Efficient data store discovery in a scientific P 2 P network

In this paper we propose to use schema-based peer-to-peer systems for the exchange of scienitific documents. Compared with recent file sharing networks highly domain-oriented scientific documents are shared in relatively small research communities with tight internal and only a few external connections. The documents are annotated with small a but well-defined sets of metadata using standard taxonomies and are stored in many distributed, autonomous, heterogenous data repositories. As a recent development super-peer networks emerge improving the network performance by clustering peers at super-peers. By using Semantic Overlay Clusters (SOC) for clustering the super-peer networks we enable the creation of context-specific, logical views over the physical P2P network topology according to the research communities demands. SOCs define peer clusters according to the metadata description of peers and their contents. The respective clustering policy expresses the demand on the peers for a particular research context. In this paper we show how SOCs can be defined based on policies. We detail the definition of conditions that we use for our ECA-rule like policiy definition approach. 1 Semantic Overlay Clusters in schema-based P2P networks Using peer-to-peer systems for the exchange of files, especially of music files, is a quite common application. Recently peer-to-peer networks have also been used successfully to interconnect between distributed heterogenous scientific data stores enabling the exchange of scientific documents and the search in complex heterogenous meta data structures. Examples for this new class of peerto-peer networks, so called schema based peer-to-peer networks, are [1, 6, 3, 16, 11]. Such networks combine approaches from peer-to-peer research as well as from the database and semantic web research areas. These networks allow the aggregation and integration of data from autonomous, distributed data sources. They build upon peers that use explicit schemas to describe their content. Naturally such meta data is pretty heterogenous as documents stem from a wide variety of domains and communities. However, current schema-based P2P networks still have some shortcomings, e.g. broadcasting all queries to all data store and are not scalable. Therefore, intelligent routingand network organization strategies are essential in such networks enabling queries to be routed to a semantically chosen subset of peers able to answer parts or whole queries. First approaches to enhance routing efficiency in a clustered network have already been proposed by [17] and [19]. Current schema based Peer-to-Peer Systems therefore distinguish between a technical network layer and a semantic clustering layer. While the technical layer provides efficient algorithms for maintaining an real existing network topology, a Semantic Overlay Clusters (SOC) [19] layer provides a virtual context-specific view on selected peers. The SOC Layer therefore abstracts from the underlaying technical infrastructure and topology and enables the use of efficient integration technologies and an context specific query routing in schema based Peer-to-Peer networks. We expect that for scientific publication researchers gather in rather small communities with some connections among each other. In the context of P2P systems we can therefore assume that most document searches (queries) must not be broadcasted to all peers. Instead, queries should be send to only those peers that are able to answer them. By employing SOCs within the P2P network we advance the state-of-the-art for restricting complex query broadcast to only those peers capable of meaningful query-answering and of integrating small groups of schemas for a particular context (clustering) [10]. SOCs are designed for large and highly distributed networks improving search ∗Computation and Information Structures (CIS), Technische Universit ät Berlin, 10587 Berlin, Germany, aloeser@cs.tuberlin. de †Learning Lab Lower Saxony, 30167 Hannover, Germany, wolpers, siberski,nejdl@learninglab.de