1 Mashup Workflow

2 What We Have

3 Challenges with REST APIs * Only ask what its built to answer * No standard - must relearn each time * Opaque - no way for consumers to see, reuse or improve the data model * Silos of Data - no linking at the data level * VeryTop Down

4 What We Want

5 Workflow * Take data (CSV, XSL) * Convert it to RDF * Load in triple store * Query with Sparql * Visualize results using standard off-the-shelf visualization tools (Google viz, Exhibit, etc)

6 Workflow

7 Sparql Proxy This is a web service that allows you to query any SPARQL endpoint, and get back the results in any format you want.

8 Example query: [required] encoded String of SPARQL query query-uri :[required] URI of SPARQL query (use as an alternative to "query" parameter. These two parameters are mutul-exclusive)

9 Example service-uri: [required] URI of SPARQL Endpoint output: output format. ''xml'' - SPARQL/XML (default) : ''exhibit'' - JSON for MIT Exhibit : ''gvds'' - JSON for Google Visualization : ''csv'' - CSV : ''html'' - HTML table : “sparql” - SPARQL JSON

10 Google Visualization Javascript library that allows use to send and visualize data on the fly. Many visualizations to choose from. Pretty easy to use.

11 Google Viz Load the appropriate Google Visualization API packages. Define a callback function for loading visualization code, which is called upon the loading of the HTML page.

12 Google vis Obtain data to pass to our Visualization instance. The Google Viz is designed to accept data in the form of specially- formatted JSON which can then be fed to a JSON processing function.

13 Google Viz Get back a response from the query processor, containing the data from the JSON file. Define a new data table to store the response data in. This way we can reorganize the data into the correct format

14 Google Viz Define a configuration for the Visualization instance containing information such as resolution. Give the Visualization object the new data, and tell it to draw.