Abstract: Access to sensitive information in a database can be restricted to improve security and enable efficient auditing. A security engine receives a request from a requesting entity to access data in the database and determines that the requested data includes sensitive information. In response to the requesting entity being authorized to access the data, the security engine retrieves the requested data from the database and modifies the retrieved data by modifying metadata of the retrieved data to include a tag indicating that the retrieved data includes sensitive information. The security engine provides the modified data to the requesting entity and modifies a data access log to identify each attempted access to the modified data. When sensitive data is requested, an interface can include an obscuring element, requiring a user to manually select the element to view the data, enabling the logging of the explicit access request by the user.

Abstract: A central database system provides predictive web navigation using machine learning and clustering to guide a user to a web page. After tracking a number of web pages viewed by various users on one or more web domains and the orders in which these web pages are viewed, the central database system can train a model to predict which web page a user is likely to view next. If the user is lost while navigating, the central database system may guide the user to the predicted web page. In one example of guidance, the central database system presents a web element with a hyperlink to the predicted web page. For example, the central database system can modify a web page that the lost user is presently viewing to include an iframe with a recommendation to travel to a different web page.

Abstract: A database management engine provides a user interface that allows users to access and modify employee information in a database. The database includes entries for employees, and each database entry includes identifying information about the associated employee. A user can request to modify data within database entries, for instance in order to update information associated with an employee. Responsive to the request, the database management engine identifies liabilities associated with the database modification stemming from associated tax laws. Based on the identified tax liabilities, the engine computes the aggregate tax liability owed by the employer and/or employee. Before modifying a database entry, the engine modifies the user interface to include interface elements detailing the computed aggregate tax liability. The user explicitly can be required to confirm the database modification in view of the aggregate tax liability.

Abstract: An enhanced routing system determines a service provider best suited to fulfill a user's request to interact and establishes a communication session between the user's client device and a device of the service provider. The enhanced routing system may use user characteristics and behavior to select the service provider. For example, the enhanced routing system receives a request to connect to a customer service system from a user who has recently started a new job and has been accessing a banking application on his mobile phone. The enhanced routing system may determine that a payroll service provider is best suited to fulfill the user's request. For example, the enhanced routing system uses a machine learning model that has been trained on previously fulfilled requests. In this way, the enhanced routing system improves upon systems that continuously prompt the user for information by selecting a service provider without overburdening the user.

Abstract: 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.

Abstract: A central database system provides predictive web navigation using machine learning and clustering to guide a user to a web page. After tracking a number of web pages viewed by various users on one or more web domains and the orders in which these web pages are viewed, the central database system can train a model to predict which web page a user is likely to view next. If the user is lost while navigating, the central database system may guide the user to the predicted web page. In one example of guidance, the central database system presents a web element with a hyperlink to the predicted web page. For example, the central database system can modify a web page that the lost user is presently viewing to include an iframe with a recommendation to travel to a different web page.

Abstract: Users of a database management engine may generate fillable digital documents by mapping interface elements onto form documents. When a user maps interface elements onto a form document, the user may accidentally overlap two or more interface elements. To rectify this, the database management engine may modify the position of one of interface elements based on a set of positioning rules. In addition, the database management engine may identify and suggest mappings to users based on similar documents that have been previously mapped. The database management engine identifies similar documents using information about the document, the user, and the mapping itself. The mapping associated with the most similar document may be provided to the user as a suggested mapping. The database management engine converts the form document and finalized mapping into a fillable digital document. The fillable digital document is sent to recipients, who complete the fillable digital document.

Abstract: Users of a database management engine may generate fillable digital documents by mapping interface elements onto form documents. When a user maps interface elements onto a form document, the user may accidentally overlap two or more interface elements. To rectify this, the database management engine may modify the position of one of interface elements based on a set of positioning rules. In addition, the database management engine may identify and suggest mappings to users based on similar documents that have been previously mapped. The database management engine identifies similar documents using information about the document, the user, and the mapping itself. The mapping associated with the most similar document may be provided to the user as a suggested mapping. The database management engine converts the form document and finalized mapping into a fillable digital document. The fillable digital document is sent to recipients, who complete the fillable digital document.

Abstract: A central database system allows entities to easily manage human resources functions. An entity requests that the central database system executes an employer function on its behalf, and the central database system determines a probability of the entity defaulting before the entity can finalize the employer function with the central database system. The central database system can train and apply a machine-learned model to dynamically determine the default probability for the entity. Based on the default probability, the central database system determines a risk tolerance associated with the employer function and determines whether to process or challenge the employer function based on the risk tolerance and the default probability.

Abstract: Access to sensitive information in a database can be restricted to improve security and enable efficient auditing. A security engine receives a request from a requesting entity to access data in the database and determines that the requested data includes sensitive information. In response to the requesting entity being authorized to access the data, the security engine retrieves the requested data from the database and modifies the retrieved data by modifying metadata of the retrieved data to include a tag indicating that the retrieved data includes sensitive information. The security engine provides the modified data to the requesting entity and modifies a data access log to identify each attempted access to the modified data. When sensitive data is requested, an interface can include an obscuring element, requiring a user to manually select the element to view the data, enabling the logging of the explicit access request by the user.

Abstract: A staging engine of a staging server receives a request to change a production database from a client device. The staging engine of the staging server accesses one or more schemas corresponding to the production database and determines one or more migration commands based on the received request and the accessed one or more schemas. The one or more migration commands correspond to a difference between a current structure of the production database and a final structure of the production database after the production database is updated. The staging engine transmits the one or more migration commands to a migration engine, wherein the migration engine asynchronously applies changes to the production database according to the one or more migration commands.

Abstract: A central database system trains a machine-learned model based on training data identifying characteristics of account holder entities, characteristics of account provider entities, and relationships between the account holder entities and account provider entities. For a target entity, the central database system then identifies a target set of account provider entities, and applies the trained machine-learned model to identify a subset of the target set of account provider entities. The identified subset of account provider entities are entities that, if recommended to the target entity, are most likely to result in an established relationship with the target entity. A recommendation is then generated for display to the target entity, the recommendation identifying the subset of account provider entities and including interface elements that, if selected by the target entity, cause a notification identifying the target entity to be sent to a corresponding account provider entity.

Abstract: A flexible advance system allows users to request and receive advances. The flexible advance system processes advances such that short-term advances are repaid through a wage deduction from the user's next regularly scheduled compensation period. The flexible advance system provides a user interface that allows users to request to receive compensation for a portion of time worked. Responsive to a request, an entry in a database associated with the user is accessed to determine a total amount of available advance compensation available for request by the user. In response to the requested amount being less than the total amount of available advance compensation, a transfer of compensation is executed to an account of the user. The flexible advance system also modifies the entry in the database by subtracting the compensated amount of time from the total amount of uncompensated time.

Abstract: A central database system receives information associated with an employee from an employer. Using this information, the central database system can provision one or more user accounts for the employee, for instance via an API of an account provider. The central database system can use a machine learned model to identify fields of the API and to translate the information associated with the employee based on information requirements associated with the API. When a characteristic of the employee, such as the employee's title, subsequently changes within the central database system, one or more features associated with the user account can be automatically updated in response to the change.

Abstract: An image processing system accesses an image of a completed form document. The image of the form document includes one or more features, such as form text, at particular locations within the image. The image processing system accesses a template of the form document and computes a rotation and zoom of the image of the form document relative to the template of the form document based on the locations of the features within the image of the form document relative to the locations of the corresponding features within the template of the form document. The image processing system performs a rotation operation and a zoom operation on the image of the form document, and extracts data entered into fields of the modified image of the form document. The extracted data can be then accessed or stored for subsequent use.

Abstract: 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.

Abstract: A database management engine provides a user interface that allows users to access and modify employee information in a database. The database includes entries for employees, and each database entry includes identifying information about the associated employee. A user can request to modify data within database entries, for instance in order to update information associated with an employee. Responsive to the request, the database management engine identifies liabilities associated with the database modification stemming from associated tax laws. Based on the identified tax liabilities, the engine computes the aggregate tax liability owed by the employer and/or employee. Before modifying a database entry, the engine modifies the user interface to include interface elements detailing the computed aggregate tax liability. The user explicitly can be required to confirm the database modification in view of the aggregate tax liability.

Abstract: Users of a database management engine may generate fillable digital documents by mapping interface elements onto form documents. When a user maps interface elements onto a form document, the user may accidentally overlap two or more interface elements. To rectify this, the database management engine may modify the position of one of interface elements based on a set of positioning rules. In addition, the database management engine may identify and suggest mappings to users based on similar documents that have been previously mapped. The database management engine identifies similar documents using information about the document, the user, and the mapping itself. The mapping associated with the most similar document may be provided to the user as a suggested mapping. The database management engine converts the form document and finalized mapping into a fillable digital document. The fillable digital document is sent to recipients, who complete the fillable digital document.

Abstract: A central database system allows entities to easily manage human resources functions. An entity requests that the central database system executes an employer function on its behalf, and the central database system determines a probability of the entity defaulting before the entity can finalize the employer function with the central database system. The central database system can train and apply a machine-learned model to dynamically determine the default probability for the entity. Based on the default probability, the central database system determines a risk tolerance associated with the employer function and determines whether to process or challenge the employer function based on the risk tolerance and the default probability.

Abstract: An enhanced routing system determines a service provider best suited to fulfill a user's request to interact and establishes a communication session between the user's client device and a device of the service provider. The enhanced routing system may use user characteristics and behavior to select the service provider. For example, the enhanced routing system receives a request to connect to a customer service system from a user who has recently started a new job and has been accessing a banking application on his mobile phone. The enhanced routing system may determine that a payroll service provider is best suited to fulfill the user's request. For example, the enhanced routing system uses a machine learning model that has been trained on previously fulfilled requests. In this way, the enhanced routing system improves upon systems that continuously prompt the user for information by selecting a service provider without overburdening the user.