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Given a massive and dynamic space of information (nuggets) and a query to be answered, how can the correct (answer) nuggets be retrieved in an effective and efficient manner? We present a large-scale distributed real-time architecture based on anytime intelligent foraging, gathering, and matching (I-FGM) on massive and dynamic information spaces. Simply put, we envision that when given a search query, large numbers of computational processes are alerted or activated in parallel to begin identifying and retrieving the appro-priate information nuggets. In particular, our approach aims to provide an anytime capa-bility which functions as follows: Given finite computational resources, I-FGM will pro-ceed to explore the information space and, over time, continuously identify and update promising candidate nugget, thus, good candidates will be available at anytime on re-quest. With the computational costs of evaluating the relevance of a candidate nugget, the anytime nature of I-FGM will provide increasing confidence on nugget selections over time by providing admissible partial evaluations. When a new promising candidate is identified, the current set of selected nuggets is re-evaluated and updated appropriately. Essentially, I-FGM will guide its finite computational resources in locating the target in-formation nuggets quickly and iteratively over time. In addition, the goal of I-FGM is to naturally handle new nuggets as they appear. A central element of our framework is to provide a formal computational model of this massive data-intensive problem.
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