Refactoring the search problem

The most common way of framing the search problem is as an exchange between a user and a database, where the user issues queries and the database replies with results that satisfy constraints imposed by the query but that also optimize some notion of relevance. There are several variations to this basic model that augment the dialogue between humans and machines through query refinement, relevance feedback, and other mechanism. However, rarely is this problem ever posed in a way in which the properties of the client and server are fundamentally different and in a way in which exploiting the differences can be used to yield substantially different experiences. I propose a reframing of the basic search problem which presupposes that servers are scalable on most dimensions but suffer from low communication latencies while clients have lower scalability but support vastly richer user interactions because of lower communication latencies. Framed in this manner, there is clear utility in refactoring the search problem so that user interactions are processed fluidly by a client while the server is relegated to pre-computing the properties of a result set that cannot be efficiently left to the client. I will demonstrate Pivot, an experimental client application that allows the user to visually interact with thousands of search results at once, while using facetted-based exploration in a zoomable interface. I will argue that the evolving structure of the Web will tend to push all IR-based applications in a similar direction, which has the algorithmic intelligence increasingly split between clients and servers. Put another way, my claim is that future clients will be neither thin nor dumb.