SlideShare una empresa de Scribd logo
1 de 27
MPTStore:  A Fast, Scalable, and Stable Resource Index Aaron Birkland and Chris Wilper Open Repositories 2007 San Antonio, TX
Background: RDF in Fedora ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Background: RDF in Fedora ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Motivation: The NSDL Use Case ,[object Object],[object Object],[object Object]
Motivation: The NSDL Use Case ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Motivation: The NSDL Use Case ,[object Object],[object Object],[object Object]
Motivation: The NSDL Use Case ,[object Object],[object Object],[object Object]
<foxml:datastream ID=”RELS-EXT” ...> ... <example:id>PLUGH-XYZZY</example:id> <example:memberOf rdf:resource=”info:fedora/demo:73” /> </foxml: datastream > ... Must be globally unique <example:objectType>Resource</example:objectType> This object... 1) Must exist 2) Must be 'Active' 3) Must be objectType 'Aggregation'
Motivation: The NSDL Use Case ,[object Object],[object Object],[object Object],[object Object],[object Object]
The Challenge ,[object Object],[object Object],[object Object]
The Challenge ,[object Object],[object Object],[object Object],[object Object],[object Object]
The Challenge ,[object Object],[object Object],[object Object],[object Object],[object Object]
The Challenge ,[object Object],[object Object],[object Object],[object Object]
Our Solution ,[object Object],[object Object],[object Object]
<info:fedora/demo:1> <info:fedora/demo:2> <info:fedora/demo:3> <info:fedora/demo:4> s o t1 <info:fedora/fedora-def:model#disseminates> <http://ns.example.org/rels#memberOf> 1 2 p pkey tmap Triples   Predicate Mapping
Our Solution ,[object Object],[object Object],[object Object],[object Object],[object Object]
Our Solution ,[object Object],[object Object],[object Object],[object Object]
Our Solution ,[object Object],[object Object],[object Object],[object Object]
Our Solution ,[object Object],[object Object],[object Object],[object Object]
Our Solution ,[object Object],[object Object],[object Object]
Results ,[object Object],[object Object],[object Object]
Results ,[object Object],[object Object],[object Object]
Results ,[object Object],[object Object]
Fast, Immediate Updates ,[object Object],[object Object],[object Object]
RI: Future Directions ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
RI: Future Directions ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Thank You ,[object Object],[object Object],[object Object],[object Object]

Más contenido relacionado

La actualidad más candente

Designing your SaaS Database for Scale with Postgres
Designing your SaaS Database for Scale with PostgresDesigning your SaaS Database for Scale with Postgres
Designing your SaaS Database for Scale with Postgres
Ozgun Erdogan
 
Hadoop Adventures At Spotify (Strata Conference + Hadoop World 2013)
Hadoop Adventures At Spotify (Strata Conference + Hadoop World 2013)Hadoop Adventures At Spotify (Strata Conference + Hadoop World 2013)
Hadoop Adventures At Spotify (Strata Conference + Hadoop World 2013)
Adam Kawa
 
RedisSearch / CRDT: Kyle Davis, Meir Shpilraien
RedisSearch / CRDT: Kyle Davis, Meir ShpilraienRedisSearch / CRDT: Kyle Davis, Meir Shpilraien
RedisSearch / CRDT: Kyle Davis, Meir Shpilraien
Redis Labs
 
Search Lucene
Search LuceneSearch Lucene
Search Lucene
Jeremy Coates
 
Handling Data in Mega Scale Web Systems
Handling Data in Mega Scale Web SystemsHandling Data in Mega Scale Web Systems
Handling Data in Mega Scale Web Systems
Vineet Gupta
 
STAT Requirement Analysis
STAT Requirement AnalysisSTAT Requirement Analysis
STAT Requirement Analysis
stat
 
Realtime Analytics and Anomalities Detection using Elasticsearch, Hadoop and ...
Realtime Analytics and Anomalities Detection using Elasticsearch, Hadoop and ...Realtime Analytics and Anomalities Detection using Elasticsearch, Hadoop and ...
Realtime Analytics and Anomalities Detection using Elasticsearch, Hadoop and ...
DataWorks Summit
 

La actualidad más candente (20)

Optimizing Presto Connector on Cloud Storage
Optimizing Presto Connector on Cloud StorageOptimizing Presto Connector on Cloud Storage
Optimizing Presto Connector on Cloud Storage
 
Designing your SaaS Database for Scale with Postgres
Designing your SaaS Database for Scale with PostgresDesigning your SaaS Database for Scale with Postgres
Designing your SaaS Database for Scale with Postgres
 
Portable Lucene Index Format & Applications - Andrzej Bialecki
Portable Lucene Index Format & Applications - Andrzej BialeckiPortable Lucene Index Format & Applications - Andrzej Bialecki
Portable Lucene Index Format & Applications - Andrzej Bialecki
 
Improved Search with Lucene 4.0 - Robert Muir
Improved Search with Lucene 4.0 - Robert MuirImproved Search with Lucene 4.0 - Robert Muir
Improved Search with Lucene 4.0 - Robert Muir
 
Hadoop Adventures At Spotify (Strata Conference + Hadoop World 2013)
Hadoop Adventures At Spotify (Strata Conference + Hadoop World 2013)Hadoop Adventures At Spotify (Strata Conference + Hadoop World 2013)
Hadoop Adventures At Spotify (Strata Conference + Hadoop World 2013)
 
RedisSearch / CRDT: Kyle Davis, Meir Shpilraien
RedisSearch / CRDT: Kyle Davis, Meir ShpilraienRedisSearch / CRDT: Kyle Davis, Meir Shpilraien
RedisSearch / CRDT: Kyle Davis, Meir Shpilraien
 
Building a Large Scale SEO/SEM Application with Apache Solr
Building a Large Scale SEO/SEM Application with Apache SolrBuilding a Large Scale SEO/SEM Application with Apache Solr
Building a Large Scale SEO/SEM Application with Apache Solr
 
PyConDE / PyData Karlsruhe 2017 – Connecting PyData to other Big Data Landsca...
PyConDE / PyData Karlsruhe 2017 – Connecting PyData to other Big Data Landsca...PyConDE / PyData Karlsruhe 2017 – Connecting PyData to other Big Data Landsca...
PyConDE / PyData Karlsruhe 2017 – Connecting PyData to other Big Data Landsca...
 
Finite State Queries In Lucene
Finite State Queries In LuceneFinite State Queries In Lucene
Finite State Queries In Lucene
 
Search Lucene
Search LuceneSearch Lucene
Search Lucene
 
Handling Data in Mega Scale Web Systems
Handling Data in Mega Scale Web SystemsHandling Data in Mega Scale Web Systems
Handling Data in Mega Scale Web Systems
 
2011 06-30-hadoop-summit v5
2011 06-30-hadoop-summit v52011 06-30-hadoop-summit v5
2011 06-30-hadoop-summit v5
 
MongoDB Capacity Planning
MongoDB Capacity PlanningMongoDB Capacity Planning
MongoDB Capacity Planning
 
Case study of Rujhaan.com (A social news app )
Case study of Rujhaan.com (A social news app )Case study of Rujhaan.com (A social news app )
Case study of Rujhaan.com (A social news app )
 
STAT Requirement Analysis
STAT Requirement AnalysisSTAT Requirement Analysis
STAT Requirement Analysis
 
Realtime Analytics and Anomalities Detection using Elasticsearch, Hadoop and ...
Realtime Analytics and Anomalities Detection using Elasticsearch, Hadoop and ...Realtime Analytics and Anomalities Detection using Elasticsearch, Hadoop and ...
Realtime Analytics and Anomalities Detection using Elasticsearch, Hadoop and ...
 
Big Data Anti-Patterns: Lessons From the Front LIne
Big Data Anti-Patterns: Lessons From the Front LIneBig Data Anti-Patterns: Lessons From the Front LIne
Big Data Anti-Patterns: Lessons From the Front LIne
 
Gruter TECHDAY 2014 Realtime Processing in Telco
Gruter TECHDAY 2014 Realtime Processing in TelcoGruter TECHDAY 2014 Realtime Processing in Telco
Gruter TECHDAY 2014 Realtime Processing in Telco
 
Massive parallel processing database systems mpp
Massive parallel processing database systems mppMassive parallel processing database systems mpp
Massive parallel processing database systems mpp
 
NoSQL databases pros and cons
NoSQL databases pros and consNoSQL databases pros and cons
NoSQL databases pros and cons
 

Destacado

Open Repositories 2011 - DuraSpace Plenary - Fedora Roadmap
Open Repositories 2011 - DuraSpace Plenary - Fedora RoadmapOpen Repositories 2011 - DuraSpace Plenary - Fedora Roadmap
Open Repositories 2011 - DuraSpace Plenary - Fedora Roadmap
Chris Wilper
 
remember when?
remember when?remember when?
remember when?
flash911uk
 

Destacado (8)

Why Are We Afraid of Death?
Why Are We Afraid of Death?Why Are We Afraid of Death?
Why Are We Afraid of Death?
 
sport pp
sport ppsport pp
sport pp
 
Open Repositories 2011 - DuraSpace Plenary - Fedora Roadmap
Open Repositories 2011 - DuraSpace Plenary - Fedora RoadmapOpen Repositories 2011 - DuraSpace Plenary - Fedora Roadmap
Open Repositories 2011 - DuraSpace Plenary - Fedora Roadmap
 
remember when?
remember when?remember when?
remember when?
 
La Logosynthèse en bref
La Logosynthèse en brefLa Logosynthèse en bref
La Logosynthèse en bref
 
Vous avez la grippe
Vous avez la grippeVous avez la grippe
Vous avez la grippe
 
Logosintesi in un guscio di Noce
Logosintesi in un guscio di NoceLogosintesi in un guscio di Noce
Logosintesi in un guscio di Noce
 
humour
humourhumour
humour
 

Similar a MPTStore: A Fast, Scalable, and Stable Resource Index

CS 542 Parallel DBs, NoSQL, MapReduce
CS 542 Parallel DBs, NoSQL, MapReduceCS 542 Parallel DBs, NoSQL, MapReduce
CS 542 Parallel DBs, NoSQL, MapReduce
J Singh
 
Dr.Hadoop- an infinite scalable metadata management for Hadoop-How the baby e...
Dr.Hadoop- an infinite scalable metadata management for Hadoop-How the baby e...Dr.Hadoop- an infinite scalable metadata management for Hadoop-How the baby e...
Dr.Hadoop- an infinite scalable metadata management for Hadoop-How the baby e...
Dipayan Dev
 

Similar a MPTStore: A Fast, Scalable, and Stable Resource Index (20)

Architecture by Accident
Architecture by AccidentArchitecture by Accident
Architecture by Accident
 
CS 542 Parallel DBs, NoSQL, MapReduce
CS 542 Parallel DBs, NoSQL, MapReduceCS 542 Parallel DBs, NoSQL, MapReduce
CS 542 Parallel DBs, NoSQL, MapReduce
 
Architectural anti-patterns for data handling
Architectural anti-patterns for data handlingArchitectural anti-patterns for data handling
Architectural anti-patterns for data handling
 
Front Range PHP NoSQL Databases
Front Range PHP NoSQL DatabasesFront Range PHP NoSQL Databases
Front Range PHP NoSQL Databases
 
disertation
disertationdisertation
disertation
 
Voldemort & Hadoop @ Linkedin, Hadoop User Group Jan 2010
Voldemort & Hadoop @ Linkedin, Hadoop User Group Jan 2010Voldemort & Hadoop @ Linkedin, Hadoop User Group Jan 2010
Voldemort & Hadoop @ Linkedin, Hadoop User Group Jan 2010
 
Hadoop and Voldemort @ LinkedIn
Hadoop and Voldemort @ LinkedInHadoop and Voldemort @ LinkedIn
Hadoop and Voldemort @ LinkedIn
 
NoSQL Introduction, Theory, Implementations
NoSQL Introduction, Theory, ImplementationsNoSQL Introduction, Theory, Implementations
NoSQL Introduction, Theory, Implementations
 
Hardware Provisioning
Hardware ProvisioningHardware Provisioning
Hardware Provisioning
 
Overview of MongoDB and Other Non-Relational Databases
Overview of MongoDB and Other Non-Relational DatabasesOverview of MongoDB and Other Non-Relational Databases
Overview of MongoDB and Other Non-Relational Databases
 
Architectural anti patterns_for_data_handling
Architectural anti patterns_for_data_handlingArchitectural anti patterns_for_data_handling
Architectural anti patterns_for_data_handling
 
MinneBar 2013 - Scaling with Cassandra
MinneBar 2013 - Scaling with CassandraMinneBar 2013 - Scaling with Cassandra
MinneBar 2013 - Scaling with Cassandra
 
MySQL And Search At Craigslist
MySQL And Search At CraigslistMySQL And Search At Craigslist
MySQL And Search At Craigslist
 
Hadoop training in bangalore
Hadoop training in bangaloreHadoop training in bangalore
Hadoop training in bangalore
 
Elastic search from the trenches
Elastic search from the trenchesElastic search from the trenches
Elastic search from the trenches
 
Nosql databases
Nosql databasesNosql databases
Nosql databases
 
NoSql Databases
NoSql DatabasesNoSql Databases
NoSql Databases
 
Dr.Hadoop- an infinite scalable metadata management for Hadoop-How the baby e...
Dr.Hadoop- an infinite scalable metadata management for Hadoop-How the baby e...Dr.Hadoop- an infinite scalable metadata management for Hadoop-How the baby e...
Dr.Hadoop- an infinite scalable metadata management for Hadoop-How the baby e...
 
The future of Big Data tooling
The future of Big Data toolingThe future of Big Data tooling
The future of Big Data tooling
 
No sq lv1_0
No sq lv1_0No sq lv1_0
No sq lv1_0
 

Último

Último (20)

Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
 
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
 
Apidays New York 2024 - The value of a flexible API Management solution for O...
Apidays New York 2024 - The value of a flexible API Management solution for O...Apidays New York 2024 - The value of a flexible API Management solution for O...
Apidays New York 2024 - The value of a flexible API Management solution for O...
 
🐬 The future of MySQL is Postgres 🐘
🐬  The future of MySQL is Postgres   🐘🐬  The future of MySQL is Postgres   🐘
🐬 The future of MySQL is Postgres 🐘
 
Strategies for Landing an Oracle DBA Job as a Fresher
Strategies for Landing an Oracle DBA Job as a FresherStrategies for Landing an Oracle DBA Job as a Fresher
Strategies for Landing an Oracle DBA Job as a Fresher
 
MINDCTI Revenue Release Quarter One 2024
MINDCTI Revenue Release Quarter One 2024MINDCTI Revenue Release Quarter One 2024
MINDCTI Revenue Release Quarter One 2024
 
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
 
Top 5 Benefits OF Using Muvi Live Paywall For Live Streams
Top 5 Benefits OF Using Muvi Live Paywall For Live StreamsTop 5 Benefits OF Using Muvi Live Paywall For Live Streams
Top 5 Benefits OF Using Muvi Live Paywall For Live Streams
 
Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood
Polkadot JAM Slides - Token2049 - By Dr. Gavin WoodPolkadot JAM Slides - Token2049 - By Dr. Gavin Wood
Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood
 
AWS Community Day CPH - Three problems of Terraform
AWS Community Day CPH - Three problems of TerraformAWS Community Day CPH - Three problems of Terraform
AWS Community Day CPH - Three problems of Terraform
 
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
 
Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...
Workshop - Best of Both Worlds_ Combine  KG and Vector search for  enhanced R...Workshop - Best of Both Worlds_ Combine  KG and Vector search for  enhanced R...
Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...
 
Artificial Intelligence Chap.5 : Uncertainty
Artificial Intelligence Chap.5 : UncertaintyArtificial Intelligence Chap.5 : Uncertainty
Artificial Intelligence Chap.5 : Uncertainty
 
HTML Injection Attacks: Impact and Mitigation Strategies
HTML Injection Attacks: Impact and Mitigation StrategiesHTML Injection Attacks: Impact and Mitigation Strategies
HTML Injection Attacks: Impact and Mitigation Strategies
 
Manulife - Insurer Innovation Award 2024
Manulife - Insurer Innovation Award 2024Manulife - Insurer Innovation Award 2024
Manulife - Insurer Innovation Award 2024
 
Boost PC performance: How more available memory can improve productivity
Boost PC performance: How more available memory can improve productivityBoost PC performance: How more available memory can improve productivity
Boost PC performance: How more available memory can improve productivity
 
presentation ICT roal in 21st century education
presentation ICT roal in 21st century educationpresentation ICT roal in 21st century education
presentation ICT roal in 21st century education
 
Real Time Object Detection Using Open CV
Real Time Object Detection Using Open CVReal Time Object Detection Using Open CV
Real Time Object Detection Using Open CV
 
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
 
Understanding Discord NSFW Servers A Guide for Responsible Users.pdf
Understanding Discord NSFW Servers A Guide for Responsible Users.pdfUnderstanding Discord NSFW Servers A Guide for Responsible Users.pdf
Understanding Discord NSFW Servers A Guide for Responsible Users.pdf
 

MPTStore: A Fast, Scalable, and Stable Resource Index

  • 1. MPTStore: A Fast, Scalable, and Stable Resource Index Aaron Birkland and Chris Wilper Open Repositories 2007 San Antonio, TX
  • 2.
  • 3.
  • 4.
  • 5.
  • 6.
  • 7.
  • 8. <foxml:datastream ID=”RELS-EXT” ...> ... <example:id>PLUGH-XYZZY</example:id> <example:memberOf rdf:resource=”info:fedora/demo:73” /> </foxml: datastream > ... Must be globally unique <example:objectType>Resource</example:objectType> This object... 1) Must exist 2) Must be 'Active' 3) Must be objectType 'Aggregation'
  • 9.
  • 10.
  • 11.
  • 12.
  • 13.
  • 14.
  • 15. <info:fedora/demo:1> <info:fedora/demo:2> <info:fedora/demo:3> <info:fedora/demo:4> s o t1 <info:fedora/fedora-def:model#disseminates> <http://ns.example.org/rels#memberOf> 1 2 p pkey tmap Triples Predicate Mapping
  • 16.
  • 17.
  • 18.
  • 19.
  • 20.
  • 21.
  • 22.
  • 23.
  • 24.
  • 25.
  • 26.
  • 27.