Abstract | The peer-to-peer computing paradigm is an intriguing alternative to Google-style search
engines for querying and ranking Web content. In a network with many thousands or
millions of peers the storage and access load requirements per peer are much lighter
than for a centralized Google-like server farm; thus more powerful techniques from information
retrieval, statistical learning, computational linguistics, and ontological reasoning
can be employed on each peer¢s local search engine for boosting the quality
of search results. In addition, peers can dynamically collaborate on advanced and particularly
difficult queries. Moroever, a peer-to-peer setting is ideally suited to capture
local user behavior, like query logs and click streams, and disseminate and aggregate
this information in the network, at the discretion of the corresponding user, in order to
incorporate richer cognitive models.
This paper gives an overview of ongoing work in the EU Integrated Project DELIS
that aims to develop foundations for a peer-to-peer search engine with Google-or-better
scale, functionality, and quality, which will operate in a completely decentralized and
self-organizing manner. The paper presents the architecture of such a system and the
Minerva prototype testbed, and it discusses various core pieces of the approach: efficient
execution of top-k ranking queries, strategies for query routing when a search request
needs to be forwarded to other peers, maintaining a self-organizing semantic overlay
network, and exploiting and coping with user and community behavior. |