Systems and methods are disclosed for flexibly applying a query term to heterogeneous data. A query system can receive a query that includes a data-determinant query term. As the system executes the query it can generate interim search results. As the system query processes the interim search results based on the query, it can apply the data-determinant query term to records of the interims search results based on the structure of the records.

A current system allows data-driven hypothesis generation to identify therapeutic candidates for a disease phenotype treatment by identifying drugs and clinical indications associated with lower occurrences of disease-associated phenotype(s) by a drug/drug class. A current system may include a pharmaceutical hierarchical ontology; a phenotype hierarchical ontology; a record database comprising clinical event records; a database mining engine; and a mapping engine. The database mining engine may iteratively progress through a portion of the pharmacological hierarchical ontology and phenotype hierarchical ontology to iteratively select pairs of cohort entries from each ontology; and for each pair of cohort entries, query the clinical record database for matching records. The mapping engine may map each pair of cohort entries into a matrix comprising a drug-event cell for each pair and apply a value thereto representing the number of database records matching items returned by the database mining engine for the corresponding cohort entries.

Data mining operations are performed within a columnar database management system. The columnar database management system stores input sets of data for a data mining operation. An input set of data is represented as a column of data in the columnar database management system. The columnar database management system stores instructions to perform one or more data mining operations for processing the input sets of data. The columnar database management system receives requests for performing data mining operations and performs the processing of the data mining operation within the columnar database management system. As a result, the processing of data mining operations is performed without requiring multiple data transfers between an application implementing the data mining operations and the columnar database management system.

A data analytics system may include a first risk relationship data store containing electronic records that represent a plurality of risk relationships between the enterprise and a first risk relationship provider. Similarly, a second risk relationship data store containing electronic records that represent a plurality of risk relationships between the enterprise and a second risk relationship provider. A back-end application computer server may include a data mining engine that analyzes a set of electronic records in the first and second risk relationship data stores to identify flags corresponding to risk drivers. A predictive analytics engine may then calculate a risk score associated with the set of electronic records based on the associated entity attribute values and the identified flags corresponding to risk drivers. An insight platform may automatically generate a recommended action for the enterprise to lower the calculated risk score.

A system and method for providing sensory analytics responses. The method comprises collecting raw data from a plurality of data sources; extracting, from the collected raw data, a subset of the raw data to be analyzed; generating, based on the extracted subset of the raw data, an analytics dataset, wherein the analytics dataset includes a performance indicator; determining, based on at least one received input, at least one query; determining, based on the generated analytics dataset, a response to the at least one query, wherein the response includes at least one sensory output; and causing projection of the determined at least one sensory output.

Methods and devices for propagating transactions in a network of nodes, each node having one or more connections to other nodes. The method includes receiving a plurality of incoming transactions over a time period; combining the plurality of incoming transactions using network coding to generate a composite message; sending the composite message to one or more nodes in the network; and determining an adjusted time period based on an equilibrium constant parameter and a count of transactions in the plurality of incoming transactions received over the time period.

The present application discloses an information processing method performed at a computing device. The method includes: collecting first information; executing an intent identification task on the first information to obtain an intent identification processing result; executing a slot identification task on the first information according to the intent identification processing result to obtain a slot identification processing result; narrowing a search range according to the slot identification processing result; and performing a database search for the first information within the narrowed search range.

A data server may support segment identification based on a selected user profile. For example, a user may select a user profile as the basis for identifying a segment of additional user profiles. The server may identify attributes associated with the selected user identifier and generate an expression based on the identified subset. The expression may include a normalization function corresponding to at least one attribute. The normalization function may identify correlated attribute values for an attribute associated with the selected user profile. The data server may query a data storage system to identify the additional user profiles based on the expression. The data server may also support user defined Boolean expressions such that the expression is used to identify user identifiers associated with a first attribute and a second attribute.

Systems and methods are described for processing ingested data in an asynchronous manner as the data is being ingested to detect potential anomalies. For example, one or more streaming data processors can convert data as the data is ingested into a comparable data structure, determine whether the comparable data structure should be assigned to an existing data pattern or a new data pattern, and optionally update a characteristic of the data pattern to which the comparable data structure is assigned. The streaming data processor(s) can perform these operations automatically in real-time or in periodic batches. Once one or more comparable data structures have been assigned to one or more data patterns, the streaming data processor(s) can analyze the comparable data structures assigned to a particular data pattern to determine whether any of the comparable data structures appear to be anomalous.

A computer-implemented method includes a method comprising using at least one hardware processor to: receive a plurality of data from a plurality of data sources; standardize the plurality of data; tag the standardized plurality of data with one or more companies; train a prediction model to predict a metric for each of the one or more companies based on the standardized plurality of data tagged with that company and historical measurements for that company; and apply the prediction model to new data to predict the metric for at least one of the one or more companies.