Peer Schemas Research Articles

Mapping Discovery Over Revealing Schemas

In a world of wide-scale information sharing, data are described in different formats, i.e. data structures, values and schemas. Querying such sources entails techniques that can bridge the data formats. Some existing techniques deal with schema mapping and view complementary aspects of the problem. Important ones, consider producing all the possible mappings for a pair of schemas, insinuating accompanying semantics in the mappings and adapting correct mappings as schemas evolve. In this work, we consider the problem of discovering mappings as schemas of autonomous sources are gradually revealed. Using as an example setting an overlay of peer databases, we present a schema mapping solution that discovers correct mappings as peer schemas are gradually revealed to remote peers. Mapping discovery is schema-centric and incorporates new semantics as they are unveiled. Mapping experience is reused and possible mappings are ranked so that the best choice is presented to the user. The experimental study confirms the suitability of the proposed solution to dynamic settings of heterogeneous sources.

Effects of Children’s Negative Peer Schema on the Perception Bias of Peer Relationships

Effects of Children’s Negative Peer Schema on the Perception Bias of Peer Relationships

Query translation between sources in peer-to-peer database sharing systems

This paper presents a query translation mechanism between heterogeneous peers in peer-to-peer database sharing systems (PDSSs). A PDSS combines a database management system with P2P functionalities. In a PDSS, each peer chooses its own data model and schema, and maintains data independently without any global coordinator. One of the problems in such a system is translating queries between peers taking into account both the schema and data heterogeneity. Query translation is the problem of rewriting a query posed in terms of one peer schema to a query in terms of another peer schema. The proposed approach uses of user-provided syntax and data level mappings that each peer constructs and shares with other peers. Based on the mapping information, our algorithm applies translation rules to translate queries. The translation mechanism is evaluated on a prototype peer-to-peer network to demonstrate the viability of the approach.

Schema mapping and query translation in heterogeneous P2P XML databases

Peers in a peer-to-peer data management system often have heterogeneous schemas and no mediated global schema. To translate queries across peers, we assume each peer provides correspondences between its schema and a small number of other peer schemas. We focus on query reformulation in the presence of heterogeneous XML schemas, including data---metadata conflicts. We develop an algorithm for inferring precise mapping rules from informal schema correspondences. We define the semantics of query answering in this setting and develop query translation algorithm. Our translation handles an expressive fragment of XQuery and works both along and against the direction of mapping rules. We describe the HePToX heterogeneous P2P XML data management system which incorporates our results. We report the results of extensive experiments on HePToX on both synthetic and real datasets. We demonstrate our system utility and scalability on different P2P distributions.

Structured Query Translation in Peer to Peer Database Sharing Systems

This paper presents a query translation mechanism between heterogeneous peers in Peer to Peer Database Sharing Systems (PDSSs). A PDSS combines a database management system with P2P functionalities. The local databases on peers are called peer databases. In a PDSS, each peer chooses its own data model and schema and maintains data independently without any global coordinator. One of the problems in such a system is translating queries between peers, taking into account both the schema and data heterogeneity. Query translation is the problem of rewriting a query posed in terms of one peer schema to a query in terms of another peer schema. This paper proposes a query translation mechanism between peers where peers are acquainted in data sharing systems through data-level mappings for sharing data.

Inconsistency tolerance in P2P data integration: An epistemic logic approach

We study peer-to-peer ( P 2 P ) data integration, where each peer models an autonomous system that exports data in terms of its own schema, and data interoperation is achieved by means of mappings among the peer schemas, rather than through a unique global schema. We propose a multi-modal epistemic logical formalization based on the idea that each peer is conceived as a rational agent that exchanges knowledge/belief with other peers, thus nicely modeling the modular structure of the system. We then address the issue of dealing with possible inconsistencies, and distinguish between two types of inconsistencies, called local and P2P, respectively. We define a nonmonotonic extension of our logic that is able to reason on the beliefs of peers under both local and P2P inconsistency tolerance. Tolerance to local inconsistency essentially means that the presence of inconsistency within one peer does not affect the consistency of the whole system. Tolerance to P2P inconsistency means being able to resolve inconsistencies arising from the interaction between peers. We study query answering in the new nonmonotonic logic, with the main goal of establishing its decidability and its computational complexity. Indeed, we show that, under reasonable assumptions on peer schemas, query answering is decidable, and is coNP-complete with respect to data complexity, i.e., the size of the data stored at the peers.

Schema mediation for large-scale semantic data sharing

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 common and 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 large-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 that 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. Then we describe several methods for optimizing the reformulation algorithm and an initial set of experiments studying its performance. Finally, we define and consider several global problems in managing semantic mappings in a PDMS.

Querying distributed data through distributed ontologies: a simple but scalable approach

This framework for peer-to-peer data-sharing systems allows efficient query answering over a network of semantically related peers. A simple class-based language appropriate for practical applications defines peer schemas as hierarchies of atomic classes and mappings as inclusions of logical combinations of atomic classes.