Peer-to-peer (P2P) systems research has gained considerable attention recently with the increasing popularity of file sharing applications. Since these applications are used for sharing huge amounts of data, it is very important to efficiently locate the data of interest in such systems. However, these systems usually do not provide efficient search techniques. Existing systems offer only keyword search functionality through a centralized index or by query flooding. In this paper, we propose a scheme based on reference vectors for sharing multi-dimensional data in P2P systems. This scheme effectively supports a larger set of query operations (such as k-NN queries and content-based similarity search) than current systems, which generally support only exact key lookups and keyword searches.The basic idea is to store multiple replicas of an object's index at different peers based on the distances between the object's feature vector and the reference vectors. Later, when a query is posed, the system identifies the peers that are likely to store the index information about relevant objects using reference vectors. Thus the system is able to return accurate results by contacting a small fraction of the participating peers.

As a distributed hash table (DHT), a content addressable network (CAN) provides efficient routing and object location in a decentralized manner while offering fault tolerance and dynamic peer operations. However, as opposed to other DHTs that use a flat ID space, CAN uses a multi-dimensional logical space. DHTs usually require O(logN) routing information per peer and provide routing in O(logN) hops, where N is the number of peers in the system. In CAN, on the other hand, each peer keeps only constant amount of routing information and the routing takes O(dN/sup 1/d/) hops, where d is the dimensionality of the logical space. Hence the routing performance of CAN is worse than other DHTs especially when d is small. In this paper, we describe and evaluate several schemes for efficient routing in CAN by keeping additional routing information at the peers. Furthermore, due to the underlying multidimensional ID space, CAN is used by applications that require content-based mapping of data objects onto the ID space. Since uniform hashing is not used, such mappings introduce skewed object distributions among the peers. Thus we also describe load balancing schemes for CAN and investigate their efficiency.

Similarity search in textual databases and bioinformatics has received substantial attention in the past decade. Numerous filtration and indexing techniques have been proposed to reduce the curse of dimensionality. This paper proposes a novel approach to map the problem of whole- genome sequence similarity search into an approximate vector comparison in the well-established multidimensional vector space. We propose the application of the singular value decomposition (SVD) dimensionality reduction technique as a pre-processing filtration step to effectively reduce the search space and the running time of the search operation. Our empirical results on a prokaryote and a eukaryote DNA contig dataset, demonstrate effective filtration to prune non-relevant portions of the database with up to 2.3 times faster running time compared with q-gram approach. SVD filtration may easily be integrated as a pre-processing step for any of the well-known sequence search heuristics as BLAST, QUASAR and FastA. We analyze the precision of applying SVD filtration as a transformation-based dimensionality reduction technique, and finally discuss the imposed trade-offs.

Peer-to-peer systems are mainly used for object sharing although they can provide the infrastructure for many other applications. We extend the idea of object sharing to data sharing on a peer-to-peer system. We propose a method, which is based on the multidimensional CAN system, for efficiently evaluating range queries. The answers of the range queries are cached at the peers and are used to answer future range queries. The scalability and efficiency of our design is shown through simulation.

Current Web service discovery methods are based on centralized approaches where Web services are identified based on service functionality. Examples of service functionality include car rental, hotel booking and book selling. Since higher level Web services are increasingly composed in terms of lower level Web services, it is important that service discovery not only be based on service functionality but also be based on process behavior, i.e., how a service functionality is served. Furthermore, centralized approaches to service discovery suffer from problems such as high operational and maintenance cost, single point of failure, and scalability. Another issue that has not been considered in current Web service discovery paradigms is the issue of trust and quality of service of the service provider. We, therefore, propose a structured peer-to-peer framework for Web service discovery in which Web services are located based on both service functionality and process behavior. In addition, we integrate a scalable reputation model in this distributed peer-to-peer framework to rank Web services based on both trust and service quality.