Old Techniques for New Join Algorithms: A Case Study in RDF Processing

Recently there has been significant interest around designing specialized RDF engines, as traditional query processing mechanisms incur orders of magnitude performance gaps on many RDF workloads. At the same time researchers have released new worst-case optimal join algorithms which can be asymptotically better than the join algorithms in traditional engines. In this paper we apply worst-case optimal join algorithms to a standard RDF workload, the LUBM benchmark, for the first time. We do so using two worst-case optimal engines: (1) LogicBlox, a commercial database engine, and (2) EmptyHeaded, our prototype research engine with enhanced worst-case optimal join algorithms. We show that without any added optimizations both LogicBlox and EmptyHeaded outperform two state-of-the-art specialized RDF engines, RDF-3X and TripleBit, by up to 6x on cyclic join queries-the queries where traditional optimizers are suboptimal. On the remaining, less complex queries in the LUBM benchmark, we show that three classic query optimization techniques enable EmptyHeaded to compete with RDF engines, even when there is no asymptotic advantage to the worst-case optimal approach. We validate that our design has merit as EmptyHeaded outperforms MonetDB by three orders of magnitude and LogicBlox by two orders of magnitude, while remaining within an order of magnitude of RDF-3X and TripleBit

AiiDA: Automated Interactive Infrastructure and Database for Computational Science

Computational science has seen in the last decades a spectacular rise in the scope, breadth, and depth of its efforts. Notwithstanding this prevalence and impact, it is often still performed using the renaissance model of individual artisans gathered in a workshop, under the guidance of an established practitioner. Great benefits could follow instead from adopting concepts and tools coming from computer science to manage, preserve, and share these computational efforts. We illustrate here our paradigm sustaining such vision, based around the four pillars of Automation, Data, Environment, and Sharing. We then discuss its implementation in the open-source AiiDA platform (http://www.aiida.net), that has been tuned first to the demands of computational materials science. AiiDA's design is based on directed acyclic graphs to track the provenance of data and calculations, and ensure preservation and searchability. Remote computational resources are managed transparently, and automation is coupled with data storage to ensure reproducibility. Last, complex sequences of calculations can be encoded into scientific workflows. We believe that AiiDA's design and its sharing capabilities will encourage the creation of social ecosystems to disseminate codes, data, and scientific workflows.Comment: 30 pages, 7 figure

RDF Querying

Reactive Web systems, Web services, and Web-based publish/ subscribe systems communicate events as XML messages, and in many cases require composite event detection: it is not sufficient to react to single event messages, but events have to be considered in relation to other events that are received over time. Emphasizing language design and formal semantics, we describe the rule-based query language XChangeEQ for detecting composite events. XChangeEQ is designed to completely cover and integrate the four complementary querying dimensions: event data, event composition, temporal relationships, and event accumulation. Semantics are provided as model and fixpoint theories; while this is an established approach for rule languages, it has not been applied for event queries before

Perbandingan Performa Relational, Document-Oriented dan Graph Database Pada Struktur Data Directed Acyclic Graph

Abstract.Directed Acyclic Graph (DAG) is a directed graph which is not cyclic and is usually employed in social network and data genealogy. Based on the characteristic of DAG data, a suitable database type should be evaluated and then chosen as a platform. A performance comparison among relational database (PostgreSQL), document-oriented database (MongoDB), and graph database (Neo4j) on a DAG dataset are then conducted to get the appropriate database type. The performance test is done on Node.js running on Windows 10 and uses the dataset that has 3910 nodes in single write synchronous (SWS) and single read (SR). The access performance of PostgreSQL is 0.64ms on SWS and 0.32ms on SR, MongoDB is 0.64ms on SWS and 4.59ms on SR, and Neo4j is 9.92ms on SWS and 8.92ms on SR. Hence, relational database (PostgreSQL) has better performance in the operation of SWS and SR than document-oriented database (MongoDB) and graph database (Neo4j).Keywords: database performance, directed acyclic graph, relational database, document-oriented database, graph database Abstrak.Directed Acyclic Graph (DAG) adalah graf berarah tanpa putaran yang dapat ditemui pada data jejaring sosial dan silsilah keluarga. Setiap jenis database memiliki performa yang berbeda sesuai dengan struktur data yang ditangani. Oleh karena itu perlu diketahui database yang tepat khususnya untuk data DAG. Tujuan penelitian ini adalah membandingkan performa dari relational database (PostgreSQL), document-oriented database (MongoDB) dan graph database (Neo4j) pada data DAG. Metode yang dilakukan adalah mengimplentasi dataset yang memiliki 3910 node dalam operasi single write synchronous (SWS) dan single read (SR) pada setiap database menggunakan Node.js dalam Windows 10. Hasil pengujian performa PostgreSQL dalam operasi SWS sebesar 0.64ms dan SR sebesar 0.32ms, performa MongoDB pada SWS sebesar 0.64ms dan SR sebesar 4.59ms sedangkan performa Neo4j pada operasi SWS sebesar 9.92ms dan SR sebesar 8.92ms. Hasil penelitian menunjukan bahwa relational database (PostgreSQL) memiliki performa terbaik dalam operasi SWS dan SR dibandingkan document-oriented database (MongoDB) dan graph database (Neo4j).Kata Kunci: performa database, directed acyclic graph, relational database, document-oriented database, graph databas