Jul 13, 2016 - Keywords. Semantic trajectory, data interlinking, Linked Open Data ... becoming the base of big data analysis in several application fields, from ..... dell'Olio. Palazzo. Vecchio. Stop 5. Ponte. Vecchio. End. 10:30 am. 11.01 am.
developer Mark Headd used data sets on location and space availability of child care centres to cre ... Mark describes t
cies, and many other cities are launching their own open data initiatives. .... The cost of developing systems to standa
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when using Linked Open Data for analysing and enriching statistics illustrated by a real life use case scenario. Keywords: Data Integration, Linked Open Data, ...
be queried by the same relational interface as the pre-existing relational database. The first method ... algorithm which decompose a select-project-join query into a set of local ... we have defined a Document Type Definition named SML (Semantic Mar
starting point. Once satisfied that all available datasets have been identified, the process of creating an ex- ternally
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Smart Innovation Set coordinated by. Dimitri Uzunidis. Volume 3. Big Data, Open Data and Data Development. Jean-Louis Mo
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Enriching Event Data with Geographic Context. Log Management and SIEM solutions provide numerous tools for automatically
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American Statistician,. 53, 132-136. Cobb, G.W. (1992). Teaching statistics. In L. Steen (Ed.), Heeding the call for change: Suggestions for curricular action (pp.
Jun 11, 2015 - Proposal for a Regulation of the European Parliament and of the Council on the protection of individuals with regard to the processing of personal data and on the free movement of such data (General Data Protection. Regulation). - Prep
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company names in plain text and amounts spent with each. Before we continue you might want to change the column titles t
Enriching Data In this exercise we are going to use data from Open Corporates to enrich a government financial transactions dataset. The purpose of this exercise is to reveal the power of linked data and how you can go about creating linked data from tabular, transactional data. The dataset we are going to be using is the Foreign and Commonwealth Office spend over £25,000 dataset. http://data.gov.uk/dataset/financial-transactions-data-fco In the making of the exercise the April 2010 dataset was used. The question we are going to try and answer is: “How many of the companies the FCO dealt with in ….. are still active, how many are dissolved and how many have been liquidated?”
Step 1 - Import into Refine The first stage is to import this data into Google/Open Refine.
Step 2 - Reconcile and link With the data now imported we have a column listing company names in plain text and amounts spent with each. Before we continue you might want to change the column titles to reflect the data.
The Open Data Institute - David Tarrant
In order to link the data to publicly available data we are going to use the Open Corporates reconciliation API. From the company name column drop down select the Start Reconcile option from the Reconcile menu. You will note that Open Corporates is not yet available as an option so a new standard reconciliation service will need to be added. The service URL is: https://opencorporates.com/reconcile
Once done, select this service and click Start Reconciling. When complete you will notice that each company may have several matches in Open Corporates, with each scored differently. You can click on each option to see a brief overview of that company and then tick which you believe is the correct option.
Rather than manually processing the entire dataset you can also filter to high or low quality matches using the facet on the left. You can also just choose to match all companies against their best candidate using the match option in the reconcile menus actions.
The Open Data Institute - David Tarrant
Step 3 - Get the data We now have a link to the Open Corporates page for each company that lists data about that company. It is the current status that we need in order to answer the original question. In order to fetch it for the companies we shall use the Open Corporates API in order to download the data for each company. This stage involves adding a column by fetching URLs. Although we could fetch the URI from step 4 using content negotiation, Refine does not understand HTTP redirection thus we would not get the data back from the URI. For this reason it is necessary to cut a corner and download the data directly from where the redirect would have sent Refine. Enter the following in the Expression box: “https://api.opencorporates.com” + cell.recon.match.id + “.json” When required to use an API key, enter the following expression replacing the APIKEY with the one made available by the course leader: “https://api.opencorporates.com” + cell.recon.match.id + “.json?api_token=APIKEY”
Make sure you name the column and turn down the throttle delay so that this process completes in reasonable time. The throttle delay is there so we don’t overwhelm an API with requests. Once done click OK and you should end up with a column full of JSON data from which we need to pick out the company status. In order to see what the JSON data looks like why not take one of the returned values and paste it into the JSON validator at jsonlint.com.
The Open Data Institute - David Tarrant
Step 4 - Extract the company status One last column to add based upon the data column this time with the following expression to parse the JSON data: value.parseJson()["results"]["company"]["current_status"]
Once complete you might want to collapse the data column so that you can see more rows on the screen. It is then possible to apply facets to find out how many companies are still active, liquidated, dissolved or other.
Extension Exercises Why not try extracting data other than current status from the Open Corporates data?
Can you link to any other data available from Open Corporates or suppliers of other reconciliation endpoints?