MuSe: a multi-level storage scheme for big RDF data using MapReduce

Abstract

Resource Description Framework (RDF) model owing to its flexible structure is increasingly being used to represent Linked data. The rise in amount of Linked data and Knowledge graphs has resulted in an increase in the volume of RDF data. RDF is used to model metadata especially for social media domains where the data is linked. With the plethora of RDF data sources available on the Web, scalable RDF data management becomes a tedious task. In this paper, we present MuSe—an efficient distributed RDF storage scheme for storing and querying RDF data with Hadoop MapReduce. In MuSe, the Big RDF data is stored at two levels for answering the common triple patterns in SPARQL queries. MuSe considers the type of frequently occuring triple patterns and optimizes RDF storage to answer such triple patterns in minimum time. It accesses only the tables that are sufficient for answering a triple pattern instead of scanning the whole RDF dataset. The extensive experiments on two synthetic RDF datasets i.e. LUBM and WatDiv, show that MuSe outperforms the compared state-of-the art frameworks in terms of query execution time and scalability.

Data Lake Conceptualized Web Platform for Food Research Data Collection

“…Studies on the storage and processing of large datasets have been conducted in various fields. Examples include research on storing large-scale meteorological data [9], storing remotely sensed data that are produced in large quantities [10], using Hadoop for storing resource description framework models for linked data and knowledge graphs [11,12], and research on using Hadoop for processing medical data to predict chronic kidney diseases in the context of large-scale bioscience data [13]. Other studies have explored the advantages of Hadoop in storing and searching proteomic datasets [14], as well as storing largescale genomic data in FASTA/Q files [15].…”