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We are surrounded
by data             2013-02-06
                    Toronto Data Science Group




                                           1
We are surrounded by
MESSY data                                       2013-02-06
                                                 Toronto Data Science Group




 - Multiple standards and formats
        Structured vs unstructured
        Field nomination and format varies ...
 - Human Error (misspellings, errors, etc)
 - Non-normalized inputs (free-text entries, the
 “other" option)
 - Incomplete data (laziness)
 ....

                                                                        2
Lack of              2013-02-06
                     Toronto Data Science Group




 Time

          Skills

            »      Software

                                            3
OpenRefine the                         2013-02-06
                                       Toronto Data Science Group




 - Swiss army knife for data manipulation!

 - glue step between your IT systems




                                                              4
What's OpenRefine
(former Google Refine, former Gridworks)   2013-02-06
                                           Toronto Data Science Group




 - A Cross platform Web Application that runs
 locally

 - A Community based project hosted on GitHub

 - Which have two distributions and multiple
 extensions

 - Something between a spreadsheet and SQL

                                                                  5
Three use case                         2013-02-06
                                       Toronto Data Science Group




1. Data Cleaning


2. ETL (Extract Transform Load) Prototyping


3. Data extension (reconciliation & linked data)




                                                              6
#1 Data Cleaning                    2013-02-06
                                    Toronto Data Science Group




 Graphical interface   Cluster similar record
 Facet option          Support three languages:
                         - GREL Jyton, Clojure
                         + regex




                                                           7
Facet example   2013-02-06
                Toronto Data Science Group




                                       8
Cluster example   2013-02-06
                  Toronto Data Science Group




                                         9
#2 ETL Prototyping
(Extract – Transform - Load)               2013-02-06
                                           Toronto Data Science Group




  Extract & Load               Transform
  Support:                     - Understand your data
  - tabular (csv, xls)         - Test the
                                 transformation that
  - hierarchical (xml, json)     need to be done
                               - Undo / Redo
                               - Export transformation
                                 in JSON format
                               - Automate using the
                                 python or ruby
                                 extension                        10
History and JSON export   2013-02-06
                          Toronto Data Science Group




                                                 11
#3 Extend your Data
(reconciliation & linked data)                 2013-02-06
                                               Toronto Data Science Group




- Cross between                  Reconcile against
  OpenRefine projects            - RDF file & Local SPARQL
  (vlookup)                        endpoints
- Fetch URL and           - Online databases
  call web services (API)




                                                                      12
Reconciliation example   2013-02-06
                         Toronto Data Science Group




                                                13
2013-02-06
                                      Toronto Data Science Group




   Thanks!
Martin Magdinier             OpenRefine
martin.magdinier@gmail.com http://openrefine.org
@magdmartin                  @OpenRefine




                                                             14

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20130206 open refine

  • 1. We are surrounded by data 2013-02-06 Toronto Data Science Group 1
  • 2. We are surrounded by MESSY data 2013-02-06 Toronto Data Science Group - Multiple standards and formats Structured vs unstructured Field nomination and format varies ... - Human Error (misspellings, errors, etc) - Non-normalized inputs (free-text entries, the “other" option) - Incomplete data (laziness) .... 2
  • 3. Lack of 2013-02-06 Toronto Data Science Group Time Skills » Software 3
  • 4. OpenRefine the 2013-02-06 Toronto Data Science Group - Swiss army knife for data manipulation! - glue step between your IT systems 4
  • 5. What's OpenRefine (former Google Refine, former Gridworks) 2013-02-06 Toronto Data Science Group - A Cross platform Web Application that runs locally - A Community based project hosted on GitHub - Which have two distributions and multiple extensions - Something between a spreadsheet and SQL 5
  • 6. Three use case 2013-02-06 Toronto Data Science Group 1. Data Cleaning 2. ETL (Extract Transform Load) Prototyping 3. Data extension (reconciliation & linked data) 6
  • 7. #1 Data Cleaning 2013-02-06 Toronto Data Science Group Graphical interface Cluster similar record Facet option Support three languages: - GREL Jyton, Clojure + regex 7
  • 8. Facet example 2013-02-06 Toronto Data Science Group 8
  • 9. Cluster example 2013-02-06 Toronto Data Science Group 9
  • 10. #2 ETL Prototyping (Extract – Transform - Load) 2013-02-06 Toronto Data Science Group Extract & Load Transform Support: - Understand your data - tabular (csv, xls) - Test the transformation that - hierarchical (xml, json) need to be done - Undo / Redo - Export transformation in JSON format - Automate using the python or ruby extension 10
  • 11. History and JSON export 2013-02-06 Toronto Data Science Group 11
  • 12. #3 Extend your Data (reconciliation & linked data) 2013-02-06 Toronto Data Science Group - Cross between Reconcile against OpenRefine projects - RDF file & Local SPARQL (vlookup) endpoints - Fetch URL and - Online databases call web services (API) 12
  • 13. Reconciliation example 2013-02-06 Toronto Data Science Group 13
  • 14. 2013-02-06 Toronto Data Science Group Thanks! Martin Magdinier OpenRefine martin.magdinier@gmail.com http://openrefine.org @magdmartin @OpenRefine 14