Semantic Loss

Dynamic environments are descentralized systems that provide users with querying capabilities over a set of heterogeneous, distributed and autonomous data sources. Data Integration Systems, Peer Data Management Systems (PDMS) and Dataspaces are examples of such systems. They are composed by data sources (peers) that belong to a specific domain and are linked to each other by mappings (correspondences). Nonetheless, a challenge inherent to dynamic environments is to analyze the semantic loss during query reformulation. A semantic loss may occur when a query is reformulated from a peer to another in the system. To minimize the consequences of this problem, we propose the use of information quality criteria to help the semantic loss analysis. The semantic loss analysis is a step executed to verify the query routing possibilities.

Dynamic environments are descentralized systems that provide users with querying capabilities over a set of heterogeneous, distributed and autonomous data sources. Data Integration Systems, Peer Data Management Systems (PDMS) and Dataspaces are examples of such systems. They are composed by data sources (peers) that belong to a specific domain and are linked to each other by mappings (correspondences). Nonetheless, a challenge inherent to dynamic environments is to analyze the semantic loss during query reformulation. A semantic loss may occur when a query is reformulated from a peer to another in the system. To minimize the consequences of this problem, we propose the use of information quality criteria to help the semantic loss analysis. The semantic loss analysis is a step executed to verify the query routing possibilities.

In distributed data environments, peers (data sources) are connected with each other through a set of semantic correspondences in such a way that peers directly connected are called semantic neighbours. Queries are submitted considering partial information provided by a peer schema and may be answered by other neighbour peers. From the query submission peer, the original query is successively rewritten into queries over the peers, according to the correspondences between the original peer and the target ones. In this process, some of the original query terms may be lost while other ones may be added, leading to a semantic loss of the original query. In this work, we argue that it is essential to try preserving the original query semantics if we wish to hold what the users defined as important at query submission time. With this in mind, we propose an approach to preserve the original query semantics in query routing processes. Furthermore, we present a metric for assessing the relevance of neighbour peers according to an estimated query semantic value obtained at each query reformulation. In this paper, we present the developed approach and some experimental results we have accomplished.

Peer data management systems (Pdms) consist of autonomous peers with mappings to other peers. Queries submitted at a peer are answered with data residing at that peer and by data that is reached along paths of mappings through the network of peers. System P is a full-fledged relational Pdms featuring peers distributed over the network, relational schemata and multiple local sources at each peer, LaV and GaV mappings between peer schemata, fully localized query planning and execution. In addition, it serves as a test bed with automated deployment of schemata and data across the network and an independent monitor peer to perform scalability measurements. We have observed that the number of mappings and peers that must be traversed to obtain a complete query answer reaches the point of intractability for already relatively small Pdms (50+ peers). We present and evaluate a set of decentral strategies that guide peers in their decision along which further mappings the query should be sent. Thereby we compromise on the completeness of the query result but gain feasibility of query answering in large Pdms.

Query answering has been addressed as a key issue in distributed environments such as peer data management systems (PDMS). An important step in this process regards query routing, i.e., how to find peers (data sources) that are most likely to provide results according to the submitted query. In this process, queries are reformulated and propagated through network peers using the semantic mappings between neighbour peers' schemas. The successive processes of query reformulation may result in a semantic loss of the original query, i.e., concepts which belong to the original query may be lost when reformulated queries are produced. Thereby, this work proposes the use of semantic measures obtained from information quality (IQ) criteria aiming to avoid or minimise this semantic loss. Moreover, it combines semantic information and IQ, by presenting a model, which is instantiated to illustrate how this proposal produces the semantic measures and enhances query routing processes.

Query answering has been addressed as a key issue in dynamic distributed environments such as Peer Data Management Systems (PDMS). An important step in this process regards query routing, i.e., how to find peers (data sources) that are most likely to provide matching results according to the semantics of a submitted query. To help matters, we argue that semantic information like contextual information, combined with Information Quality (IQ) provided by IQ measures, may be employed together to enrich query routing processes. In this work, we propose an instantiation of a metamodel which combines both concepts as a means to produce semantic knowledge to be used in query routing processes. We also present an example of such instantiation.

Query answering has been addressed as a key issue in distributed environments such as peer data management systems (PDMS). An important step in this process regards query routing, i.e., how to find peers (data sources) that are most likely to provide results according to the submitted query. In this process, queries are reformulated and propagated through network peers using the semantic mappings between neighbour peers' schemas. The successive processes of query reformulation may result in a semantic loss of the original query, i.e., concepts which belong to the original query may be lost when reformulated queries are produced. Thereby, this work proposes the use of semantic measures obtained from information quality (IQ) criteria aiming to avoid or minimise this semantic loss. Moreover, it combines semantic information and IQ, by presenting a model, which is instantiated to illustrate how this proposal produces the semantic measures and enhances query routing processes.

Query answering has been addressed as a key issue in distributed environments such as peer data management systems (PDMS). An important step in this process regards query routing, i.e., how to find peers (data sources) that are most likely to provide results according to the submitted query. In this process, queries are reformulated and propagated through network peers using the semantic mappings between neighbour peers' schemas. The successive processes of query reformulation may result in a semantic loss of the original query, i.e., concepts which belong to the original query may be lost when reformulated queries are produced. Thereby, this work proposes the use of semantic measures obtained from information quality (IQ) criteria aiming to avoid or minimise this semantic loss. Moreover, it combines semantic information and IQ, by presenting a model, which is instantiated to illustrate how this proposal produces the semantic measures and enhances query routing processes.

Abstract. Data management solutions in distributed environments have been continuously evolving during the last years to answer users ’ needs and face new technology challenges. To help matters, ontologies have been used as a support for the techniques of managing data. For instance, ontologies may be used to describe the semantics of data at different sources, helping to overcome problems of semantic interoperability and data heterogeneity, and thus assisting schema integration and query answering over the distributed data sources. The goal of this paper is to highlight the use of ontologies in order to enhance data management issues in distributed environments. To this end, we describe a set of ongoing works which have been developed in our research.

In distributed data environments, peers (data sources) are connected with each other through a set of semantic correspondences in such a way that peers directly connected are called semantic neighbours. Queries are submitted considering partial information provided by a peer schema and may be answered by other neighbour peers. From the query submission peer, the original query is successively rewritten into queries over the peers, according to the correspondences between the original peer and the target ones. In this process, some of the original query terms may be lost while other ones may be added, leading to a semantic loss of the original query. In this work, we argue that it is essential to try preserving the original query semantics if we wish to hold what the users defined as important at query submission time. With this in mind, we propose an approach to preserve the original query semantics in query routing processes. Furthermore, we present a metric for assessing the relevance of neighbour peers according to an estimated query semantic value obtained at each query reformulation. In this paper, we present the developed approach and some experimental results we have accomplished.

Data management solutions in distributed environments have been continuously evolving during the last years to answer users’ needs and face new technology challenges. To help matters, ontologies have been used as a support for the techniques of managing data. For instance, ontologies may be used to describe the semantics of data at different sources, helping to overcome problems of semantic interoperability and data heterogeneity, and thus assisting schema integration and query answering over the distributed data sources. The goal of this paper is to highlight the use of ontologies in order to enhance data management issues in distributed environments. To this end, we describe a set of ongoing works which have been developed in our research.

Query answering has been addressed as a key issue in distributed environments such as peer data management systems (PDMS). An important step in this process regards query routing, i.e., how to find peers (data sources) that are most likely to provide results according to the submitted query. In this process, queries are reformulated and propagated through network peers using the semantic mappings between neighbour peers' schemas. The successive processes of query reformulation may result in a semantic loss of the original query, i.e., concepts which belong to the original query may be lost when reformulated queries are produced. Thereby, this work proposes the use of semantic measures obtained from information quality (IQ) criteria aiming to avoid or minimise this semantic loss. Moreover, it combines semantic information and IQ, by presenting a model, which is instantiated to illustrate how this proposal produces the semantic measures and enhances query routing processes.

Data management solutions in distributed environments have been continuously evolving during the last years to answer users' needs and face new technology challenges. To help matters, ontologies have been used as a support for the techniques of managing data. For instance, ontologies may be used to describe the semantics of data at different sources, helping to overcome problems of semantic interoperability and data heterogeneity, and thus assisting schema integration and query answering over the distributed data sources. The goal of this paper is to highlight the use of ontologies in order to enhance data management issues in distributed environments. To this end, we describe a set of ongoing works which have been developed in our research.

Peer Data Management Systems (PDMS) are a novel, useful, but challenging paradigm for distributed data management and query processing. Conventional integrated information systems have a hierarchical structure with an integration component that manages a global schema and distributes queries against this schema to the underlying data sources. PDMS are a natural extension to this architecture by allowing each participating system (peer) to act both as a data source and as an integrator. Peers are interconnected by schema mappings, which guide the rewriting of queries between the heterogeneous schemas, and thus form a P2P (peer-to-peer)-like network. Despite several years of research, the development of efficient PDMS still holds many challenges. In this article we first survey the state of the art on peer data management: We classify PDMS by characteristics concerning their system model, their semantics, their query planning schemes, and their maintenance. Then we systematically examine open research directions in each of those areas. In particular, we observe that research results from both the domain of P2P systems and of conventional distributed data management can have an impact on the development of PDMS.

Peer Data Management Systems (PDMS) are a novel, useful, but challenging paradigm for distributed data management and query processing. Conventional integrated information systems have a hierarchical structure with an integration component that manages a global schema and distributes queries against this schema to the underlying data sources. PDMS are a natural extension to this architecture by allowing each participating system (peer) to act both as a data source and as an integrator. Peers are interconnected by schema mappings, which guide the rewriting of queries between the heterogeneous schemas, and thus form a P2P (peer-to-peer)-like network. Despite several years of research, the development of efficient PDMS still holds many challenges. In this article we first survey the state of the art on peer data management: We classify PDMS by characteristics concerning their system model, their semantics, their query planning schemes, and their maintenance. Then we systematically examine open research directions in each of those areas. In particular, we observe that research results from both the domain of P2P systems and of conventional distributed data management can have an impact on the development of PDMS.

Query answering has been addressed as a key issue in distributed environments such as peer data management systems (PDMS). An important step in this process regards query routing, i.e., how to find peers (data sources) that are most likely to provide results according to the submitted query. In this process, queries are reformulated and propagated through network peers using the semantic mappings between neighbour peers' schemas. The successive processes of query reformulation may result in a semantic loss of the original query, i.e., concepts which belong to the original query may be lost when reformulated queries are produced. Thereby, this work proposes the use of semantic measures obtained from information quality (IQ) criteria aiming to avoid or minimise this semantic loss. Moreover, it combines semantic information and IQ, by presenting a model, which is instantiated to illustrate how this proposal produces the semantic measures and enhances query routing processes.

Data management solutions in distributed environments have been continuously evolving during the last years to answer users' needs and face new technology challenges. To help matters, ontologies have been used as a support for the techniques of managing data. For instance, ontologies may be used to describe the semantics of data at different sources, helping to overcome problems of semantic interoperability and data heterogeneity, and thus assisting schema integration and query answering over the distributed data sources. The goal of this paper is to highlight the use of ontologies in order to enhance data management issues in distributed environments. To this end, we describe a set of ongoing works which have been developed in our research.

SPARQL is today the standard access language for Semantic Web data. In the recent years XML databases have also acquired industrial importance due to the widespread applicability of XML in the Web. In this paper we present a framework that bridges the heterogeneity gap and creates an interoperable environment where SPARQL queries are used to access XML databases. Our approach assumes that fairly generic mappings between ontology constructs and XML Schema constructs have been automatically derived or manually specified. The mappings are used to automatically translate SPARQL queries to semantically equivalent XQuery queries which are used to access the XML databases. We present the algorithms and the implementation of SPARQL2XQuery framework, which is used for answering SPARQL queries over XML databases.

Dynamic distributed environments are decentralized systems that provide users with
querying capabilities over a set of heterogeneous, distributed and autonomous data
sources (peers). Data Integration Systems, Peer Data Management Systems (PDMS)
and Dataspaces are examples of such systems. They are composed by peers that
belong to a specific domain and are linked to each other by correspondences
(semantic connections). Nonetheless, a challenge inherent to dynamic distributed
environments is the query reformulation process between a pair of peers. When a user
poses a query at a peer, in order to acquire more information, the query should be
reformulated in accordance with the neighbor peers schema. In this process some
problems as semantic loss and query degradation can arise.
The semantic loss and query degradation are problems related to the loss of
semantic concepts during query reformulation. In the other hand, in such a semantic
environment instead of a semantic loss the query can have a semantic enrichment by
aggregating semantic related concepts during reformulation. In this sense, the user’s
query can be enriched and semantically richer results can be delivered.
Information Quality criteria has been used in some works to evaluate the level
of quality of the distributed dynamic environment’s elements such as, peers, data and
query answer. These criteria are dynamic measures provided by the system and serve
as scores that can be constantly evaluated to get the actual level of quality.
In this work we present four Information Quality criteria that measure the loss
and enrichment of semantic concepts during query reformulation among peers. We
present an example of our analysis and the algorithms that implement the evaluation
of the presented criteria. We also give our definitions to the semantic loss and query
degradation problems. Finally, we present the experimentation we have done with the
SPEED PDMS and the obtained results.

Query answering has been addressed as a key issue in distributed
environments such as Peer Data Management Systems (PDMS).
An important step in this process regards query routing, i.e., how
to find peers that are most likely to provide matching results
according to the semantics of a submitted query. To help matters,
we argue that semantic information obtained from Information
Quality (IQ) measurements can enrich query routing processes.
However, dealing with IQ entails a complex development activity
because several tasks (e.g., IQ criteria assessment) must be
performed. In this work, we propose a model which combines
semantic information concepts with IQ ones as a way to produce
semantic knowledge to be used in query routing processes.

Peer Data Management Systems (Pdms) are a novel, useful, but challenging paradigm for distributed data management and query processing. Conventional integrated information systems have a hierarchical structure with an integration component that manages a global schema and distributes queries against this schema to the underlying data sources. Pdms are a natural extension to this architecture by allowing each participating system (peer) to act both as a data source and as an integrator. Peers are interconnected by schema mappings, which guide the rewriting of queries between the heterogeneous schemas, and thus form a P2P (peer-to-peer)-like network.Despite several years of research, the development of efficient Pdms still holds many challenges. In this article we first survey the state of the art on peer data management: We classify Pdms by characteristics concerning their system model, their semantics, their query planning schemes, and their maintenance. Then we systematically examine open research directions in each of those areas. In particular, we observe that research results from both the domain of P2P systems and of conventional distributed data management can have an impact on the development of Pdms.

Dynamic environments are descentralized systems thatprovide users with querying capabilities over a set of heterogeneous,distributed and autonomous data sources. Data Integration Systems,Peer Data Management Systems (PDMS) and Dataspaces are examples of such systems. They are composed by data sources (peers) that belongto a specific domain and are linked to each other by mappings (corre-spondences). Nonetheless, a challenge inherent to dynamic environ-ments is to analyze the semantic loss during query reformulation. Asemantic loss may occur when a query is reformulated from a peer to another in the system.

To minimize the consequences of this problem,we propose the use of information quality criteria to help the semanticloss analysis. The semantic loss analysis is a step executed to verify thequery routing possibilities.

SPARQL is today the standard access language for Semantic Web data. In the recent years XML databases have also acquired industrial importance due to the widespread applicability of XML in the Web. In this paper we present a framework that bridges the heterogeneity gap and creates an interoperable environment where SPARQL queries are used to access XML databases. Our approach assumes that fairly generic mappings between ontology constructs and XML Schema constructs have been automatically derived or manually specified. The mappings are used to automatically translate SPARQL queries to semantically equivalent XQuery queries which are used to access the XML databases. We present the algorithms and the implementation of SPARQL2XQuery framework, which is used for answering SPARQL queries over XML databases.