The emergence of modern data-intensive applications requires sophisticated database techniques for processing advanced types of user queries on massive data. In this paper, we study such a new type of query, called progressive queries. A progressive query is defined as a set of interrelated and incrementally formulated step-queries. A step-query in a progressive query PQ is specified on the fly based on the results of previously-executed step-queries in PQ. Hence, a progressive query cannot be formulated in advance before its execution, which raises challenges for its processing and optimization. We introduce a query model to characterize different types of progressive queries. We then present a new index structure, called the collective index, to efficiently process progressive queries. The collective index technique incrementally evaluates step-queries via dynamically maintained member indexes. Utilizing the special structure of a collective index, the (member) indexes on the input relation(s) of a step-query are efficiently transformed into indexes on the result relation. Algorithms to efficiently process single-input (unary) linear and multipleinput (join) linear progressive queries based on the collective index are presented. Our experiment results show that the proposed collective index technique outperforms the conventional query processing methods in processing progressive queries.