Adaptive Integration of Distributed Semantic Web Data

Lynden, Steven; Kojima, Isao; Matono, Akiyoshi; Tanimura, Yusuke

doi:10.1007/978-3-642-12038-1_12

Cited by 16 publications

(13 citation statements)

References 12 publications

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“…However, the details are not given in their publication so it is not clear how ANAPSID estimates the triple patterns' execution times. ADERIS (Lynden et al, 2010(Lynden et al, , 2011) also uses list of predicates as metadata catalog and predicate-based selection for data source selection. It sends SPARQL SELECT queries with DISTINCT keyword to each endpoint to find out the unique predicates.…”

Section: Data Source Selection Methodsmentioning

confidence: 99%

Federated query processing on linked data: a qualitative survey and open challenges

Oğuz¹,

Ergenc²,

Yin³

et al. 2015

The Knowledge Engineering Review

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A large number of data providers publish and connect their structured data on the Web as linked data. Thus, the Web of data becomes a global data space. In this paper, we initially give an overview of query processing approaches used in this interlinked and distributed environment, and then focus on federated query processing on linked data. We provide a detailed and clear insight on data source selection, join methods and query optimization methods of existing query federation engines. Furthermore, we present a qualitative comparison of these engines and give a complementary comparison of the measured metrics of each engine with the idea of pointing out the major strengths of each one. Finally, we discuss the major challenges of federated query processing on linked data.

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Section: Data Source Selection Methodsmentioning

confidence: 99%

Federated query processing on linked data: a qualitative survey and open challenges

Oğuz¹,

Ergenc²,

Yin³

et al. 2015

The Knowledge Engineering Review

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“…ADERIS generates predicate tables for each predicate which cover the related subjects and objects of that predicate. The first version of ADERIS [9] joins two predicate tables as they become complete while the other predicate tables are being generated. In the second version, Lynden et al [10] propose an adaptive cost model to determine the join order.…”

Section: Related Workmentioning

confidence: 99%

“…For these reasons, we think that adaptive query optimization [7] is a need in this unpredictable environment. There are only two engines ANAPSID [8] and ADERIS [9,10] which consider adaptive query optimization for query federation. Acosta et al [8] propose a non-blocking join method based on symmetric hash join [11] and Xjoin [12] whereas Lynden et al [10] propose a cost model for dynamically changing the join order.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Join Operator for Federated Queries over Linked Data Endpoints

Oğuz

Yin

Hameurlain

et al. 2016

Advances in Databases and Information Systems

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OATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible. This is an author-deposited version published in : http://oatao.univ-toulouse.fr/ Eprints ID : 17165The contribution was presented at ADBIS 2016 :http://adbis2016.vsb.cz/ Abstract. Traditional static query optimization is not adequate for query federation over linked data endpoints due to unpredictable data arrival rates and missing statistics. In this paper, we propose an adaptive join operator for federated query processing which can change the join method during the execution. Our approach always begins with symmetric hash join in order to produce the first result tuple as soon as possible and changes the join method as bind join when it estimates that bind join is more efficient than symmetric hash join for the rest of the process. We compare our approach with symmetric hash join and bind join. Performance evaluation shows that our approach provides optimal response time and has the adaptation ability to the different data arrival rates.

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“…To achieve this, indices are kept describing the contents and capabilities of the different query endpoints. For instance, in [9], properties found in a dataset are extracted and stored, which is equivalent to SIM1. In semwiq [10], lists of classes and properties are kept; this leads to information loss compared to SIM2 and SIM3, as it is no longer known which classes occur in the domain or range of the found properties.…”

Section: Related Workmentioning

confidence: 99%

Mobile Querying of Online Semantic Web Data for Context-Aware Applications

Woensel

Casteleyn

Paret

et al. 2011

IEEE Internet Comput.

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Mobile devices are becoming increasingly multi-functional and personal, providing mobile applications with the necessary user information (e.g., preferences, personal calendar) to achieve personalization. At the same time, detection technologies (e.g., Radio Frequency IDentification, or RFID) allow mobile devices to detect nearby physical entities, and thus map the user's environment. By exploiting existing online data sources about these detected entities, mobile applications can further improve personalization by including knowledge on the mobile user's physical environment. Semantic Web sources are useful in this respect, as they are machine-readable and facilitate integration with other sources. SCOUT, developed by the authors, is a mobile application framework that supports the linking of physical entities to online semantic sources, and provides applications with an integrated, query-able view on these sources and the user's environment. In order to efficiently access this large set of distributed online semantic sources, a tailored data management approach has been developed.

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Adaptive Integration of Distributed Semantic Web Data

Cited by 16 publications

References 12 publications

Federated query processing on linked data: a qualitative survey and open challenges

Federated query processing on linked data: a qualitative survey and open challenges

Adaptive Join Operator for Federated Queries over Linked Data Endpoints

Mobile Querying of Online Semantic Web Data for Context-Aware Applications

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