A Business Process (BP for short) consists of a set of activities that achieve some business goal when combined in a flow. Among all the (maybe infinitely many) possible execution flows of a BP, analysts are often interested in identifying flows that are "most important", according to some weight metric. This paper studies the following problem: given a specification of such a BP, a weighting function over BP execution flows, a query, and a number k , identify the k flows with the highest weight among those satisfying the query. We provide here, for the first time, a provably optimal algorithm for identifying the top-k weighted flows of a given BP, and use it for efficient top-k query evaluation.

Web-sites for on-line shopping typically offer a vast number of product options and combinations thereof. While this is very useful, it often makes the navigation in the site and the identification of the "ideal" purchase (where the notion of ideal differs among users) a confusing, non-trivial experience. This demonstration presents ShopIT (ShoppIng assitanT), a system that assists on-line shoppers by suggesting the most effective navigation paths for their specified criteria and preferences. The suggestions are continually adapted to choices/decisions taken by the users while navigating. ShopITis based on a set of novel, adaptive, provably optimal algorithms for TOP-K query evaluation.