Two words: “comprehensive knowledge.” Business intelligence applications require comprehensive data about a subject so that they can perform various transformations and reveal what the data really means. Discovery is the first stage of many on our journey to building comprehensive knowledge from trustworthy sources. We begin by finding as many sources as possible that know something about the subject we’re interested in learning more about, then we poll these sources to gather supplemental data about an entity (later we will address how to determine the reliability of one source versus any other source).

Cloud-based harvester servers receive and queue commands for supplemental data from Trusted Discovery Technology. First, an array of harvester CPUs poll the harvester server requesting tasks to complete. Next, harvester CPUs gather the specified information from the assigned public and private sources. The harvester CPUs upload the information back to the harvester server for processing via Trusted Discovery.

The collective set of sources used for an entity record set is called a “Jury.” The role of a Jury is to examine evidence and adjudicate whether or not the source is authentic and pertinent to “vote” on a quality verdict / judgement.

First, we start by constructing an Experimental Persona. A Profile is comprised of everything the Harvesters and Parsers learn from all available Sources. Every Source provides Contributors linked to the Experimental Persona. There are many Contributors for any single Experimental Persona. The reason the experimental persona is called “experimental” is it has not been authenticated and verified as being real. See the Trusted Discovery to understand those fundamentals.

The Jury is tasked with trying to ascertain the best collective knowledge regarding the Experimental Persona. How the Jury approaches this task is the subject of Jury & Authenticity.