Abstract: Disclosed herein is a method of facilitating provisioning contexts for business situations. Accordingly, the method may include receiving at least one business dataset associated with a business organization in real time from a device, analyzing the at least one business dataset, extracting a plurality of business events from the at least one business dataset based on the analyzing of the at least one business dataset, analyzing the plurality of business events, determining a plurality of business insights from the plurality of business events using the event assessment algorithm, analyzing the plurality of business insights, determining at least one connection between at least two of the plurality of business insights, constructing a semantic graph for providing a context for each of at least one business situation associated with the business organization, and storing the semantic graph.

Abstract: Systems and methods are described for automatically identifying cause-effect relationships using hybrid systems. A server may retrieve selected expert rule templates that have input parameters that match the parameters of event data objects derived from a stream of input data. Cause-effect relationships may be determined between parameters of the data set when the selected expert rule templates are satisfied. The rule-based aspect is augmented using statistical correlation to identify a correlated pair of different parameters based on pairwise comparison of all parameters of a data set and a coincidence probability of the different parameters. Using the identified correlated pair, the server may create a new expert rule template in an expert rule database. Subsequent data in the stream of input data may trigger generating an alert when one of the expert rule templates is contradicted by the incoming data, thereby ensuring that the expert rule templates are up-to-date and accurate.

Abstract: Systems and methods are described for automatically generating natural language queries and chatbot-assisted responses to the queries. A server may generate observed fact data structures from numeric-type data columns of a data set received from a client device over a network connection. From the generated observed facts, a subset of priority observed facts may be identified based on a plurality of priority factors associated with each observed fact. To generate actionable text recommendations, the server may combine one of the priority observed facts with a natural language template retrieved from a template database. The resulting populated natural language template may then be augmented with a selected call to action query selected based on a received user explanation type. The chatbot service may then cause a text recommendation to be transmitted to a user device that is responsive to the format and values contained within the augmented natural language template.