In accordance with an embodiment, described herein is a system and method for providing extensibility in an analytic applications environment, including a semantic layer that enables the use of custom semantic extensions to extend a semantic data model (semantic model). In accordance with an embodiment, customizations to the out-of-the-box semantic model are performed using a layered approach, wherein the factory code for the semantic model remains intact, with changes/delta editable by the customer layered on top of that model, such that the changes can be patched/reversed if necessary.

A non-transitory computer readable storage medium has instructions executed by a processor to receive from a network connection different sources of unstructured data. An entity is formed by combining one or more sources of the unstructured data, where the entity has relational data attributes. A representation for the entity is created, where the representation includes embeddings that are numeric vectors computed using machine learning embedding models, including trunk models, where a trunk model is a machine learning model trained on data in a self-supervised manner. An enrichment model is created to predict a property of the entity. A query is processed to produce a query result, where the query is applied to one or more of the entity, the embeddings, the machine learning embedding models, and the enrichment model.

Aspects of the disclosure relate to transliteration of machine interpretable languages. A computing platform may receive a query formatted in a first format for execution on a first database. The computing platform may translate the query to a second format for execution on a second database by: 1) extracting non-essential portions of the query from the query, and replacing the non-essential portions of the query with pointers to create a query key; 2) storing, along with their corresponding pointers, the non-essential portions of the query as query parameters; 3) executing a lookup function on a query library to identify a translated query corresponding to the query key and including the corresponding pointers; and 4) updating the translated query to include the query parameters based on the corresponding pointers to create an output query. The computing platform may execute the output query on the second database.

Methods, systems, devices, and tangible non-transitory computer readable media for configuring and implementing application policies are provided. The disclosed technology can access application policy data associated with implementing an application policy. The application policy data can include rules associated with implementing the application policy by using organizational data associated with a plurality of applications that includes a set of extra-organizational applications that perform operations associated with a different set of extra-organizational applications. Based on the application policy data, organizational records of the organizational data that satisfy the one or more rules can be determined. The plurality of applications associated with the one or more organizational records that satisfy the one or more rules can then be accessed. Furthermore, based at least in part on the application policy and the one or more organizational records, the one or more operations associated with implementing the application policy can be performed.

Routines are used to connect bot queries entered via a bot interface and database queries executed on a database. Each routine is associated with routine configurations, including (a) query attributes associated with bot queries that trigger the routine, (2) a database query executed or to be executed for the routine, and/or (3) display settings for displaying information returned from the database query in a bot response. Each routine is configured to generate an output structured data object (SDO) indicating information returned from the database query and the display settings applicable to the information. The output SDOs from the various routines are transmitted to the bot application via a single API endpoint. The routine configurations are entered by a design user through one or more design user interfaces rendered by an integrative configuration application module.

Systems, methods, and computer-readable media for asynchronous (async) querying are described. In embodiments, a system may convert a user-issued query into a distributed execution instruction set (DEIS), and issue the DEIS to data stores that may have various database structures. The system may obtain database objects from the data stores, and store a result set indicating the obtained database objects in a location specified by the user-issued query. The system may also provide mechanisms to allow users to view progress of their async query jobs and/or cancel ongoing async query jobs. Other embodiments may be described and/or claimed.

Disclosed are a data file distribution method and equipment, a smart device and a computer storage medium. The method includes the following operations: sorting data files according to an access frequency of each data file, a sorting mode including an ascending order or a descending order; dividing the data files into at least two data blocks according to a sorted order, numbers of data files in the at least two data blocks being equal; merging the data files in each of the at least two data blocks in pairs to update the data files; sorting the updated data files according to the access frequency of each data file until the numbers of the data files are equal to numbers of distributed nodes; and placing the data files on corresponding distributed nodes.

Implementations include querying metadata of data objects to define a sub-set of data objects, each data object in the sub-set of data objects including a generic text field and/or an attachment field, and, for each data object in the sub-set of data objects, processing historical data of a data object to identify a set of data types, the historical data stored within a field of a table of a database system, providing a recommendation for a first extension corresponding to a first data type, and receiving user input indicating acceptance of the recommendation for the first extension and, in response, automatically providing extension code that is executable to add a field extension to the table and to modify a UI of an application for input of values corresponding to the first data type, and executing the extension code to deploy the extension and to modify the UI.

Variables relevant to implementing solution automation & interface analysis determine how different implementations can be found/generated/derived & filtered.

Additionally, additional specific example implementations of components of solution automation & interface analysis (like example solution automation workflows, function types, and useful structures) to implement solution automation & interface analysis are included in the specification of this invention.

A method for performing recursive searching of items of a data structure having a data mode includes creating an instance of a query definition, the instance of the query definition comprising a unique identifier, specifying one or more elements of the query definition, providing the query definition as an input to a query engine. The method further includes the operations of determining, by the query engine, query execution instructions based on the query definition, the query instructions specifying a recursive level-by-level search until a terminal node of the data structure is reached, obtaining results of a query executed based on the query execution instructions; and outputting query results.