RDF languages and storages part 1 - expressivness Maciej Janik Conrad Ibanez CSCI 8350, Fall 2004

Outline Comparison of RDF languages RQL Sesame implementation SquishQL - bases for RDQL Redland store

Sesame Web-based architecture Persistent RDF store use of traditional DBMS use of dedicated RDF triple storage Database independent Scalable architecture Query engine that implements RQL

Sesame - architecture Written in Java Modules: HTTP/SOAP handler Admin module Query module Export module Repository Abstraction Layer Use of PostgreSQL

Sesame - modules Admin module incrementaly add RDF/RDFS clearing repository schema operations recognise ‘type’, ‘subClassOf’, ‘subPropertyOf’ consistency checking adding inferred facts to repository RDF Export module export RDF to standard XML-serialized format

Sesame - modules Query module query plan and optimizer similar to already known DB solutions query is translated to a set of simple RAL calls each leaf of the query plan can ‘evaluate itself’ and pull data from RAL data are returned as streams lack of optimization on storage level

Sesame - modules RAL - Repository Abstraction Layer makes Sesame storage independent API supportes RDF Schema semantics (e.g. subsumption reasoning) can be stacked one on another interface oriented for persistance storage (DBMS, Object-Relational DB) data returned as streams can even use net-based RDF services (!) Due to poor performance, implemented cache as one of RALs cache mainly for RDFS, as it needs code support in reasoning (subClassOf,...)

Sesame - issues Due to portability (RAL) cannot optimize for underlying data storage Incremental uploads (schema) are slow due to rebuilding table in PostreSQL Scaled up to 400,000 statements (RDF from Wordnet) very loosely connected graph took 94 minutes (71 statements per second) Slow upload of new data due to lots of required database operations Query works slow due to the same issues

Redland, Rasqual, Raptor Storage for RDF triples - do not implement any language by itself This is the main module to include in RDF manipulation system Implemented in pure C for portability Rich API enables to build modules on top of it Rasqual - RDF query module RDQL SPARQL Raptor - a fast RDF parser

Redland Triple: Subject - Predicate - Object API enables retrieval of triples Highly optimized for performance Indexes SP 2 O - get target PO 2 S - get source SO 2 P - get relations between nodex P 2 SO - get nodes in relation S 2 P- get relations for subject

Redland - RDF Model stores Memory based memory double-linked list small models hashes - memory basic indexes on triples hashes - bdb - memory native storage with DBD hashes, no persistence Persistent hashes with BDB BDB hashes on disk native storage, scales tolow million of tuples 3store triplestore from AKT project not well supported mysql uses MYSQL DB

Redland - class diagram Efficient implementation of triple in memory use of pointers URI value separated Strict memory management - no leaks Abstraction of model to support different storages Fast parser / serializer

Redland API available in different languages C, C#, Java, Perl, Python, PHP, Ruby, Tcl API for manipulating triples, URI/literals, graphs Portable - can built in most OSes Scalable to handle millions of triples while using of persistent storage but indexing is very space-consuming Support for context and hierarchy of models

RDF languages and storages part 2 - indexing semi-structure data Maciej Janik Conrad Ibanez CSCI 8350, Fall 2004