Distributed SPARQL over Big RDF Data: A Comparative Analysis Using Presto and MapReduce

Abstract

The processing of large volumes of RDF data require an efficient storage and query processing engine that can scale well with the volume of data. The initial attempts to address this issue focused on optimizing native RDF stores as well as conventional relational databases management systems. But as the volume of RDF data grew to exponential proportions, the limitations of these systems became apparent and researchers began to focus on using big data analysis tools, most notably Hadoop, to process RDF data. Various studies and benchmarks that evaluate these tools for RDF data processing have been published. In the past two and half years, however, heavy users of big data systems, like Facebook, noted limitations with the query performance of these big data systems and began to develop new distributed query engines for big data that do not rely on map-reduce. Facebook's Presto is one such example. The results of the experiment show that Presto has a much higher performance than Hive can be used to process big RDF data. The thesis also proposes an architecture based on Presto, Presto-RDF, that can be used to process big RDF data.ii DEDICATION This thesis is dedicated my parents for their love and support and to my brother who initiated the idea of coming to the US for my master's study and who sponsored my education.iii ACKNOWLEDGMENTS

Analisis Data Kereta Api dan Stasiun pada Daerah Operasi VIII Surabaya Menggunakan SPARQL dengan Algoritma Betweenness Centrality

“…[9]. SPARQL memungkinkan untuk dijalankan pada semua format RDF, seperti RDF/XML, Turtle, N-triples, dan lain-lain [10]. SPARQL memiliki struktur sebagai seperti Gambar 1 berikut: Gambar 1 Struktur SPARQL PREFIX digunakan untuk mendeklarasikan awalan untuk mempersingkat penulisan alamat sehingga mempermudah penulisan query.…”

A Systematic Review of SQL-on-Hadoop by Using Compact Data Formats

“…It is worth mentioning that authors select Avro as a target binary data format and demonstrate its efficiency in both read and write operations. Mammo and Srividya (2015) propose a Presto-based architecture, Presto-RDF that can be used to store and process big RDF data and SPARQL to SQL compiler. The comparative analysis of the performance of Presto (distributed SQL query engine) in processing big RDF data against Apache Hive has been done.…”