Abstract: A computing architecture is optimized for the reuse of machine learning model. A request is received for a machine learning model recommendation that specifies parameters associated with a desired machine learning model (e.g., schema, etc.). Thereafter, a machine learning-based discovery model, recommends at least one machine learning model (to reuse) based on the parameters specified in the request. Next, data characterizing the recommended at least one machine learning model is provided (e.g., loaded into memory, displayed in a graphical user interface, transmitted to a remote computing system, and/or stored in physical persistence, etc.). In some variations, a graphical user interface can be rendered that allows a client application to select one of the recommended machine learning models and, further optionally, to activate the selected machine learning models. Related apparatus, systems, techniques and articles are also described.

Abstract: User-generated input is received that includes a sequence of words associated with initiation of a computer-implemented event. Thereafter, such input is parsed using at least one natural language processing (NLP) model. This parsed input is then used by a machine learning model to determine a suggested template having a plurality of fields for initiating the event. The template can then be presented in a graphical user interface. Related apparatus, systems, techniques and articles are also described.

Abstract: The present disclosure involves systems, software, and computer implemented methods for creating line item information from tabular data. One example method includes receiving event data values at a system. Column headers of columns in the event data values are identified. At least one column header is not included in standard line item terms used by the system. Column values of the columns in the event data values are identified. The identified column headers and the identified column values are processed using one or more models to map each column to a standard line item term used by the system. The processing includes using context determination and content recognition to identify standard line item terms. An event is created in the system, including the creation of line items from the identified column value. Each line item includes standard line item terms mapped to the columns.

Abstract: User-generated input is received that includes a sequence of words associated with initiation of a computer-implemented event. Thereafter, such input is parsed using at least one natural language processing (NLP) model. This parsed input is then used by a machine learning model to determine a suggested template having a plurality of fields for initiating the event. The template can then be presented in a graphical user interface. Related apparatus, systems, techniques and articles are also described.

Abstract: Example embodiments for classification are described. In an example embodiment, a request including a text term to be classified is received from a source system via a communication network at a computer system. A rule associated with the text term is accessed, in which the rule indicates at least one classifier of a plurality of classifiers installed at the computer system. Classification information for the text term is generated at the computer system using the at least one classifier indicated by the rule. The generated classification information includes a classification selected from a taxonomy by the at least one classifier. The generated classification information is transmitted via the communication network to the source system.

Abstract: A system includes reception of a definition of a first attribute to associate with a logical object of the first application, reception of a definition of a second attribute to associate with a second logical object of the first application, determination, based on the definition of the first attribute and the definition of the second attribute, that the first attribute and the second attribute are associated with a same semantic, based on the determination, generation of e a first identifier associated with the first attribute and the second attribute and storing the first identifier associated with the first attribute and the second attribute in a first data schema associated with the first application, and storage of the first identifier associated with the first attribute and the second attribute in an analytics schema associated with an analytics application.

Abstract: Various examples are directed to systems and methods for generating a contract form using a contract library data structure. A computing device may receive, from a user, a contract form request comprising a set of contract classifiers. The computing device may access a contract library data structure and executes a filter routine to traverse the contract library data structure to identify at least one contract term associated with one or more filter tags corresponding to the set of contract classifiers. The filter routine may return an indication of the first contract term associated with the first node. The computing device may generate a contract form comprising the first contract term.

Abstract: Various examples are directed to systems and methods for identifying database records in a database table. A database management system receives a search request comprising a first set of strings associated with a first column of the database table and a second set of strings associated with a second column of the database table. The database management system selects a set of first column keywords using the first set of strings and executes a first tier query at the database table, the first tier query comprising, a must clause based at least in part on the set of first column keywords and a should clause based at least in part on a set of second column keywords. Responsive to determining that no database record returned by the first tier query has a relevance score greater than a threshold value, the database management system executes a second tier query at the database table.

Abstract: A method of processing raw data as it is received from a data provider via an input channel is disclosed. Values are derived from the raw data as it is received from the data provider via the input channel. The derived values correspond to custom fields of a summary table. The summary table is configured to store a summary of the raw data The custom fields correspond to data capable of improving an analysis of an entity by an analysis tool. The derived values are inserted into the custom fields of the summary table. Access to the summary table is provided as enriched data for use by the analysis tool to improve the analysis of the entity.

Abstract: Example embodiments for accessing data are described. In an example embodiment, a request is received for a data record including multiple data fields. Based on location information for each field from a metadata file, each field of a first portion of the multiple fields is retrieved from a local data store, with each of the fields of the first portion having a corresponding size less than a threshold size. Each retrieved field of the first portion may be presented to a user, along with a field indicator for at least one field of a second portion of the multiple fields, with each of the fields of the second portion having a corresponding size at least as large as the threshold size. In response to a selection of the field indicator, at least one field of the second portion may be retrieved and presented to the user.

Abstract: Disclosed herein are system, method, and computer program product embodiments for converting a static contract clause into an operationalizing contract clause on a blockchain. An embodiment operates by converting the static contract clause to an operationalized contract clause having a trigger and an action. The embodiment generates executable code for a blockchain based on the trigger and the action. The embodiment deploys the executable code on a block in the blockchain. The embodiment then executes the action in the executable code in the blockchain in response to receiving the trigger.

Abstract: Example embodiments for classification are described. In an example embodiment, a request including a text term to be classified is received from a source system via a communication network at a computer system. A rule associated with the text term is accessed, in which the rule indicates at least one classifier of a plurality of classifiers installed at the computer system. Classification information for the text term is generated at the computer system using the at least one classifier indicated by the rule. The generated classification information includes a classification selected from a taxonomy by the at least one classifier. The generated classification information is transmitted via the communication network to the source system.

Abstract: Example embodiments for accessing data are described. In an example embodiment, a request is received for a data record including multiple data fields. Based on location information for each field from a metadata file, each field of a first portion of the multiple fields is retrieved from a local data store, with each of the fields of the first portion having a corresponding size less than a threshold size. Each retrieved field of the first portion may be presented to a user, along with a field indicator for at least one field of a second portion of the multiple fields, with each of the fields of the second portion having a corresponding size at least as large as the threshold size. In response to a selection of the field indicator, at least one field of the second portion may be retrieved and presented to the user.

Abstract: Provided is a method and system for scheduling a task in a data warehouse. A task in a data warehouse is scheduled based on selection of a report related to the data warehouse.

Abstract: Presented is a method of managing exclusive access to a resource. The method includes determining anticipated wait time, for a task to obtain exclusive access to a resource, and processing the task, depending on the anticipated wait time required to obtain exclusive access to the resource.

Abstract: Provided is a method of loading data in a data warehouse. Two types of work tables are generated, namely, a first work table and a second work table. The data is loaded into the first work table and prepared for loading, using the second work table as temporary table for data preparation. The prepared data is loaded into the data warehouse.

Abstract: Provided is a method of loading data in a data warehouse. Two types of work tables are generated, namely, a first work table and a second work table. The data is loaded into the first work table and prepared for loading, using the second work table as temporary table for data preparation. The prepared data is loaded into the data warehouse.

Abstract: Provided is a method and system for scheduling a task in a data warehouse. A task in a data warehouse is scheduled based on selection of a report related to the data warehouse.

Abstract: Presented is a method of managing exclusive access to a resource. The method includes determining anticipated wait time, for a task to obtain exclusive access to a resource, and processing the task, depending on the anticipated wait time required to obtain exclusive access to the resource.