Fixing the Inconsistencies in Fuzzy Spatiotemporal RDF Graph

Many industries prefer worldwide business operations due to the economic advantage of globalization on product design and development. These industries increasingly operate globalized multi-tier supply chains and deliver products and services all over the world. This global approach produces huge amounts of heterogeneous data residing at various business operations, and the integration of these data plays an important role. Integrating data from multiple heterogeneous sources need to deal with different data models, database schema, and query languages. This chapter presents a semantic web technology-based data integration framework that uses relational databases and XML data with the help of ontology. To model different source schemas, this chapter proposes a method based on the resource description framework (RDF) graph patterns and query rewriting techniques. The semantic translation between the source schema and RDF ontology is described using query and transformational language SPARQL.

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Extracting entity-specific substructures for RDF graph embeddings

Semantic Web ◽

10.3233/sw-190359 ◽

2019 ◽

Vol 10 (6) ◽

pp. 1087-1108

Author(s):

Muhammad Rizwan Saeed ◽

Charalampos Chelmis ◽

Viktor K. Prasanna

Keyword(s):

Graph Embeddings ◽

Rdf Graph

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Dynamic Partitioning Supporting Load Balancing for Distributed RDF Graph Stores

Symmetry ◽

10.3390/sym11070926 ◽

2019 ◽

Vol 11 (7) ◽

pp. 926

Author(s):

Kyoungsoo Bok ◽

Junwon Kim ◽

Jaesoo Yoo

Keyword(s):

Load Balancing ◽

Distributed Processing ◽

Data Partitioning ◽

Rdf Graph ◽

Dynamic Partitioning ◽

Usage Frequency ◽

Partitioning Methods ◽

Description Framework ◽

Rdf Graphs ◽

Distributed Server

Various resource description framework (RDF) partitioning methods have been studied for the efficient distributed processing of a large RDF graph. The RDF graph has symmetrical characteristics because subject and object can be used interchangeably if predicate is changed. This paper proposes a dynamic partitioning method of RDF graphs to support load balancing in distributed environments where data insertion and change continue to occur. The proposed method generates clusters and subclusters by considering the usage frequency of the RDF graph that are used by queries as the criteria to perform graph partitioning. It creates a cluster by grouping RDF subgraphs with higher usage frequency while creating a subcluster with lower usage frequency. These clusters and subclusters conduct load balancing by using the mean frequency of queries for the distributed server and conduct graph data partitioning by considering the size of the data stored in each distributed server. It also minimizes the number of edge-cuts connected to clusters and subclusters to minimize communication costs between servers. This solves the problem of data concentration to specific servers due to ongoing data changes and additions and allows efficient load balancing among servers. The performance results show that the proposed method significantly outperforms the existing partitioning methods in terms of query performance time in a distributed server.

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