Peer-to-peer (P2P) overlay networks allow for efficient information discovery in large-scale distributed systems. Although point queries are well supported by current P2P systems - in particular systems based on distributed hash tables (DHTs) -, providing efficient support for more complex queries remains a challenge. Therefore, the goal of this research is to develop methodologies that enable efficient processing of complex queries, in particular processing of multi-attribute range queries, over DHTs.

Generally, the support for multi-attribute range queries over DHTs has been provided either by creating an individual index for each data attribute or by creating a single index using the combination of all data attributes. In contrast to these approaches, we propose to create and modify indices using the attribute combinations that dynamically appear in multi-attribute range queries in the system.

In order to limit the overhead induced by index maintenance, the total number of created indices has to be limited. Thus, one of the major problems is to create a limited number of indices such that the overall system performance is optimal for multi-attribute range queries. We propose several index recommendation algorithms that implement heuristic solutions to this NP-hard problem. Our evaluations show that these heuristics lead to a close-to-optimal system performance for multi-attribute range queries.

The final outcome of this research is an adaptive DHT-based information discovery system that adapts its set of indices according to the dynamic load of multi-attribute range queries in the system. The index adaptation is carried out using a four-phase index adaptation process. Our evaluations show that the adaptive information discovery system continuously optimizes the overall system performance for multi-attribute range queries. Moreover, compared to a non-adaptive system, our system achieves several orders of a magnitude improved performance.