Technologies are described herein for executing queries expressed with reference to a structured query language against unstructured data. A user issues a structured query through a traditional structured data management (“SDM”) application. Upon receiving the structured query, an SDM driver analyzes the structured query and extracts a data structure from the unstructured data, if necessary. The structured query is then converted to an unstructured query based on the extracted data structure. The converted unstructured query may then be executed against the unstructured data. Results from the query are reorganized into structured data utilizing the extracted data structure and are then presented to the user through the SDM application.

A system for predicting and prescribing actions for impacting policymaking outcomes may include at least one processor configured to access first information scraped from the Internet to identify, for a particular pending policy, information about a plurality of policymakers slated to make a determination on the pending policy. The processor may parse the scraped first information to determine an initial prediction relating to an outcome of the pending policy. The processor may access second information to identify an action likely to change at least one of the initial prediction and the propensity of at least one policymaker, to thereby generate a subsequent prediction corresponding to an increase in a likelihood of achieving the desired outcome. The processor may display to the system user a recommendation to take the action in order to increase the likelihood of achieving the desired outcome.

One example method includes, in a data buffer that includes one or more words and whitespaces, calculating a hash value of data in a window that is movable within the data buffer, comparing the hash value to a mask, and when the hash value matches the mask, identifying a position of the window in the data buffer as a chunk anchor position, searching for a whitespace nearest the chunk anchor position, and designating an offset of the whitespace as a segment boundary.

A computer implemented system, method, or computer readable medium for providing a visual interactive software tool that permits users to investigate and evaluate financial, trade, and business-related risks. An interactive tool is provided that can integrate front and back end capabilities to, for example, provide quick and efficient navigation to pertinent information in a visual workspace. The system, method, or computer readable medium can be configured to include a software engine that identifies corridors or other subgraph features in accordance with automated traversal based on a set of rules. The features can provide a notification and selective visual access to risk-related subgraphs based on topic, category, or sanctions as a virtual lens for the user in connection with a displayed node.

A method is provided for an application program interface (API) to interface with an ontology store storing a plurality of modifiable ontology models having associated dynamic definitions associated that define classes of the associated ontology model and relationships between the respective classes and that is modifiable over time. The method includes receiving from a requesting entity a request that specifies an ontology model and one or more parameters defining attributes of an instantiated ontology object, accessing the ontology store, identifying an ontology model in the ontology store that corresponds to the ontology model specified, and manipulating the identified ontology model based on its one or more parameters. The method further includes generating a semantics query for accessing the identified ontology model based on the one or more parameters specified in the request, submitting the semantics query to and receiving query results from the ontology store, and returning the query results to the requesting entity.

A method processes information. Training pairs are generated by a computer system using matching fields in matching pairs of records for a data type, wherein matches are present between the matching fields in the matching pairs of records. Similarities between the training pairs are determined by the computer system using an importance map with importance values for the matching fields. Shapley values are determined by the computer system using the training pairs and the similarities between the training pairs. The importance map is adjusted by the computer system using the Shapley values.

A storage structure for data containing relational objects, as well as methods for retrieval and visualized display of data so stored are capable of recording relations among all objects in any scenario, as well as new relational states caused by changes and corresponding events happening in succession. The storage structure uses uniform formats to process information into logical and relational data, thereby contributing to subsequent characteristic analysis, supporting retrieval by objects and designated relational modes, and providing accurate display of information and its variations through versatile, visualized way.

In one embodiment, a method includes, by a client system of a user of an online social network, sending, to one or more computing devices of the online social network, a text post inputted by the user. The text post is associated with a query. The method includes receiving, from the one or more computing devices in response to the text post, an initial recommendation list. The initial recommendation list includes references to one or more objects referenced in comments associated with one or more prior posts associated with the query. The method includes sending, to the one or more computing devices, a request for updates to the initial recommendation list. Moreover, the method includes receiving, from the one or more computing devices in response to the request, an updated recommendation list comprising references to one or more objects referenced in comments associated with the text post.

Systems, methods, and devices are described for classifying and answering medical inquiries based on machine-generated data resources and machine learning models. A CCDA document including clinical information and observations of a patient are received from a requestor and utilized to generate a FHIR model instance specific to the CCDA document. Question text having medical inquires for the patient, and received with the CCDA document, is processed by a machine learning model to determine question categories for the medical inquiries which are utilized to map the inquires to objects of the model instance using another machine learning model. The model instance is queried based on the mapping to return values associated with the inquiries. The values are transmitted back to the requestor.

An apparatus for use in a communication system sets, in a search request serving to retrieve, from a storage entity, records that match filters, an indication which indicates if, for one or more matching records of the records that match the filters, a content of the one or more matching records is to be received in a response to the search request along with references to the records that match the filters, and transmits the search request including the indication.