Note: this repository is very big (> 1 GB). Cloning it will take a while and require a lot of disk space.
This repository contains datasets organized in graphs to be used with ArangoDB.
More about ArangoDB and graphs: Graph documentation
The "RandomUsers" directory contains files with random users.
{
"name": {
"first": "Diedre",
"last": "Clinton"
},
"gender": "female",
"birthday": "1959-11-06",
"contact": {
"address": {
"street": "2 Fraser Ave",
"zip": "81223",
"city": "Cotopaxi",
"state":"CO"
},
"email": ["diedre.clinton@nosql-matters.org",
"clinton@nosql-matters.org",
"diedre@nosql-matters.org"],
"region": "719",
"phone": ["719-7055896"]
},
"likes": ["swimming"],
"memberSince":"2009-03-14"
}
In order to import these users, use:
arangoimp --file names_XXX.json --collection=users --create-collection=true --type=json
where XXX is 100, 1000, 10000, 100000, 200000, 300000.
The Cities directory contains a list of cities with geo information. There are roughly 320000 cities.
locId,country,region,city,postalCode,latitude,longitude,metroCode,areaCode
1,"O1","","","",0.0000,0.0000,,
In order to import these cities, use
arangoimp --file GeoLiteCity.csv --collection=cities --create-collection=true --type=csv
The Countries directory contains a list of countries with wikipedia links. There are 241 contries.
"id","code","name","continent","wikipedia_link","keywords"
1,"AD","Andorra","EU","http://en.wikipedia.org/wiki/Andorra",
In order to import these countries, use
arangoimp --file countries.csv --collection=countries --create-collection=true --type=csv
There are roughly 4100 regions with wikipedia links.
"id","code","local_code","name","continent","iso_country","wikipedia_link","keywords"
1,"AD-02",02,"Canillo","EU","AD","http://en.wikipedia.org/wiki/Canillo",
In order to import these regions, use
arangoimp --file regions.csv --collection=regions --create-collection=true --type=csv
There are roughly 1200 geo coordinates for McDonalds in France.
lat,long
42.524330,2.833970
In order to import these, use
arangoimp --file france.csv --collection=mcdonalds --create-collection=true --type=csv
The Bezirke directory contains a list of German counties with geo information. There are 169431 Bezirke.
RC,UFI,UNI,LAT,LONG,DMS_LAT,DMS_LONG,MGRS,JOG,FC,DSG,PC,CC1,ADM1,POP,ELEV,CC2,NT,LC,SHORT_FORM,GENERIC,SORT_NAME_RO,FULL_NAME_RO,FULL_NAME_ND_RO,SORT_NAME_RG,FULL_NAME_RG,FULL_NAME_ND_RG,NOTE,MODIFY_DATE
1,6132652,6143433,52.5,13.283333,523000,131700,33UUU8347218037,NN33-10,A,ADM2,,GM,16,,,,N,,Charlottenburg-Wilmersdorf,Bezirk,BEZIRKCHARLOTTENBURGWILMERSDORF,Bezirk Charlottenburg-Wilmersdorf,Bezirk Charlottenburg-Wilmersdorf,CHARLOTTENBURGWILMERSDORF BEZIRK,"Charlottenburg-Wilmersdorf, Bezirk","Charlottenburg-Wilmersdorf, Bezirk",,2001-12-20
In order to import these counties, use
arangoimp --file bezirke.csv --collection=bezirke --create-collection=true --type=csv
The Airports directory contains a list of airports with geo information. There are roughly 44000 airports.
"id","ident","type","name","latitude_deg","longitude_deg","elevation_ft","continent","iso_country","iso_region","municipality","scheduled_service","gps_code","iata_code","local_code","home_link","wikipedia_link","keywords"
6523,"00A","heliport","Total Rf Heliport",40.07080078125,-74.9336013793945,11,"NA","US","US-PA","Bensalem","no","00A",,"00A",,,
In order to import these airports, use
arangoimp --file airports.csv --collection=airports --create-collection=true --type=csv
wikiimporter is a converter for Wikipedia dumps written by Sebastian Cohnen in Ruby, see https://github.com/tisba/wikiimporter. Downloading the wikipedia dump will take some time - it is roughly 2.5 GByte.
cd wikiimporter
mkdir data
mkdir log
sudo bundle install
curl `./bin/getlatestdumpurl.rb` -o data/wiki.xml.bz2
bzcat data/wiki.xml.bz2 | ./bin/wikixml2json.rb --max-pages 10000 > data/articles.json
arangoimp --file data/articles.json --collection=wiki --create-collection=true --type=json
See https://github.com/Nerds/NerdPursuit for details. Each question is stored in its own file. So, you must create a file with all questions first:
./nerd_pursuit_compress.sh
and then import the generated file using
arangoimp --file nerd_pursuit_compressed.json --collection=nerds --create-collection=true --type=json
The IPRanges directory contains IP address ranges and geo information. There are 3.7 Million ranges.
{
"locId" : "17",
"endIpNum" : "16777471",
"startIpNum" : "16777216",
"geo" : [ -27, 133 ]
}
In order to import these locations, use:
arangoimp --file geoblocks.json --collection=ip_ranges --create-collection=true --type=json
Download the data
./dblp-download.sh
this will create an XML file dblp.xml (roughly 1.1 GByte).
python dblp2json.py dblp.xml > dblp.json
converts the file to json
The Graphs/AirlineCompany directory contains a subset of the Airports and flight routes of an imaginary airline company among them. Most of the flights are starting from Cologne Airport (CGN).
In order to import this data use
unix> arangorestore --input-directory "<path-to>/AirlineCompany"
If you want to create a graph for this data use
unix> arangosh
arangosh> var Graph = require("org/arangodb/graph").Graph;
arangosh> new Graph("Airline", "airports", "flights");
The Graphs/IMDB contains a dataset taken form IMDB http://www.imdb.com.
In order to import this data execute the following command:
unix> arangosh
arangosh> require("internal").load("Graphs/IMDB/import.js");This dataset has been used for the screencast of the graph visualisation tool.
The debian linux distribution consists of packages, which relate to each others by dependencies, which demand or recommend other packages to be installed. Also conflicts are a possible relation, which prohibits two packages to be installed at once. The script used to gather this graph data is available alongside with pyarango. However, it takes a while to translate the debian package database into arangodb documents, so here is a dump of the Debian Jessie package database.
Since this is a dump of a complete database, you can use arangorestore to import this. We will create an own database debianGraph so it doesn't interfere with your existing data:
unix> arangorestore --input-directory DebianDependencyGraph/ \
--create-collection true \
--include-system-collections true \
--create-database true \
--server.database debianGraph
Using the ArangoDB graph viewer, we can browse random starting points in the graph:
Small, multi-purpose dataset including a small graph of parents and children (Characters --ChildOf--> Characters).
See README for details.
Amazon product co-purchasing network metadata (summer 2006) with product metadata for over half a million different products from different categories: https://snap.stanford.edu/data/amazon-meta.html