scholarly journals P2P-Based Query Processing over Linked Data

2014 ◽  
pp. 339-368
Author(s):  
Marcel Karnstedt ◽  
Kai-Uwe Sattler ◽  
Manfred Hauswirth
Keyword(s):  
Query Processing ◽  
Linked Data

Scalable distributed indexing and query processing over Linked Data

2012 ◽  
Vol 10 ◽  
pp. 3-32 ◽  
Author(s):  
Marcel Karnstedt ◽  
Kai-Uwe Sattler ◽  
Manfred Hauswirth
Keyword(s):  
Query Processing ◽  
Linked Data ◽  
Distributed Indexing

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

2015 ◽  
Vol 30 (5) ◽  
pp. 545-563 ◽  
Author(s):  
Damla Oguz ◽  
Belgin Ergenc ◽  
Shaoyi Yin ◽  
Oguz Dikenelli ◽  
Abdelkader Hameurlain
Keyword(s):  
Query Processing ◽  
Linked Data ◽  
Optimization Methods ◽  
Qualitative Comparison ◽  
Source Selection ◽  
Qualitative Survey ◽  
Web Of Data ◽  
Clear Insight ◽  
Federated Query Processing ◽  
The Web

AbstractA 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.

scholarly journals Sparql query processing in relational databases

2018 ◽  
Vol 7 (3.3) ◽  
pp. 84
Author(s):  
Ju Ri Kim ◽  
Zhanfang Zhao ◽  
Sung Kook Han
Keyword(s):  
Query Processing ◽  
Linked Data ◽  
Relational Databases ◽  
Sparql Query ◽  
Conceptual Level ◽  
Mapping Procedure ◽  
New Approach ◽  
Data Interchange ◽  
Conventional Mapping ◽  
Er Model

Background/Objectives: The mapping RDB to RDF has become important to populate Linked Data more efficiently. This paper shows how to implement SPARQL endpoint in RDB using a conceptual level mapping approach.Methods/Statistical analysis: Many diverse approaches and related languages for mapping RDB to RDF have been proposed. The prominent achievements of mapping RDB to RDF are two standard draft Direct Mapping and R2RML proposed by W3C RDB2RDF Working Group. This paper analyzes these conventional mapping approaches and proposes a new approach based on schema mapping. The paper also presents SPARQL query processing in RDB.Findings: There are distinct differences between instance level mapping and conceptual level mapping for RDB2RDF. Data redundancy of instance level mapping causes many inevitable problems during mapping procedure. The conceptual level mapping can provide straightforward and efficient way. The ER model in RDB and RDF model in Linked Data have obvious similarity. The ER model describes entities and relationships, which is the conceptual schema of RDB. RDF model consists of three parts: subject, predicate and object, which is the standard model for data interchange on the Web. The entities in ER model and subjects in RDF model are all the things that can be anything in the real world. Both the relationships in ER model and predicates in RDF model describe the relations between things.Since RDB and RDF share the similar modeling approach at the schema level, it is reasonable that mapping approach should be based on RDB schema. This kind of conceptual level mapping also can provide efficient SPARQL query processing in RDB.Improvements/Applications: The paper realizes SPARQL query processing in RDB, which is based on conceptual level mapping. The query experiments show that it is a concise and efficient way to populate Linked Data.  

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