SlideShare une entreprise Scribd logo
1  sur  9
Télécharger pour lire hors ligne
Database Technologies
  for Semantic Web
     Edilson Ribeiro da Silva Júnior
        José Maria Silveira Neto




             
TripleStore
   triple {subject, predicate, object}
   TripleStore is an RDF Database. 
   add(triple), remove(triple), triples(pattern), ...




                          
Sesame

   An open­source framework for querying, 
    inferencing and analyzing RDF data.
   SeSQL
   BSD­style license
   Java
   PostgreSQL, MySQL, Microsoft SQL Server and 
    Oracle  databases.



                         
SeSQL
   Sesame RDF Query Languag, ”circle”
   is a new RDF/RDFS query language that is currently 
    being developed by Aduna as part of Sesame. It 
    combines the best features of other (query) 
    languages (RQL, RDQL, N­Triples, N3) and adds 
    some of its own. 
   Graph transformation, RDF Schema support, XML 
    Schema datatype support, Expressive path 
    expression syntax, Optional path matching.

                       
Virtuoso
   object­relational SQL database
   built­in web server
   SPARQL
   GPLv2
   ODBC, JDBC, ADO .Net and OLE/DB.
   C, Java, etc.



                           
Jena
   HP Labs Semantic Web Programme
   RDF API
   Reading and writing RDF in RDF/XML, N3 and N­
    Triples
   OWL API
   In­memory and persistent storage
   SPARQL query engine
   Jena SDB
   BSD­style license
                         
R2RQ
   D2RQ is a declarative language to describe 
    mappings between relational database schemata and 
    OWL/RDFS ontologies. The D2RQ Platform uses 
    these mapping to enables applications to access a 
    RDF­view on a non­RDF database through the Jena 
    and Sesame APIs, as well as over the Web via the 
    SPARQL Protocol and as Linked Data.




                       
Large TripleStores
   Garlik JXT (9.8B)                  Jena with PostgreSQL (200M)
   YARS2 (7B)                         Kowari (160M)
   BigOWLIM (1.85B)                   3store with MySQL 3 (100M)
   Virtuoso Open­Source Edition       Sesame (70M)
    (1B+)
   AllegroGraph (1B)
   Jena SDB (650M)
   Mulgara (500M)
   RDF gateway (262M)



                            
References
   http://rdflib.net/2005/10/26/rdflib­2.2.4/doc/triple_store.
   http://en.wikipedia.org/wiki/Triplestore
   http://recuperaciondeinformacion.itrello.com/SeRQL­en
   http://esw.w3.org/topic/LargeTripleStores
   http://www4.wiwiss.fu­berlin.de/bizer/D2RQ/




                         

Contenu connexe

Tendances

Big Data Processing with Spark and Scala
Big Data Processing with Spark and Scala Big Data Processing with Spark and Scala
Big Data Processing with Spark and Scala Edureka!
 
Apache HBase - Just the Basics
Apache HBase - Just the BasicsApache HBase - Just the Basics
Apache HBase - Just the BasicsHBaseCon
 
Introduction To HBase
Introduction To HBaseIntroduction To HBase
Introduction To HBaseAnil Gupta
 
Hadoop a Highly Available and Secure Enterprise Data Warehousing solution
Hadoop a Highly Available and Secure Enterprise Data Warehousing solutionHadoop a Highly Available and Secure Enterprise Data Warehousing solution
Hadoop a Highly Available and Secure Enterprise Data Warehousing solutionEdureka!
 
Performance of Spark vs MapReduce
Performance of Spark vs MapReducePerformance of Spark vs MapReduce
Performance of Spark vs MapReduceEdureka!
 
Hadoop : The Pile of Big Data
Hadoop : The Pile of Big DataHadoop : The Pile of Big Data
Hadoop : The Pile of Big DataEdureka!
 
Apache Spark beyond Hadoop MapReduce
Apache Spark beyond Hadoop MapReduceApache Spark beyond Hadoop MapReduce
Apache Spark beyond Hadoop MapReduceEdureka!
 
Spark for big data analytics
Spark for big data analyticsSpark for big data analytics
Spark for big data analyticsEdureka!
 
Introduction to Apache Hive(Big Data, Final Seminar)
Introduction to Apache Hive(Big Data, Final Seminar)Introduction to Apache Hive(Big Data, Final Seminar)
Introduction to Apache Hive(Big Data, Final Seminar)Takrim Ul Islam Laskar
 
HBaseCon 2012 | HBase Schema Design - Ian Varley, Salesforce
HBaseCon 2012 | HBase Schema Design - Ian Varley, SalesforceHBaseCon 2012 | HBase Schema Design - Ian Varley, Salesforce
HBaseCon 2012 | HBase Schema Design - Ian Varley, SalesforceCloudera, Inc.
 
Spark For The Business Analyst
Spark For The Business AnalystSpark For The Business Analyst
Spark For The Business AnalystGustaf Cavanaugh
 
Intro to Apache Spark by CTO of Twingo
Intro to Apache Spark by CTO of TwingoIntro to Apache Spark by CTO of Twingo
Intro to Apache Spark by CTO of TwingoMapR Technologies
 
Hive: Data Warehousing for Hadoop
Hive: Data Warehousing for HadoopHive: Data Warehousing for Hadoop
Hive: Data Warehousing for Hadoopbigdatasyd
 

Tendances (20)

Big Data Processing with Spark and Scala
Big Data Processing with Spark and Scala Big Data Processing with Spark and Scala
Big Data Processing with Spark and Scala
 
Apache hive
Apache hiveApache hive
Apache hive
 
Apache HBase - Just the Basics
Apache HBase - Just the BasicsApache HBase - Just the Basics
Apache HBase - Just the Basics
 
Session 14 - Hive
Session 14 - HiveSession 14 - Hive
Session 14 - Hive
 
Introduction To HBase
Introduction To HBaseIntroduction To HBase
Introduction To HBase
 
Hadoop a Highly Available and Secure Enterprise Data Warehousing solution
Hadoop a Highly Available and Secure Enterprise Data Warehousing solutionHadoop a Highly Available and Secure Enterprise Data Warehousing solution
Hadoop a Highly Available and Secure Enterprise Data Warehousing solution
 
Using MRuby in a database
Using MRuby in a databaseUsing MRuby in a database
Using MRuby in a database
 
HBase
HBaseHBase
HBase
 
Performance of Spark vs MapReduce
Performance of Spark vs MapReducePerformance of Spark vs MapReduce
Performance of Spark vs MapReduce
 
RDFauthor (EKAW)
RDFauthor (EKAW)RDFauthor (EKAW)
RDFauthor (EKAW)
 
Hadoop : The Pile of Big Data
Hadoop : The Pile of Big DataHadoop : The Pile of Big Data
Hadoop : The Pile of Big Data
 
Apache Spark beyond Hadoop MapReduce
Apache Spark beyond Hadoop MapReduceApache Spark beyond Hadoop MapReduce
Apache Spark beyond Hadoop MapReduce
 
Spark for big data analytics
Spark for big data analyticsSpark for big data analytics
Spark for big data analytics
 
Introduction to Apache Hive(Big Data, Final Seminar)
Introduction to Apache Hive(Big Data, Final Seminar)Introduction to Apache Hive(Big Data, Final Seminar)
Introduction to Apache Hive(Big Data, Final Seminar)
 
HBaseCon 2012 | HBase Schema Design - Ian Varley, Salesforce
HBaseCon 2012 | HBase Schema Design - Ian Varley, SalesforceHBaseCon 2012 | HBase Schema Design - Ian Varley, Salesforce
HBaseCon 2012 | HBase Schema Design - Ian Varley, Salesforce
 
Apache Hive
Apache HiveApache Hive
Apache Hive
 
Spark For The Business Analyst
Spark For The Business AnalystSpark For The Business Analyst
Spark For The Business Analyst
 
Intro to Apache Spark by CTO of Twingo
Intro to Apache Spark by CTO of TwingoIntro to Apache Spark by CTO of Twingo
Intro to Apache Spark by CTO of Twingo
 
Hive: Data Warehousing for Hadoop
Hive: Data Warehousing for HadoopHive: Data Warehousing for Hadoop
Hive: Data Warehousing for Hadoop
 
Sql server 2
Sql server 2Sql server 2
Sql server 2
 

En vedette

Strategic planning
Strategic planningStrategic planning
Strategic planningDhani Ahmad
 
Database connectivity and web technologies
Database connectivity and web technologiesDatabase connectivity and web technologies
Database connectivity and web technologiesDhani Ahmad
 
Database administration and security
Database administration and securityDatabase administration and security
Database administration and securityDhani Ahmad
 
Top-down and bottom-up informatics: who has the high ground?
Top-down and bottom-up informatics: who has the high ground?Top-down and bottom-up informatics: who has the high ground?
Top-down and bottom-up informatics: who has the high ground?Vince Smith
 
Datamining at SemWebPro 2012
Datamining at SemWebPro 2012Datamining at SemWebPro 2012
Datamining at SemWebPro 2012Vincent Michel
 
Preference Handling in Relational Query Languages
Preference Handling in Relational Query LanguagesPreference Handling in Relational Query Languages
Preference Handling in Relational Query Languagesradned
 
9780538469685 ppt ch12 1er exa
9780538469685 ppt ch12 1er exa9780538469685 ppt ch12 1er exa
9780538469685 ppt ch12 1er exacarldevsco63
 
Multimedia database
Multimedia databaseMultimedia database
Multimedia databaseRashmi Agale
 
Emerging database technology multimedia database
Emerging database technology   multimedia databaseEmerging database technology   multimedia database
Emerging database technology multimedia databaseSalama Al Busaidi
 
Resource description framework
Resource description frameworkResource description framework
Resource description frameworkhozifa1010
 
Bsc cs ii-dbms- u-i-database systems
Bsc cs ii-dbms- u-i-database systemsBsc cs ii-dbms- u-i-database systems
Bsc cs ii-dbms- u-i-database systemsRai University
 
Chapter02 succeeding as a systems analyst
Chapter02 succeeding as a systems analystChapter02 succeeding as a systems analyst
Chapter02 succeeding as a systems analystDhani Ahmad
 
Chapter06 initiating and planning systems development projects
Chapter06 initiating and planning systems development projectsChapter06 initiating and planning systems development projects
Chapter06 initiating and planning systems development projectsDhani Ahmad
 
Database systems
Database systemsDatabase systems
Database systemsDhani Ahmad
 
TID Chapter 10 Introduction To Database
TID Chapter 10 Introduction To DatabaseTID Chapter 10 Introduction To Database
TID Chapter 10 Introduction To DatabaseWanBK Leo
 

En vedette (20)

Strategic planning
Strategic planningStrategic planning
Strategic planning
 
Database connectivity and web technologies
Database connectivity and web technologiesDatabase connectivity and web technologies
Database connectivity and web technologies
 
Database administration and security
Database administration and securityDatabase administration and security
Database administration and security
 
Top-down and bottom-up informatics: who has the high ground?
Top-down and bottom-up informatics: who has the high ground?Top-down and bottom-up informatics: who has the high ground?
Top-down and bottom-up informatics: who has the high ground?
 
Datamining at SemWebPro 2012
Datamining at SemWebPro 2012Datamining at SemWebPro 2012
Datamining at SemWebPro 2012
 
Preference Handling in Relational Query Languages
Preference Handling in Relational Query LanguagesPreference Handling in Relational Query Languages
Preference Handling in Relational Query Languages
 
Introduction to RDF
Introduction to RDFIntroduction to RDF
Introduction to RDF
 
9780538469685 ppt ch12 1er exa
9780538469685 ppt ch12 1er exa9780538469685 ppt ch12 1er exa
9780538469685 ppt ch12 1er exa
 
Database
DatabaseDatabase
Database
 
Multimedia database
Multimedia databaseMultimedia database
Multimedia database
 
Emerging database technology multimedia database
Emerging database technology   multimedia databaseEmerging database technology   multimedia database
Emerging database technology multimedia database
 
Resource description framework
Resource description frameworkResource description framework
Resource description framework
 
Multimedia Database
Multimedia DatabaseMultimedia Database
Multimedia Database
 
Bsc cs ii-dbms- u-i-database systems
Bsc cs ii-dbms- u-i-database systemsBsc cs ii-dbms- u-i-database systems
Bsc cs ii-dbms- u-i-database systems
 
database
databasedatabase
database
 
Chapter02 succeeding as a systems analyst
Chapter02 succeeding as a systems analystChapter02 succeeding as a systems analyst
Chapter02 succeeding as a systems analyst
 
Chapter01 1
Chapter01 1Chapter01 1
Chapter01 1
 
Chapter06 initiating and planning systems development projects
Chapter06 initiating and planning systems development projectsChapter06 initiating and planning systems development projects
Chapter06 initiating and planning systems development projects
 
Database systems
Database systemsDatabase systems
Database systems
 
TID Chapter 10 Introduction To Database
TID Chapter 10 Introduction To DatabaseTID Chapter 10 Introduction To Database
TID Chapter 10 Introduction To Database
 

Similaire à Database Technologies for Semantic Web

GraphTech Ecosystem - part 1: Graph Databases
GraphTech Ecosystem - part 1: Graph DatabasesGraphTech Ecosystem - part 1: Graph Databases
GraphTech Ecosystem - part 1: Graph DatabasesLinkurious
 
Strata NYC 2015 - Supercharging R with Apache Spark
Strata NYC 2015 - Supercharging R with Apache SparkStrata NYC 2015 - Supercharging R with Apache Spark
Strata NYC 2015 - Supercharging R with Apache SparkDatabricks
 
8th TUC Meeting - Zhe Wu (Oracle USA). Bridging RDF Graph and Property Graph...
8th TUC Meeting -  Zhe Wu (Oracle USA). Bridging RDF Graph and Property Graph...8th TUC Meeting -  Zhe Wu (Oracle USA). Bridging RDF Graph and Property Graph...
8th TUC Meeting - Zhe Wu (Oracle USA). Bridging RDF Graph and Property Graph...LDBC council
 
Cleveland Hadoop Users Group - Spark
Cleveland Hadoop Users Group - SparkCleveland Hadoop Users Group - Spark
Cleveland Hadoop Users Group - SparkVince Gonzalez
 
An Introduction to Apache Spark
An Introduction to Apache SparkAn Introduction to Apache Spark
An Introduction to Apache SparkElvis Saravia
 
Spark from the Surface
Spark from the SurfaceSpark from the Surface
Spark from the SurfaceJosi Aranda
 
Big data analysis using spark r published
Big data analysis using spark r publishedBig data analysis using spark r published
Big data analysis using spark r publishedDipendra Kusi
 
Enabling exploratory data science with Spark and R
Enabling exploratory data science with Spark and REnabling exploratory data science with Spark and R
Enabling exploratory data science with Spark and RDatabricks
 
Hadoop and rdbms with sqoop
Hadoop and rdbms with sqoop Hadoop and rdbms with sqoop
Hadoop and rdbms with sqoop Guy Harrison
 
Cassandra Lunch #89: Semi-Structured Data in Cassandra
Cassandra Lunch #89: Semi-Structured Data in CassandraCassandra Lunch #89: Semi-Structured Data in Cassandra
Cassandra Lunch #89: Semi-Structured Data in CassandraAnant Corporation
 
XSLT+SPARQL: Scripting the Semantic Web with SPARQL embedded into XSLT styles...
XSLT+SPARQL: Scripting the Semantic Web with SPARQL embedded into XSLT styles...XSLT+SPARQL: Scripting the Semantic Web with SPARQL embedded into XSLT styles...
XSLT+SPARQL: Scripting the Semantic Web with SPARQL embedded into XSLT styles...Diego Berrueta
 
Introduction to Property Graph Features (AskTOM Office Hours part 1)
Introduction to Property Graph Features (AskTOM Office Hours part 1) Introduction to Property Graph Features (AskTOM Office Hours part 1)
Introduction to Property Graph Features (AskTOM Office Hours part 1) Jean Ihm
 
Applying large scale text analytics with graph databases
Applying large scale text analytics with graph databasesApplying large scale text analytics with graph databases
Applying large scale text analytics with graph databasesData Ninja API
 
A Tale of Three Apache Spark APIs: RDDs, DataFrames, and Datasets with Jules ...
A Tale of Three Apache Spark APIs: RDDs, DataFrames, and Datasets with Jules ...A Tale of Three Apache Spark APIs: RDDs, DataFrames, and Datasets with Jules ...
A Tale of Three Apache Spark APIs: RDDs, DataFrames, and Datasets with Jules ...Databricks
 
NEW LAUNCH! How to build graph applications with SPARQL and Gremlin using Ama...
NEW LAUNCH! How to build graph applications with SPARQL and Gremlin using Ama...NEW LAUNCH! How to build graph applications with SPARQL and Gremlin using Ama...
NEW LAUNCH! How to build graph applications with SPARQL and Gremlin using Ama...Amazon Web Services
 
TDWI Accelerate, Seattle, Oct 16, 2017: Distributed and In-Database Analytics...
TDWI Accelerate, Seattle, Oct 16, 2017: Distributed and In-Database Analytics...TDWI Accelerate, Seattle, Oct 16, 2017: Distributed and In-Database Analytics...
TDWI Accelerate, Seattle, Oct 16, 2017: Distributed and In-Database Analytics...Debraj GuhaThakurta
 
TWDI Accelerate Seattle, Oct 16, 2017: Distributed and In-Database Analytics ...
TWDI Accelerate Seattle, Oct 16, 2017: Distributed and In-Database Analytics ...TWDI Accelerate Seattle, Oct 16, 2017: Distributed and In-Database Analytics ...
TWDI Accelerate Seattle, Oct 16, 2017: Distributed and In-Database Analytics ...Debraj GuhaThakurta
 

Similaire à Database Technologies for Semantic Web (20)

GraphTech Ecosystem - part 1: Graph Databases
GraphTech Ecosystem - part 1: Graph DatabasesGraphTech Ecosystem - part 1: Graph Databases
GraphTech Ecosystem - part 1: Graph Databases
 
Strata NYC 2015 - Supercharging R with Apache Spark
Strata NYC 2015 - Supercharging R with Apache SparkStrata NYC 2015 - Supercharging R with Apache Spark
Strata NYC 2015 - Supercharging R with Apache Spark
 
8th TUC Meeting - Zhe Wu (Oracle USA). Bridging RDF Graph and Property Graph...
8th TUC Meeting -  Zhe Wu (Oracle USA). Bridging RDF Graph and Property Graph...8th TUC Meeting -  Zhe Wu (Oracle USA). Bridging RDF Graph and Property Graph...
8th TUC Meeting - Zhe Wu (Oracle USA). Bridging RDF Graph and Property Graph...
 
Cleveland Hadoop Users Group - Spark
Cleveland Hadoop Users Group - SparkCleveland Hadoop Users Group - Spark
Cleveland Hadoop Users Group - Spark
 
Spark_Part 1
Spark_Part 1Spark_Part 1
Spark_Part 1
 
An Introduction to Apache Spark
An Introduction to Apache SparkAn Introduction to Apache Spark
An Introduction to Apache Spark
 
Spark from the Surface
Spark from the SurfaceSpark from the Surface
Spark from the Surface
 
Introduction to dotNetRDF
Introduction to dotNetRDFIntroduction to dotNetRDF
Introduction to dotNetRDF
 
Big data analysis using spark r published
Big data analysis using spark r publishedBig data analysis using spark r published
Big data analysis using spark r published
 
Enabling exploratory data science with Spark and R
Enabling exploratory data science with Spark and REnabling exploratory data science with Spark and R
Enabling exploratory data science with Spark and R
 
NoSQL
NoSQLNoSQL
NoSQL
 
Hadoop and rdbms with sqoop
Hadoop and rdbms with sqoop Hadoop and rdbms with sqoop
Hadoop and rdbms with sqoop
 
Cassandra Lunch #89: Semi-Structured Data in Cassandra
Cassandra Lunch #89: Semi-Structured Data in CassandraCassandra Lunch #89: Semi-Structured Data in Cassandra
Cassandra Lunch #89: Semi-Structured Data in Cassandra
 
XSLT+SPARQL: Scripting the Semantic Web with SPARQL embedded into XSLT styles...
XSLT+SPARQL: Scripting the Semantic Web with SPARQL embedded into XSLT styles...XSLT+SPARQL: Scripting the Semantic Web with SPARQL embedded into XSLT styles...
XSLT+SPARQL: Scripting the Semantic Web with SPARQL embedded into XSLT styles...
 
Introduction to Property Graph Features (AskTOM Office Hours part 1)
Introduction to Property Graph Features (AskTOM Office Hours part 1) Introduction to Property Graph Features (AskTOM Office Hours part 1)
Introduction to Property Graph Features (AskTOM Office Hours part 1)
 
Applying large scale text analytics with graph databases
Applying large scale text analytics with graph databasesApplying large scale text analytics with graph databases
Applying large scale text analytics with graph databases
 
A Tale of Three Apache Spark APIs: RDDs, DataFrames, and Datasets with Jules ...
A Tale of Three Apache Spark APIs: RDDs, DataFrames, and Datasets with Jules ...A Tale of Three Apache Spark APIs: RDDs, DataFrames, and Datasets with Jules ...
A Tale of Three Apache Spark APIs: RDDs, DataFrames, and Datasets with Jules ...
 
NEW LAUNCH! How to build graph applications with SPARQL and Gremlin using Ama...
NEW LAUNCH! How to build graph applications with SPARQL and Gremlin using Ama...NEW LAUNCH! How to build graph applications with SPARQL and Gremlin using Ama...
NEW LAUNCH! How to build graph applications with SPARQL and Gremlin using Ama...
 
TDWI Accelerate, Seattle, Oct 16, 2017: Distributed and In-Database Analytics...
TDWI Accelerate, Seattle, Oct 16, 2017: Distributed and In-Database Analytics...TDWI Accelerate, Seattle, Oct 16, 2017: Distributed and In-Database Analytics...
TDWI Accelerate, Seattle, Oct 16, 2017: Distributed and In-Database Analytics...
 
TWDI Accelerate Seattle, Oct 16, 2017: Distributed and In-Database Analytics ...
TWDI Accelerate Seattle, Oct 16, 2017: Distributed and In-Database Analytics ...TWDI Accelerate Seattle, Oct 16, 2017: Distributed and In-Database Analytics ...
TWDI Accelerate Seattle, Oct 16, 2017: Distributed and In-Database Analytics ...
 

Plus de José Maria Silveira Neto

Plus de José Maria Silveira Neto (20)

Android - visão geral
Android - visão geralAndroid - visão geral
Android - visão geral
 
Pixelart
PixelartPixelart
Pixelart
 
Tomorrow Java
Tomorrow JavaTomorrow Java
Tomorrow Java
 
JavaFX Primeiros Passos
JavaFX Primeiros PassosJavaFX Primeiros Passos
JavaFX Primeiros Passos
 
Desenvolvimento de Aplicações
Desenvolvimento de AplicaçõesDesenvolvimento de Aplicações
Desenvolvimento de Aplicações
 
Apresentando o CEJUG e o poder do Java
Apresentando o CEJUG e o poder do JavaApresentando o CEJUG e o poder do Java
Apresentando o CEJUG e o poder do Java
 
Let's talk about Certifications
Let's talk about CertificationsLet's talk about Certifications
Let's talk about Certifications
 
JavaFX Overview
JavaFX OverviewJavaFX Overview
JavaFX Overview
 
NetBeans: a IDE que você precisa
NetBeans: a IDE que você precisaNetBeans: a IDE que você precisa
NetBeans: a IDE que você precisa
 
OpenSolaris a Céu Aberto
OpenSolaris a Céu AbertoOpenSolaris a Céu Aberto
OpenSolaris a Céu Aberto
 
JavaFX introduction
JavaFX introductionJavaFX introduction
JavaFX introduction
 
High-Performance Computing and OpenSolaris
High-Performance Computing and OpenSolarisHigh-Performance Computing and OpenSolaris
High-Performance Computing and OpenSolaris
 
SVG como exemplo de XML
SVG como exemplo de XMLSVG como exemplo de XML
SVG como exemplo de XML
 
Questões de Certificação SCJP
Questões de Certificação SCJPQuestões de Certificação SCJP
Questões de Certificação SCJP
 
Microformatos em 10 minutos
Microformatos em 10 minutosMicroformatos em 10 minutos
Microformatos em 10 minutos
 
Participation Era, Sun and You
Participation Era, Sun and YouParticipation Era, Sun and You
Participation Era, Sun and You
 
Let's talk about certification: SCJA
Let's talk about certification: SCJALet's talk about certification: SCJA
Let's talk about certification: SCJA
 
Uma Olhada no Netbeans 6
Uma Olhada no Netbeans 6Uma Olhada no Netbeans 6
Uma Olhada no Netbeans 6
 
Real World Technologies
Real World TechnologiesReal World Technologies
Real World Technologies
 
Novidades no Netbeans 6
Novidades no Netbeans 6Novidades no Netbeans 6
Novidades no Netbeans 6
 

Dernier

Best VIP Call Girls Noida Sector 40 Call Me: 8448380779
Best VIP Call Girls Noida Sector 40 Call Me: 8448380779Best VIP Call Girls Noida Sector 40 Call Me: 8448380779
Best VIP Call Girls Noida Sector 40 Call Me: 8448380779Delhi Call girls
 
👉Chandigarh Call Girls 👉9878799926👉Just Call👉Chandigarh Call Girl In Chandiga...
👉Chandigarh Call Girls 👉9878799926👉Just Call👉Chandigarh Call Girl In Chandiga...👉Chandigarh Call Girls 👉9878799926👉Just Call👉Chandigarh Call Girl In Chandiga...
👉Chandigarh Call Girls 👉9878799926👉Just Call👉Chandigarh Call Girl In Chandiga...rajveerescorts2022
 
John Halpern sued for sexual assault.pdf
John Halpern sued for sexual assault.pdfJohn Halpern sued for sexual assault.pdf
John Halpern sued for sexual assault.pdfAmzadHosen3
 
MONA 98765-12871 CALL GIRLS IN LUDHIANA LUDHIANA CALL GIRL
MONA 98765-12871 CALL GIRLS IN LUDHIANA LUDHIANA CALL GIRLMONA 98765-12871 CALL GIRLS IN LUDHIANA LUDHIANA CALL GIRL
MONA 98765-12871 CALL GIRLS IN LUDHIANA LUDHIANA CALL GIRLSeo
 
Famous Olympic Siblings from the 21st Century
Famous Olympic Siblings from the 21st CenturyFamous Olympic Siblings from the 21st Century
Famous Olympic Siblings from the 21st Centuryrwgiffor
 
Grateful 7 speech thanking everyone that has helped.pdf
Grateful 7 speech thanking everyone that has helped.pdfGrateful 7 speech thanking everyone that has helped.pdf
Grateful 7 speech thanking everyone that has helped.pdfPaul Menig
 
Call Girls In DLf Gurgaon ➥99902@11544 ( Best price)100% Genuine Escort In 24...
Call Girls In DLf Gurgaon ➥99902@11544 ( Best price)100% Genuine Escort In 24...Call Girls In DLf Gurgaon ➥99902@11544 ( Best price)100% Genuine Escort In 24...
Call Girls In DLf Gurgaon ➥99902@11544 ( Best price)100% Genuine Escort In 24...lizamodels9
 
Regression analysis: Simple Linear Regression Multiple Linear Regression
Regression analysis:  Simple Linear Regression Multiple Linear RegressionRegression analysis:  Simple Linear Regression Multiple Linear Regression
Regression analysis: Simple Linear Regression Multiple Linear RegressionRavindra Nath Shukla
 
Monte Carlo simulation : Simulation using MCSM
Monte Carlo simulation : Simulation using MCSMMonte Carlo simulation : Simulation using MCSM
Monte Carlo simulation : Simulation using MCSMRavindra Nath Shukla
 
A DAY IN THE LIFE OF A SALESMAN / WOMAN
A DAY IN THE LIFE OF A  SALESMAN / WOMANA DAY IN THE LIFE OF A  SALESMAN / WOMAN
A DAY IN THE LIFE OF A SALESMAN / WOMANIlamathiKannappan
 
0183760ssssssssssssssssssssssssssss00101011 (27).pdf
0183760ssssssssssssssssssssssssssss00101011 (27).pdf0183760ssssssssssssssssssssssssssss00101011 (27).pdf
0183760ssssssssssssssssssssssssssss00101011 (27).pdfRenandantas16
 
Call Girls Hebbal Just Call 👗 7737669865 👗 Top Class Call Girl Service Bangalore
Call Girls Hebbal Just Call 👗 7737669865 👗 Top Class Call Girl Service BangaloreCall Girls Hebbal Just Call 👗 7737669865 👗 Top Class Call Girl Service Bangalore
Call Girls Hebbal Just Call 👗 7737669865 👗 Top Class Call Girl Service Bangaloreamitlee9823
 
B.COM Unit – 4 ( CORPORATE SOCIAL RESPONSIBILITY ( CSR ).pptx
B.COM Unit – 4 ( CORPORATE SOCIAL RESPONSIBILITY ( CSR ).pptxB.COM Unit – 4 ( CORPORATE SOCIAL RESPONSIBILITY ( CSR ).pptx
B.COM Unit – 4 ( CORPORATE SOCIAL RESPONSIBILITY ( CSR ).pptxpriyanshujha201
 
Lucknow 💋 Escorts in Lucknow - 450+ Call Girl Cash Payment 8923113531 Neha Th...
Lucknow 💋 Escorts in Lucknow - 450+ Call Girl Cash Payment 8923113531 Neha Th...Lucknow 💋 Escorts in Lucknow - 450+ Call Girl Cash Payment 8923113531 Neha Th...
Lucknow 💋 Escorts in Lucknow - 450+ Call Girl Cash Payment 8923113531 Neha Th...anilsa9823
 
KYC-Verified Accounts: Helping Companies Handle Challenging Regulatory Enviro...
KYC-Verified Accounts: Helping Companies Handle Challenging Regulatory Enviro...KYC-Verified Accounts: Helping Companies Handle Challenging Regulatory Enviro...
KYC-Verified Accounts: Helping Companies Handle Challenging Regulatory Enviro...Any kyc Account
 
Ensure the security of your HCL environment by applying the Zero Trust princi...
Ensure the security of your HCL environment by applying the Zero Trust princi...Ensure the security of your HCL environment by applying the Zero Trust princi...
Ensure the security of your HCL environment by applying the Zero Trust princi...Roland Driesen
 
Call Girls Jp Nagar Just Call 👗 7737669865 👗 Top Class Call Girl Service Bang...
Call Girls Jp Nagar Just Call 👗 7737669865 👗 Top Class Call Girl Service Bang...Call Girls Jp Nagar Just Call 👗 7737669865 👗 Top Class Call Girl Service Bang...
Call Girls Jp Nagar Just Call 👗 7737669865 👗 Top Class Call Girl Service Bang...amitlee9823
 
Dr. Admir Softic_ presentation_Green Club_ENG.pdf
Dr. Admir Softic_ presentation_Green Club_ENG.pdfDr. Admir Softic_ presentation_Green Club_ENG.pdf
Dr. Admir Softic_ presentation_Green Club_ENG.pdfAdmir Softic
 
Organizational Transformation Lead with Culture
Organizational Transformation Lead with CultureOrganizational Transformation Lead with Culture
Organizational Transformation Lead with CultureSeta Wicaksana
 

Dernier (20)

Best VIP Call Girls Noida Sector 40 Call Me: 8448380779
Best VIP Call Girls Noida Sector 40 Call Me: 8448380779Best VIP Call Girls Noida Sector 40 Call Me: 8448380779
Best VIP Call Girls Noida Sector 40 Call Me: 8448380779
 
👉Chandigarh Call Girls 👉9878799926👉Just Call👉Chandigarh Call Girl In Chandiga...
👉Chandigarh Call Girls 👉9878799926👉Just Call👉Chandigarh Call Girl In Chandiga...👉Chandigarh Call Girls 👉9878799926👉Just Call👉Chandigarh Call Girl In Chandiga...
👉Chandigarh Call Girls 👉9878799926👉Just Call👉Chandigarh Call Girl In Chandiga...
 
Forklift Operations: Safety through Cartoons
Forklift Operations: Safety through CartoonsForklift Operations: Safety through Cartoons
Forklift Operations: Safety through Cartoons
 
John Halpern sued for sexual assault.pdf
John Halpern sued for sexual assault.pdfJohn Halpern sued for sexual assault.pdf
John Halpern sued for sexual assault.pdf
 
MONA 98765-12871 CALL GIRLS IN LUDHIANA LUDHIANA CALL GIRL
MONA 98765-12871 CALL GIRLS IN LUDHIANA LUDHIANA CALL GIRLMONA 98765-12871 CALL GIRLS IN LUDHIANA LUDHIANA CALL GIRL
MONA 98765-12871 CALL GIRLS IN LUDHIANA LUDHIANA CALL GIRL
 
Famous Olympic Siblings from the 21st Century
Famous Olympic Siblings from the 21st CenturyFamous Olympic Siblings from the 21st Century
Famous Olympic Siblings from the 21st Century
 
Grateful 7 speech thanking everyone that has helped.pdf
Grateful 7 speech thanking everyone that has helped.pdfGrateful 7 speech thanking everyone that has helped.pdf
Grateful 7 speech thanking everyone that has helped.pdf
 
Call Girls In DLf Gurgaon ➥99902@11544 ( Best price)100% Genuine Escort In 24...
Call Girls In DLf Gurgaon ➥99902@11544 ( Best price)100% Genuine Escort In 24...Call Girls In DLf Gurgaon ➥99902@11544 ( Best price)100% Genuine Escort In 24...
Call Girls In DLf Gurgaon ➥99902@11544 ( Best price)100% Genuine Escort In 24...
 
Regression analysis: Simple Linear Regression Multiple Linear Regression
Regression analysis:  Simple Linear Regression Multiple Linear RegressionRegression analysis:  Simple Linear Regression Multiple Linear Regression
Regression analysis: Simple Linear Regression Multiple Linear Regression
 
Monte Carlo simulation : Simulation using MCSM
Monte Carlo simulation : Simulation using MCSMMonte Carlo simulation : Simulation using MCSM
Monte Carlo simulation : Simulation using MCSM
 
A DAY IN THE LIFE OF A SALESMAN / WOMAN
A DAY IN THE LIFE OF A  SALESMAN / WOMANA DAY IN THE LIFE OF A  SALESMAN / WOMAN
A DAY IN THE LIFE OF A SALESMAN / WOMAN
 
0183760ssssssssssssssssssssssssssss00101011 (27).pdf
0183760ssssssssssssssssssssssssssss00101011 (27).pdf0183760ssssssssssssssssssssssssssss00101011 (27).pdf
0183760ssssssssssssssssssssssssssss00101011 (27).pdf
 
Call Girls Hebbal Just Call 👗 7737669865 👗 Top Class Call Girl Service Bangalore
Call Girls Hebbal Just Call 👗 7737669865 👗 Top Class Call Girl Service BangaloreCall Girls Hebbal Just Call 👗 7737669865 👗 Top Class Call Girl Service Bangalore
Call Girls Hebbal Just Call 👗 7737669865 👗 Top Class Call Girl Service Bangalore
 
B.COM Unit – 4 ( CORPORATE SOCIAL RESPONSIBILITY ( CSR ).pptx
B.COM Unit – 4 ( CORPORATE SOCIAL RESPONSIBILITY ( CSR ).pptxB.COM Unit – 4 ( CORPORATE SOCIAL RESPONSIBILITY ( CSR ).pptx
B.COM Unit – 4 ( CORPORATE SOCIAL RESPONSIBILITY ( CSR ).pptx
 
Lucknow 💋 Escorts in Lucknow - 450+ Call Girl Cash Payment 8923113531 Neha Th...
Lucknow 💋 Escorts in Lucknow - 450+ Call Girl Cash Payment 8923113531 Neha Th...Lucknow 💋 Escorts in Lucknow - 450+ Call Girl Cash Payment 8923113531 Neha Th...
Lucknow 💋 Escorts in Lucknow - 450+ Call Girl Cash Payment 8923113531 Neha Th...
 
KYC-Verified Accounts: Helping Companies Handle Challenging Regulatory Enviro...
KYC-Verified Accounts: Helping Companies Handle Challenging Regulatory Enviro...KYC-Verified Accounts: Helping Companies Handle Challenging Regulatory Enviro...
KYC-Verified Accounts: Helping Companies Handle Challenging Regulatory Enviro...
 
Ensure the security of your HCL environment by applying the Zero Trust princi...
Ensure the security of your HCL environment by applying the Zero Trust princi...Ensure the security of your HCL environment by applying the Zero Trust princi...
Ensure the security of your HCL environment by applying the Zero Trust princi...
 
Call Girls Jp Nagar Just Call 👗 7737669865 👗 Top Class Call Girl Service Bang...
Call Girls Jp Nagar Just Call 👗 7737669865 👗 Top Class Call Girl Service Bang...Call Girls Jp Nagar Just Call 👗 7737669865 👗 Top Class Call Girl Service Bang...
Call Girls Jp Nagar Just Call 👗 7737669865 👗 Top Class Call Girl Service Bang...
 
Dr. Admir Softic_ presentation_Green Club_ENG.pdf
Dr. Admir Softic_ presentation_Green Club_ENG.pdfDr. Admir Softic_ presentation_Green Club_ENG.pdf
Dr. Admir Softic_ presentation_Green Club_ENG.pdf
 
Organizational Transformation Lead with Culture
Organizational Transformation Lead with CultureOrganizational Transformation Lead with Culture
Organizational Transformation Lead with Culture
 

Database Technologies for Semantic Web

  • 1. Database Technologies for Semantic Web Edilson Ribeiro da Silva Júnior José Maria Silveira Neto    
  • 2. TripleStore  triple {subject, predicate, object}  TripleStore is an RDF Database.   add(triple), remove(triple), triples(pattern), ...    
  • 3. Sesame  An open­source framework for querying,  inferencing and analyzing RDF data.  SeSQL  BSD­style license  Java  PostgreSQL, MySQL, Microsoft SQL Server and  Oracle  databases.    
  • 4. SeSQL  Sesame RDF Query Languag, ”circle”  is a new RDF/RDFS query language that is currently  being developed by Aduna as part of Sesame. It  combines the best features of other (query)  languages (RQL, RDQL, N­Triples, N3) and adds  some of its own.   Graph transformation, RDF Schema support, XML  Schema datatype support, Expressive path  expression syntax, Optional path matching.    
  • 5. Virtuoso  object­relational SQL database  built­in web server  SPARQL  GPLv2  ODBC, JDBC, ADO .Net and OLE/DB.  C, Java, etc.    
  • 6. Jena  HP Labs Semantic Web Programme  RDF API  Reading and writing RDF in RDF/XML, N3 and N­ Triples  OWL API  In­memory and persistent storage  SPARQL query engine  Jena SDB  BSD­style license    
  • 7. R2RQ  D2RQ is a declarative language to describe  mappings between relational database schemata and  OWL/RDFS ontologies. The D2RQ Platform uses  these mapping to enables applications to access a  RDF­view on a non­RDF database through the Jena  and Sesame APIs, as well as over the Web via the  SPARQL Protocol and as Linked Data.    
  • 8. Large TripleStores  Garlik JXT (9.8B)  Jena with PostgreSQL (200M)  YARS2 (7B)  Kowari (160M)  BigOWLIM (1.85B)  3store with MySQL 3 (100M)  Virtuoso Open­Source Edition   Sesame (70M) (1B+)  AllegroGraph (1B)  Jena SDB (650M)  Mulgara (500M)  RDF gateway (262M)    
  • 9. References  http://rdflib.net/2005/10/26/rdflib­2.2.4/doc/triple_store.  http://en.wikipedia.org/wiki/Triplestore  http://recuperaciondeinformacion.itrello.com/SeRQL­en  http://esw.w3.org/topic/LargeTripleStores  http://www4.wiwiss.fu­berlin.de/bizer/D2RQ/