Distributed Semantic Web Data Management in HBase and MySQL Cluster

Various computing and data resources on the Web are being enhanced with machine-interpretable semantic descriptions to facilitate better search, discovery and integration. This interconnected metadata constitutes the Semantic Web, whose volume can potentially grow the scale of the Web. Efficient management of Semantic Web data, expressed using the W3C's Resource Description Framework (RDF), is crucial for supporting new data-intensive, semantics-enabled applications. In this work, we study and compare two approaches to distributed RDF data management based on emerging cloud computing technologies and traditional relational database clustering technologies. In particular, we design distributed RDF data storage and querying schemes for HBase and MySQL Cluster and conduct an empirical comparison of these approaches on a cluster of commodity machines using datasets and queries from the Third Provenance Challenge and Lehigh University Benchmark. Our study reveals interesting patterns in query evaluation, shows that our algorithms are promising, and suggests that cloud computing has a great potential for scalable Semantic Web data management.Comment: In Proc. of the 4th IEEE International Conference on Cloud Computing (CLOUD'11

Distributed Semantic Web data management in HBase and MySQL cluster

Various computing and data resources on the Web are being enhanced with machine-interpretable semantic descriptions to facilitate better search, discovery and integration. This interconnected metadata constitutes the Semantic Web, whose volume can potentially grow the scale of the Web. Efficient management of Semantic Web data, expressed using the W3C\u27s Resource Description Framework (RDF), is crucial for supporting new data-intensive, semantics-enabled applications. In this work, we study and compare two approaches to distributed RDF data management based on emerging cloud computing technologies and traditional relational database clustering technologies. In particular, we design distributed RDF data storage and querying schemes for HBase and MySQL Cluster and conduct an empirical comparison of these approaches on a cluster of commodity machines using datasets and queries from the Third Provenance Challenge and Lehigh University Benchmark. Our study reveals interesting patterns in query evaluation, shows that our algorithms are promising, and suggests that cloud computing has a great potential for scalable Semantic Web data management

Performance Comparison of CRUD Operations in IoT based Big Data Computing

Nowadays, due to the development of mobile devices, the kinds of data that are generated are becoming diverse, and the amount is becoming huge. The vast amount of data generated in this way is called big data. Big data must be processed in a different way than existing data processing methods. Representative methods of big data processing are RDBMS (Relational Database System) and NoSQL method. We compare NoSQL and RDBMS, which are representative database systems. In this paper, we use MySQL query and MongoDB query to compare RDBMS and NoSQL. We gradually compare the performance of CRUD operations in MySQL and MongoDB by increasing the amount of data. MongoDB sets index and compares it again.  Through result of these operations is to choose a database system that fits the situation.  This makes it possible to design and analyse big data more efficiently.

Benchmarking Bottom-Up and Top-Down Strategies to Sparql-To-Sql Query Translation

Many researchers have proposed using conventional relational databases to store and query large Semantic Web datasets. The most complex component of this approach is SPARQL-to-SQL query translation. Existing algorithms perform this translation using either bottom-up or top-down strategy and result in semantically equivalent but syntactically different relational queries. Do relational query optimizers always produce identical query execution plans for semantically equivalent bottom-up and top-down queries? Which of the two strategies yields faster SQL queries? To address these questions, this work studies bottom-up and top-down translations of SPARQL queries with nested optional graph patterns. This work presents: (1) A basic graph pattern translation algorithm that yields flat SQL queries, (2) A bottom-up nested optional graph pattern translation algorithm, (3) A top-down nested optional graph pattern translation algorithm, and (4) A performance study featuring SPARQL queries with nested optional graph patterns over RDF databases created in Oracle, DB2, and PostgreSQL