An introduction to Graph Data Management

A graph database is a database where the data structures for the schema and/or instances are modeled as a (labeled)(directed) graph or generalizations of it, and where querying is expressed by graph-oriented operations and type constructors. In this article we present the basic notions of graph databases, give an historical overview of its main development, and study the main current systems that implement them

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

EAGLE—A Scalable Query Processing Engine for Linked Sensor Data

Recently, many approaches have been proposed to manage sensor data using semantic web technologies for effective heterogeneous data integration. However, our empirical observations revealed that these solutions primarily focused on semantic relationships and unfortunately paid less attention to spatio–temporal correlations. Most semantic approaches do not have spatio–temporal support. Some of them have attempted to provide full spatio–temporal support, but have poor performance for complex spatio–temporal aggregate queries. In addition, while the volume of sensor data is rapidly growing, the challenge of querying and managing the massive volumes of data generated by sensing devices still remains unsolved. In this article, we introduce EAGLE, a spatio–temporal query engine for querying sensor data based on the linked data model. The ultimate goal of EAGLE is to provide an elastic and scalable system which allows fast searching and analysis with respect to the relationships of space, time and semantics in sensor data. We also extend SPARQL with a set of new query operators in order to support spatio–temporal computing in the linked sensor data context.EC/H2020/732679/EU/ACTivating InnoVative IoT smart living environments for AGEing well/ACTIVAGEEC/H2020/661180/EU/A Scalable and Elastic Platform for Near-Realtime Analytics for The Graph of Everything/SMARTE

Bench-Ranking: ettekirjutav analüüsimeetod suurte teadmiste graafide päringutele

Relatsiooniliste suurandmete (BD) töötlemisraamistike kasutamine suurte teadmiste graafide töötlemiseks kätkeb endas võimalust päringu jõudlust optimeerimida. Kaasaegsed BD-süsteemid on samas keerulised andmesüsteemid, mille konfiguratsioonid omavad olulist mõju jõudlusele. Erinevate raamistike ja konfiguratsioonide võrdlusuuringud pakuvad kogukonnale parimaid tavasid parema jõudluse saavutamiseks. Enamik neist võrdlusuuringutest saab liigitada siiski vaid kirjeldavaks ja diagnostiliseks analüütikaks. Lisaks puudub ühtne standard nende uuringute võrdlemiseks kvantitatiivselt järjestatud kujul. Veelgi enam, suurte graafide töötlemiseks vajalike konveierite kavandamine eeldab täiendavaid disainiotsuseid mis tulenevad mitteloomulikust (relatsioonilisest) graafi töötlemise paradigmast. Taolisi disainiotsuseid ei saa automaatselt langetada, nt relatsiooniskeemi, partitsioonitehnika ja salvestusvormingute valikut. Käesolevas töös käsitleme kuidas me antud uurimuslünga täidame. Esmalt näitame disainiotsuste kompromisside mõju BD-süsteemide jõudluse korratavusele suurte teadmiste graafide päringute tegemisel. Lisaks näitame BD-raamistike jõudluse kirjeldavate ja diagnostiliste analüüside piiranguid suurte graafide päringute tegemisel. Seejärel uurime, kuidas lubada ettekirjutavat analüütikat järjestamisfunktsioonide ja mitmemõõtmeliste optimeerimistehnikate (nn "Bench-Ranking") kaudu. See lähenemine peidab kirjeldava tulemusanalüüsi keerukuse, suunates praktiku otse teostatavate teadlike otsusteni.Leveraging relational Big Data (BD) processing frameworks to process large knowledge graphs yields a great interest in optimizing query performance. Modern BD systems are yet complicated data systems, where the configurations notably affect the performance. Benchmarking different frameworks and configurations provides the community with best practices for better performance. However, most of these benchmarking efforts are classified as descriptive and diagnostic analytics. Moreover, there is no standard for comparing these benchmarks based on quantitative ranking techniques. Moreover, designing mature pipelines for processing big graphs entails considering additional design decisions that emerge with the non-native (relational) graph processing paradigm. Those design decisions cannot be decided automatically, e.g., the choice of the relational schema, partitioning technique, and storage formats. Thus, in this thesis, we discuss how our work fills this timely research gap. Particularly, we first show the impact of those design decisions’ trade-offs on the BD systems’ performance replicability when querying large knowledge graphs. Moreover, we showed the limitations of the descriptive and diagnostic analyses of BD frameworks’ performance for querying large graphs. Thus, we investigate how to enable prescriptive analytics via ranking functions and Multi-Dimensional optimization techniques (called ”Bench-Ranking”). This approach abstracts out from the complexity of descriptive performance analysis, guiding the practitioner directly to actionable informed decisions.https://www.ester.ee/record=b553332

MapReduce-based Solutions for Scalable SPARQL Querying

The use of RDF to expose semantic data on the Web has seen a dramatic increase over the last few years. Nowadays, RDF datasets are so big and rconnected that, in fact, classical mono-node solutions present significant scalability problems when trying to manage big semantic data. MapReduce, a standard framework for distributed processing of great quantities of data, is earning a place among the distributed solutions facing RDF scalability issues. In this article, we survey the most important works addressing RDF management and querying through diverse MapReduce approaches, with a focus on their main strategies, optimizations and results

An Empirical Study of Real-World SPARQL Queries

Understanding how users tailor their SPARQL queries is crucial when designing query evaluation engines or fine-tuning RDF stores with performance in mind. In this paper we analyze 3 million real-world SPARQL queries extracted from logs of the DBPedia and SWDF public endpoints. We aim at finding which are the most used language elements both from syntactical and structural perspectives, paying special attention to triple patterns and joins, since they are indeed some of the most expensive SPARQL operations at evaluation phase. We have determined that most of the queries are simple and include few triple patterns and joins, being Subject-Subject, Subject-Object and Object-Object the most common join types. The graph patterns are usually star-shaped and despite triple pattern chains exist, they are generally short.Comment: 1st International Workshop on Usage Analysis and the Web of Data (USEWOD2011) in the 20th International World Wide Web Conference (WWW2011), Hyderabad, India, March 28th, 201