Distributed computer architectures labeled “peer-to-peer ” are designed for the sharing of computer resources (content, storage, CPU cycles) by direct exchange, rather than requiring the intermediation or support of a centralized server or authority. Peer-to-peer architectures are characterized by their ability to adapt to failures and

Semantic mappings between data sources play a key role in several data sharing architectures. Mappings provide the relationships between data stored in different sources, and therefore enable answering queries that require data from other nodes in a data sharing network. Composing mappings is one of the core problems that lies at the heart of several optimization methods in data sharing networks, such as caching frequently traversed paths and redundancy analysis.

Peer data management systems (PDMS) offer a flexible architecture for decentralized data sharing. In a PDMS, every peer is associated with a schema that represents the peer's domain of interest, and semantic relationships between peers are provided locally between pairs (or small sets) of peers. By traversing semantic paths of mappings, a query over one peer can obtain relevant data from any reachable peer in the network. Semantic paths are traversed by reformulating queries at a peer into queries on its neighbors. Naively following semantic paths...

Abstract. The paper addresses the problem of reasoning with multiple ontologies interrelated with semantic mappings. This problem is becoming more and more relevant due to the necessity of building a scalable ontological reasoning tools for the Semantic Web. In contrast to the so called global approach, in which reasoning with multiple semantically related ontologies is performed in a global knowledge base that encodes both ontologies and semantic mappings, we propose a distributed reasoning approach in which reasoning is the result of combination via semantic mappings of local reasonings chunks performed in single ontologies. The paper presents a tableau-based distributed reasoning procedure which is sound and complete w.r.t. Distributed Description Logics, the formal framework used to represent multiple semantically connected ontologies. The paper also describes the design and implementation principles of a distributed reasoning system, called DRAGO (Distributed Reasoning Architecture for a Galaxy of Ontology), that implements such distributed decision procedure. 1

A major problem in today's information-driven world is that sharing heterogeneous, semantically rich data is incredibly difficult. Piazza is a peer data management system that enables sharing heterogeneous data in a distributed and scalable way. Piazza assumes the participants to be interested in sharing data, and willing to define pairwise mappings between their schemas. Then, users formulate queries over their preferred schema, and a query answering system expands recursively any mappings relevant to the query, retrieving data from other peers. In this paper, we provide a brief overview of the Piazza project including our work on developing mapping languages and query reformulation algorithms, assisting the users in defining mappings, indexing, and enforcing access control over shared data.

Intuitively, data management and data integration tools should be well-suited for exchanging information in a semantically meaningful way. Unfortunately, they suffer from two significant problems: they typically require a comprehensive schema design before they can be used to store or share information, and they are difficult to extend because schema evolution is heavyweight and may break backward compatibility. As a result, many small-scale data sharing tasks are more easily facilitated by nondatabase -oriented tools that have little support for semantics. The goal of the peer data management system (PDMS) is to address this need: we propose the use of a decentralized, easily extensible data management architecture in which any user can contribute new data, schema information, or even mappings between other peers' schemas. PDMSs represent a natural step beyond data integration systems, replacing their single logical schema with an interlinked collection of semantic mappings between peers' individual schemas. This paper describes several aspects of the Piazza PDMS, including the schema mediation formalism, query answering and optimization algorithms, and the relevance of PDMSs to the Semantic Web.

In this paper, we study query evaluation on Active XML documents (AXML for short), a new generation of XML documents that has recently gained popularity. AXML documents are XML documents whose content is given partly extensionally, by explicit data elements, and partly intensionally, by embedded calls to Web services, which can be invoked to generate data. A major

This paper describes a novel approach for obtaining semantic interoperability among data sources in a bottom-up, semi-automatic manner without relying on pre-existing, global semantic models. We assume that large amounts of data exist that have been organized and annotated according to local schemas. Seeing semantics as a form of agreement, our approach enables the participating data sources to incrementally develop global agreement in an evolutionary and completely decentralized process that solely relies on pair-wise, local interactions: Participants provide translations between schemas they are interested in and can learn about other translations by routing queries (gossiping). In previous work we relied on the realistic assumption that such translations would be provided manually only. In contrast to that, we assume in this paper that only some translations exist and generate random translations for reaching overall sematic agreement automatically. To support the participants in assessing the semantic quality of the achieved agreements we develop a formal framework that takes into account both syntactic and semantic criteria. The assessment process is incremental and the quality ratings are adjusted along with the operation of the system. Ultimately, this process results in global agreement, i.e., the semantics that all participants understand.

When joining information provider peers to a peer-to-peer network, an arbitrary distribution is sub-optimal. In fact, clustering peers by their characteristics, enhances search and integration significantly. Currently super-peer networks, such as the Edutella network, provide no sophisticated means for such a "semantic clustering" of peers. We introduce the concept of semantic overlay clusters (SOC) for super-peer networks enabling a controlled distribution of peers to clusters. In contrast to the recently announced semantic overlay network approach designed for flat, pure peer-to-peer topologies and for limited meta data sets, such as simple filenames, we allow a clustering of complex heterogeneous schemes known from relational databases and use advantages of super-peer networks, such as efficient search and broadcast of messages. Our approach is based on predefined policies defined by human experts. Based on such policies a fully decentralized broadcast- and matching approach distributes the peers automatically to super-peers. Thus we are able to automatize the integration of information sources in super-peer networks and reduce flooding of the network with messages.

The loose and dynamic association between peers in a peer-to-peer integration has meant that, to date, peer-to-peer systems have been based on exchange of files identified with a very limited set of attributes, and no schema is used to describe the data within those files. This paper