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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.
