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 backwards compatibility. As a result, many small-scale data sharing tasks are more easily facilitated by non-database-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 considers the problem of schema mediation in a PDMS. Our first contribution is a flexible language for mediating between peer schemas, which extends known data integration formalisms to our more complex architecture. We precisely characterize the complexity of query answering for our language. Next, we describe a reformulation algorithm for our language that generalizes both global-as-view and local-as-view query answering algorithms. Finally, we describe several methods for optimizing the reformulation algorithm, and an initial set of experiments studying its performance.Keywords data structures, distributed databases, query formulation, query languages, query processing, data integration tool, data management tool, peer data management system, query answering, complexity, query optimisation, query reformulation, schema design, schema mediation, semantic information, semantic mapping This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Pennsylvania's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.

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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 non-databaseoriented 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. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Pennsylvania's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it. Abstract-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 non-databaseoriented 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. Author(s)Alon

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 is highly inefficient in practice. We describe several techniques for optimizing the reformulation process in a PDMS and validate their effectiveness using real-life data sets. In particular, we develop techniques for pruning paths in the reformulation process and for minimizing the reformulated queries as they are created. In addition, we consider the effect of the strategy we use to search through the space of reformulations. Finally, we show that pre-computing semantic paths in a PDMS can greatly improve the efficiency of the reformulation process. Together, all of these techniques form a basis for scalable query reformulation in PDMS.To enable our optimizations, we developed practical algorithms, of independent interest, for checking containment and minimization of XML queries, and for composing XML mappings.

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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.

There has been recent interest in using relational database systems to store and query XML documents. Each of the techniques proposed in this context works by (a) creating tables for the purpose of storing XML documents (also called relational schema generation), (b) storing XML documents by shredding them into rows in the created tables, and (c) converting queries over XML documents into SQL queries over the created tables. Since relational schema generation is a physical database design issue -dependent on factors such as the nature of the data, the query workload and availability of schemas -there have been many techniques proposed for this purpose. Currently, each relational schema generation technique requires its own query processor to efficiently convert queries over XML documents into SQL queries over the created tables. In this paper, we present an efficient technique whereby the same query-processor can be used for all such relational schema generation techniques. This greatly simplifies the task of relational schema generation by eliminating the need to write a special-purpose query processor for each new solution to the problem. In addition, our proposed technique enables users to query seamlessly across relational data and XML documents. This provides users with unified access to both relational and XML data without them having to deal with separate databases.