In spring 2016 the first press reports regarding the "panama papers" were released. With almost 3TB of raw data this was by far the largest leak of data worldwide. This talk gives some technical insights who the ICIJ (International Consortium Of Investigate Journalists) worked with that amount of data to provide journalist an easy to use interface for doing their research. Aside other technologies one core component was a graph database. In a live demo in the panama papers dataset we'll explore to power and conciseness of the graph query language "Cypher".

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7. +190 journalists in
more than 65 countries
12 staff members (USA, Costa Rica, Venezuela, Germany, France, Spain)
50% of the team = Data & Research Unit

8. raw files
metadata
author; sender...
database
search and
discovery
raw text

9. 3 million files
x
10 seconds per
file
=
1 yr / 35 servers
= 1.5 weeks

10. Lucene syntax
queries with
proximity matching!
400
users

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LOAD CSV WITH HEADERS FROM "url" AS row
MERGE (:Person {name:row.name,
age:toInt(row.age)});

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CALL apoc.load.json("url") yield value as doc
UNWIND doc.items as item
MERGE (:Contract {title:item.title,
amount:toFloat(item.amount)});

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bin/neo4j-import –-into people.db
--nodes:Person people.csv
--nodes:Company companies.csv
--relationship:STAKEHOLDER stakeholders.csv

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61. MATCH (boss)-[:MANAGES*0..3]->(sub),
(sub)-[:MANAGES*1..3]->(report)
WHERE boss.name = “John Doe”
RETURN sub.name AS Subordinate,
count(report) AS Total