Abstract: Enterprise Hypothesis Orchestration provides users an intuitive system for building an inquiry model that thereafter creates and evaluates each of a plurality of hypotheses as it continuously searches for evidence to formulate, score, and resolve each hypothesis. The Enterprise Hypothesis Orchestration system moreover continuously deals with the uncertainty caused by noisy, missing, inaccurate, and/or contradictory data. The present invention uses abductive reasoning to infer the best explanation or hypothesis for a set of observations. Given an inquiry the Hypothesis Orchestration System identifies relevant data from which to form a plurality of hypotheses. It thereafter collects evidence in support of each hypothesis and crafts a degree of confidence that the hypothesis is true. If a hypothesis is found to lack support an analysis of any missing evidence is conducted to identify and seek which evidence would offer the highest benefit to resolving one or more of the plurality of hypotheses.

Abstract: Enterprise Hypothesis Orchestration provides users an intuitive system for building an inquiry model that thereafter creates and evaluates each of a plurality of hypotheses as it continuously searches for evidence to formulate, score, and resolve each hypothesis. The Enterprise Hypothesis Orchestration system moreover continuously deals with the uncertainty caused by noisy, missing, inaccurate, and/or contradictory data. The present invention uses abductive reasoning to infer the best explanation or hypothesis for a set of observations. Given an inquiry the Hypothesis Orchestration System identifies relevant data from which to form a plurality of hypotheses. It thereafter collects evidence in support of each hypothesis and crafts a degree of confidence that the hypothesis is true. If a hypothesis is found to lack support an analysis of any missing evidence is conducted to identify and seek which evidence would offer the highest benefit to resolving one or more of the plurality of hypotheses.

Abstract: A fuzzy complex event processing (CEP) system successfully processing noisy, incomplete, multi-source data in support of near real-time decision-making. The fuzzy CEP solution of the present invention supports decision-making by identifying and exploiting patterns hidden in complex data and can operate in a forensic mode against historical data, near real-time mode for proactive decision-making, or any combination thereof. Fusion algorithms and techniques are applied to observation data that may only partially satisfy an event description in time, space, or other relevant dimensions. Using context propagation, Bayesian reasoning, and spatiotemporal analysis, the present invention provides both predictive awareness of upcoming events and likelihood analysis for events that may have already occurred, but were not evident in the collected data, while at the same time minimizing false detections.