Optimasi SPARQL Query Menggunakan Graph Database Dengan Model Labeled Property Graph

Web semantik menyediakan kerangka kerja umum yang memungkinkan datanya dibagikan dan digunakan ulang secara lintas aplikasi. Model data RDF sendiri sudah digunakan untuk berbagai macam aplikasi web semantik yang berguna untuk mesin pencarian publik, rekayasa pengetahuan, penyimpanan data hasil penelitian, dan proses-proses bisnis aplikasi lainnya. Karena data yang disimpan sangat penting dan dituntut ketahanannya, data RDF yang ada di internet saat ini ukurannya sudah sangat besar dan akan semakin membesar. Hal ini menyebabkan proses query data pada file RDF memakan waktu yang cukup lama. Pada tugas akhir ini, permasalahan tersebut akan ditangani dengan mengusulkan metode optimasi SPARQL query dengan menggunakan database graf yang menerapkan model labeled property graph. Berdasarkan model data RDF yang sudah ada, labeled property graph dapat mengurangi jumlah node yang dihasilkan dari file RDF. Oleh karena itu metode ini diharapkan dapat meningkatkan kecepatan dalam melakukan proses traverse pada data graf. Pada tugas akhir ini, penulis akan membandingkan performa model Labeled Property Graph dengan Triple Store dalam menghadapi SPARQL query. Pengujian yang dilakukan menunjukkan bahwa metode ini dapat memberikan hasil running time yang lebih singkat dengan running time pada model Labeled Property Graph yang jauh lebih cepat jika dibandingkan dengan model Triple Store saat menghadapi SPARQL query.= ========================================================================================================= The Semantik Web provides a common framework that allows data to be shared and reused across applications. The RDF data model itself already in use for variety semantik web applications which is useful to public search engines, is also used for knowledge management, and another business process. Because of the data stored is very important and demanded endurance, the RDF data that exists on internet today is already very large and will be larger.This causes the process time of querying dataset in RDF files become a considerable issue. In this research, mentioned issues will be handled by proposing SPARQL query optimization method using graph database with Labeled Property Graph model. Based on the existing RDF data model, the Labeled Property Graph model can reduce the number of nodes generated from RDF file. Therefore this method is expected to increase the speed in conducting traverse process on graph data. In this research, I will compare the performace of Labeled Property Graph model with Triple Store model in facing certain SPARQL query. Test conducted show that this method can provide faster running time with Labeled Property Graph model that is faster when compared to Triple Store model in facing certain SPARQL query

Storing RDF as a Graph

RDF is the first W3C standard for enriching information resources of the Web with detailed meta data. The semantics of RDF data is defined using a RDF schema. The most expressive language for querying RDF is RQL, which enables querying of semantics. In order to support RQL, a RDF storage system has to map the RDF graph model onto its storage structure. Several storage systems for RDF data have been developed, which store the RDF data as triples in a relational database. To evaluate an RQL query on those triple structures, the graph model has to be rebuilt from the triples. In this paper, we presented a new approach to store RDF data as a graph in a object-oriented database. Our approach avoids the costly rebuilding of the graph and efficiently queries the storage structure directly. The advantages of our approach have been shown by performance test on our prototype implementation OO-Store

Partout: A Distributed Engine for Efficient RDF Processing

The increasing interest in Semantic Web technologies has led not only to a rapid growth of semantic data on the Web but also to an increasing number of backend applications with already more than a trillion triples in some cases. Confronted with such huge amounts of data and the future growth, existing state-of-the-art systems for storing RDF and processing SPARQL queries are no longer sufficient. In this paper, we introduce Partout, a distributed engine for efficient RDF processing in a cluster of machines. We propose an effective approach for fragmenting RDF data sets based on a query log, allocating the fragments to nodes in a cluster, and finding the optimal configuration. Partout can efficiently handle updates and its query optimizer produces efficient query execution plans for ad-hoc SPARQL queries. Our experiments show the superiority of our approach to state-of-the-art approaches for partitioning and distributed SPARQL query processing

Local Type Checking for Linked Data Consumers

The Web of Linked Data is the cumulation of over a decade of work by the Web standards community in their effort to make data more Web-like. We provide an introduction to the Web of Linked Data from the perspective of a Web developer that would like to build an application using Linked Data. We identify a weakness in the development stack as being a lack of domain specific scripting languages for designing background processes that consume Linked Data. To address this weakness, we design a scripting language with a simple but appropriate type system. In our proposed architecture some data is consumed from sources outside of the control of the system and some data is held locally. Stronger type assumptions can be made about the local data than external data, hence our type system mixes static and dynamic typing. Throughout, we relate our work to the W3C recommendations that drive Linked Data, so our syntax is accessible to Web developers.Comment: In Proceedings WWV 2013, arXiv:1308.026

Distributed Processing of Generalized Graph-Pattern Queries in SPARQL 1.1

We propose an efficient and scalable architecture for processing generalized graph-pattern queries as they are specified by the current W3C recommendation of the SPARQL 1.1 "Query Language" component. Specifically, the class of queries we consider consists of sets of SPARQL triple patterns with labeled property paths. From a relational perspective, this class resolves to conjunctive queries of relational joins with additional graph-reachability predicates. For the scalable, i.e., distributed, processing of this kind of queries over very large RDF collections, we develop a suitable partitioning and indexing scheme, which allows us to shard the RDF triples over an entire cluster of compute nodes and to process an incoming SPARQL query over all of the relevant graph partitions (and thus compute nodes) in parallel. Unlike most prior works in this field, we specifically aim at the unified optimization and distributed processing of queries consisting of both relational joins and graph-reachability predicates. All communication among the compute nodes is established via a proprietary, asynchronous communication protocol based on the Message Passing Interface

Towards Efficient Path Query on Social Network with Hybrid RDF Management

The scalability and exibility of Resource Description Framework(RDF) model make it ideally suited for representing online social networks(OSN). One basic operation in OSN is to find chains of relations,such as k-Hop friends. Property path query in SPARQL can express this type of operation, but its implementation suffers from performance problem considering the ever growing data size and complexity of OSN.In this paper, we present a main memory/disk based hybrid RDF data management framework for efficient property path query. In this hybrid framework, we realize an efficient in-memory algebra operator for property path query using graph traversal, and estimate the cost of this operator to cooperate with existing cost-based optimization. Experiments on benchmark and real dataset demonstrated that our approach can achieve a good tradeoff between data load expense and online query performance