Abstract: A system and method for collecting data analytics on the one or more business events, receiving a prior set of knowledge about the one or more business events, generating, for presentation to the user, the timeline of the set of relevant information on the one or more business events based on processing the data analytics in combination with the prior set of knowledge, generating a current hypothesis for the one or more business events associated with the one or more business ontologies associated with the one or more business events, the current hypothesis modifying the prior set of knowledge over a period of time, receiving a natural language query from the user for interacting with the timeline of the set of relevant information and the current hypothesis, and generating, for presentation to the user, a response from the timeline of the set of relevant information and the current hypothesis.

Abstract: A system and method for generating a prediction are disclosed. In one embodiment, the method includes receiving a plurality of time-based documents; receiving a user query including a time period of interest defining a subset of the time-based documents from which to generate a prediction; and a plurality of cloud-based software agents classifying the subset of the plurality of time-based documents into a plurality of classes for the plurality entities, wherein the plurality of cloud-based software agents intercommunicate using distributed processing, wherein each of the plurality of cloud-based software agents is dedicated to one of the entities in the plurality of entities; and generating, using at least one machine learning method, the prediction based on the subset of the plurality of time-based documents for at least one of a plurality of categories. However, other embodiments are disclosed.

Abstract: A user establishes a profile with an enterprise intelligence system. The user specifies a watchlist identifying entities such as members and/or enterprises in which the user is interested. The user also specifies recommendation criteria specifying the types of recommenders to use to identify connections with the entities. The recommenders are used to identify attributes in common between the user and members, and to generate a connection map describing social connections between the user and the members based on the common attributes. A recommendation model for the user generates decision aiding information. The connection map is organized and presented to the user based on the generated decision aiding information. User interactions with the connection map are observed and the recommendation model is updated based on the observed interactions. A pattern of recommenders preferred by the user is learned from the observed interactions, and is used to guide future decision making.