the first operator provides machine data needed as input for execution of the second operator.

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.