RDF Data Processing Research Articles

RDF Subgraph Matching by Means of Star Decomposition

<p>With the continuous development of the network, the scale of RDF data is becoming larger and larger. In the face of large-scale RDF data processing, the traditional database query method has been unable to meet the needs. Due to the limited characteristics of subgraph matching, most existing algorithms often have the phenomenon that many subgraphs are repeatedly traversed during the query process, resulting in a large number of intermediate result sets and low query efficiency. The core problem to be solved is how to efficiently match subgraphs. In order to improve the query efficiency of RDF subgraphs in massive RDF data graphs and solve the problem of repeated calculation of some graphs in the query process of RDF subgraphs, an RDF subgraph query algorithm based on star decomposition is proposed in this paper. The algorithm uses graph structure to decompose RDF subgraphs into stars and uses a custom node cost model to calculate the query order of the star subgraphs. By decomposing, the amount of communication among subgraphs is reduced, and the communication cost for query processing is lowered. Moreover, utilizing the query order for RDF subgraph matching can effectively reduce the generation of intermediate result sets and accelerate the efficiency of subgraph matching. On this basis, the performances of the proposed algorithm and several other widely used algorithms are compared and analyzed on two different datasets. Experiments show that the proposed algorithm has better advantages in database recreation, memory size, and execution efficiency. </p> <p> </p>

RDFAdaptor: Efficient ETL Plugins for RDF Data Process

Abstract Purpose The interdisciplinary nature and rapid development of the Semantic Web led to the mass publication of RDF data in a large number of widely accepted serialization formats, thus developing out the necessity for RDF data processing with specific purposes. The paper reports on an assessment of chief RDF data endpoint challenges and introduces the RDF Adaptor, a set of plugins for RDF data processing which covers the whole life-cycle with high efficiency. Design/methodology/approach The RDFAdaptor is designed based on the prominent ETL tool—Pentaho Data Integration—which provides a user-friendly and intuitive interface and allows connect to various data sources and formats, and reuses the Java framework RDF4J as middleware that realizes access to data repositories, SPARQL endpoints and all leading RDF database solutions with SPARQL 1.1 support. It can support effortless services with various configuration templates in multi-scenario applications, and help extend data process tasks in other services or tools to complement missing functions. Findings The proposed comprehensive RDF ETL solution—RDFAdaptor—provides an easy-to-use and intuitive interface, supports data integration and federation over multi-source heterogeneous repositories or endpoints, as well as manage linked data in hybrid storage mode. Research limitations The plugin set can support several application scenarios of RDF data process, but error detection/check and interaction with other graph repositories remain to be improved. Practical implications The plugin set can provide user interface and configuration templates which enable its usability in various applications of RDF data generation, multi-format data conversion, remote RDF data migration, and RDF graph update in semantic query process. Originality/value This is the first attempt to develop components instead of systems that can include extract, consolidate, and store RDF data on the basis of an ecologically mature data warehousing environment.

Evaluation Criteria for RDF Triplestores with an Application to Allegrograph

Since its launching as the standard language of the semantic web, the Resource Description Framework RDF has gained an enormous importance in many fields. This has led to the appearance of a variety of data systems to store and process RDF data. To help users identify the best suited RDF data stores for their needs, we establish a list of evaluation and comparison criteria of existing RDF management systems also called triplestores. This is the first work addressing such topic for such triplestores. The criteria list highlights various aspects and is not limited to special stores but covers all types of stores including among others relational, native, centralized, distributed and big data stores. Furthermore, this criteria list is established taking into account relevant issues in accordance with triplestores tasks with respect to the main issues of RDF data storage, RDF data processing, performance, distribution and ease of use. As a study case we consider an application of the evaluation criteria to the graph RDF triplestore AllegroGraph.

New Approach based on Model Driven Engineering for Processing Complex SPARQL Queries on Hive

Semantic web technologies are increasingly used in different domains. The core technology of the Semantic Web is the RDF standard. Today with the growth of RDF data it requires systems capable of handling these large volumes of data and responding to very complex requests at the join level, Several RDF data processing systems have been proposed, but are not dedicated to handling complex SPARQL queries. in this paper we present a new approach based on model driven engineering for processing complex SPARQL queries using one of the big data processing tools named Hive. We evaluate our system using three datasets from LUBM Benchmark. The results of this evaluation show the performance, and the scalability of our approach, also give very interesting results when it is compared with existing works.

A survey and experimental comparison of distributed SPARQL engines for very large RDF data

Distributed SPARQL engines promise to support very large RDF datasets by utilizing shared-nothing computer clusters. Some are based on distributed frameworks such as MapReduce; others implement proprietary distributed processing; and some rely on expensive preprocessing for data partitioning. These systems exhibit a variety of trade-offs that are not well-understood, due to the lack of any comprehensive quantitative and qualitative evaluation. In this paper, we present a survey of 22 state-of-the-art systems that cover the entire spectrum of distributed RDF data processing and categorize them by several characteristics. Then, we select 12 representative systems and perform extensive experimental evaluation with respect to preprocessing cost, query performance, scalability and workload adaptability, using a variety of synthetic and real large datasets with up to 4.3 billion triples. Our results provide valuable insights for practitioners to understand the trade-offs for their usage scenarios. Finally, we publish online our evaluation framework, including all datasets and workloads, for researchers to compare their novel systems against the existing ones.

Review of Large-Scale RDF Data Processing in MapReduce

Review of Large-Scale RDF Data Processing in MapReduce

An Indexing and Query Processing Approach of RDF Data Based on Prüfer Sequence

As a web of data,the Semantic Web is assembling and organizing web information.Therefore,Semantic Web applications face the challenge of storage and processing of RDF data at a larger and larger scale.An efficient indexing scheme may be one of the solutions.The sequence-based indexing can bring good query performance with mature sequence matching techniques while keeping reasonable space consumption.In this paper,we extend the Prufer sequence approach to support index and query processing on RDF data,and implement a prototype system called Prig.Performance comparisons with Sesame RDF framework on LUBM benchmark and SP2Bench benchmark are presented.The experimental results illustrate that our approach has a better performance on large-scale RDF data.

Deploying defeasible logic rule bases for the semantic web

Logic is currently the target of the majority of the upcoming efforts towards the realization of the Semantic Web vision, namely making the content of the Web accessible not only to humans, as it is today, but to machines as well. Defeasible reasoning, a rule-based approach to reasoning with incomplete and conflicting information, is a powerful tool in many Semantic Web applications. Despite its strong mathematical background, logic, in general, and defeasible logic, in particular, may overload the user with tons of additional complex semantic relationships among data and metadata of the Semantic Web. To this end, a comprehensible, visual representation of these semantic relationships (rules) would help users understand them and make more use of them. This paper presents VDR-DEVICE, a defeasible reasoning system, designed specifically for the Semantic Web environment. VDR-DEVICE is an integrated development environment for deploying and visualizing defeasible logic rule bases on top of RDF Schema ontologies. The system consists of a number of sub-components, which, though developed autonomously, are combined efficiently, forming a flexible framework. The system employs a defeasible reasoning system that supports direct importing and processing of RDF data and RDF Schema ontologies as well as a number of user-friendly rule base and ontology visualization modules.