No abstract

Much of the present P2P-IR literature is focused on distributed indexing structures. Within this paper, we present an approach based on the replication of peer data summaries via rumor spreading and multicast in a structured overlay.We will describe Rumorama, a P2P framework for similarity queries inspired by GlOSS and CORI and their P2P-adaptation, PlanetP. Rumorama achieves a hierarchization of PlanetP-like summary-based P2P-IR networks. In a Rumorama network, each peer views the network as a small PlanetP network with connections to peers that see other small PlanetP networks. One important aspect is that each peer can choose the size of the PlanetP network it wants to see according to its local processing power and bandwidth. Even in this adaptive environment, Rumorama manages to process a query such that the summary of each peer is considered exactly once in a network without churn. However, the actual number of peers to be contacted for a query is a small fraction of the total number of peers in the network.Within this article, we present the Rumorama base protocol, as well as experiments demonstrating the scalability and viability of the approach under churn.

The retrieval facilities of most peer-to-peer (P2P) systems are limited to queries based on a unique identifier or a small set of keywords. The techniques used for this purpose are hardly applicable for content based image retrieval (CBIR) in a P2P network. Furthermore, we will argue that the curse of dimensionality and the high communication overhead prevent the adaptation of multidimensional search trees or fast sequential scan techniques for P2P CBIR. In the present paper we will propose two compact data representations that can be distributed in a P2P network and used as the basis for a source selection. This allows for communicating with only a small fraction of all peers during query processing without deteriorating the result quality significantly. We will also present experimental results confirming our approach.

No abstract

A Geographic Information Retrieval (GIR) system for answering geographic queries has to cope with various information needs, which have a wide range of contexts and implicit requirements. A user, for example, who is looking for a place to spend his or her holidays certainly has a different understanding of distance than a user looking for a bar in the city he or she lives in.To get a better understanding of geographic information needs and their implications for GIR systems, we analysed real world (geographic) queries with regard to different facets of geographic references in queries. The results of this analysis are presented in this paper, the aim of which was a classification of the geographic aspects of information needs. We present empirical results and line out possible classification criteria, which could be helpful in designing GIR systems that are able to consider different semantics of geographic references in queries.