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Graphing Your Data
Or,
How I Stopped Worrying and Love the Triple
Store
Agenda
● What is Linked Data
● RDF
● Virtually Mapping Tuples to Triples
Some Problems...
● How to handle data reuse?
● How consistent is the data definition/model?
● How to join data across multiple data
sources?
Alternative Solutions...
● Data Governance
● Master Data Management
● Data Integration/Transformation
Alternative Problems!
● Data Governance and MDM can take years
before value is shown
● Data Transformation can be expensive
(time, resources) when moving to a data
warehouse
Semantic Web to the
Rescue
● Translate data into plain-language
● Follow standard vocabularies (and create
your own)
● Provides data in format that is:
– Secure
– Human and Machine readable
– Web-ready (it's the start of “Web 3.0”)
Semantic Web In Context
● Will not replace the data warehouse
– Intended for exploratory research
– Ideal for researchers and data analysts
An Example...
Graphing Your Data
An Example...
How the Data Formats
● 123 is a vocab:Student
● 456 is a vocab:Teacher
● 789 is a vocab:Class
● 123 vocab:hasFirstName Bob
● 456 vocab:hasFirstName Sarah
● 789 vocab:isTaughtBy 456
● 123 vocab:isRegistered 789
RDF/XML
Turtle
Triples
Converting Your Data
● ETL
– Many tools will support XML data conversion
● Database
– Some databases will port data into XML format
● Mapping Tool
– Real-time conversion layer between RDBMS
and Triples
Using Protégé
● Stanford ontology modeling tool
● Ontop plugin provides mapping layer
● Fastest, most agile way to map data into
triples.
Graphing Your Data
Graphing Your Data
Links
● Ontop - http://ontop.inf.unibz.it/
● Protege -
http://protege.stanford.edu/products.php
● Virtuoso - http://virtuoso.openlinksw.com/
● RDF/XML Tutorial-
http://www.w3schools.com/xml/xml_rdf.asp

More Related Content

Graphing Your Data

  • 1. Graphing Your Data Or, How I Stopped Worrying and Love the Triple Store
  • 2. Agenda ● What is Linked Data ● RDF ● Virtually Mapping Tuples to Triples
  • 3. Some Problems... ● How to handle data reuse? ● How consistent is the data definition/model? ● How to join data across multiple data sources?
  • 4. Alternative Solutions... ● Data Governance ● Master Data Management ● Data Integration/Transformation
  • 5. Alternative Problems! ● Data Governance and MDM can take years before value is shown ● Data Transformation can be expensive (time, resources) when moving to a data warehouse
  • 6. Semantic Web to the Rescue ● Translate data into plain-language ● Follow standard vocabularies (and create your own) ● Provides data in format that is: – Secure – Human and Machine readable – Web-ready (it's the start of “Web 3.0”)
  • 7. Semantic Web In Context ● Will not replace the data warehouse – Intended for exploratory research – Ideal for researchers and data analysts
  • 11. How the Data Formats ● 123 is a vocab:Student ● 456 is a vocab:Teacher ● 789 is a vocab:Class ● 123 vocab:hasFirstName Bob ● 456 vocab:hasFirstName Sarah ● 789 vocab:isTaughtBy 456 ● 123 vocab:isRegistered 789
  • 15. Converting Your Data ● ETL – Many tools will support XML data conversion ● Database – Some databases will port data into XML format ● Mapping Tool – Real-time conversion layer between RDBMS and Triples
  • 16. Using Protégé ● Stanford ontology modeling tool ● Ontop plugin provides mapping layer ● Fastest, most agile way to map data into triples.
  • 19. Links ● Ontop - http://ontop.inf.unibz.it/ ● Protege - http://protege.stanford.edu/products.php ● Virtuoso - http://virtuoso.openlinksw.com/ ● RDF/XML Tutorial- http://www.w3schools.com/xml/xml_rdf.asp