| = (Product) Hierarchy GraphGist = | |
| This gist is a complement to http://blog.bruggen.com/2014/03/using-neo4j-to-manage-and-calculate.html[blogpost that I wrote] about managing hierarchical data structures in http://www.neo4j.org[neo4j].. | |
| In this example, we are using a "product hierarchy", essentially holding information about the composition of a product (what is it made of, how many of the components are used, and at the lowest level, what is the price of these components). The model looks like this: | |
| image::http://1.bp.blogspot.com/-XIjEXWHpNmc/Uzbhuoo-9xI/AAAAAAABNWE/7zYyn3Vl3i0/s3200/Screen+Shot+2014-03-29+at+16.04.35.png[] | |
| Note that in the graphgist, I have cut the tree depth to 5 levels (product to costs) instead of 6 in the blogpost - and that I also reduced the width of the tree to make it manageable in a gist. |
| = The Belgian Sitcom Graph: Who's Hot in "Thuis" = | |
| This gist is going to show you how you can query the elaborate landscape of relationships in http://www.een.be/programmas/thuis/[Thuis]. It is based on this picture by https://twitter.com/pieter_vh[@pieter_vh]. Very well done actually. | |
| image::http://qsdf.be/thuisopeen/infografiekthuis.png[scaledwidth="50%"] | |
| I took the picture and created a spreadsheet version of it - take a look https://docs.google.com/spreadsheets/d/1l1bfIU6qWvZpU0xeSjJoRBlKWxGCDdLaNlUjeR0r9V0/edit?usp=sharing[over here]. That allowed me to create the graph, which we will also setup in this Gist: | |
| //setup | |
| //hide | |
| [source,cypher] |
| //create them bones | |
| create (_0:`LEG`:`BONES` {`condition`:"dry", `id`:1, `name`:"toe bone", `side`:"left", `subname`:1}) | |
| create (_1:`LEG`:`BONES` {`condition`:"dry", `id`:2, `name`:"toe bone", `side`:"left", `subname`:2}) | |
| create (_2:`LEG`:`BONES` {`condition`:"dry", `id`:3, `name`:"toe bone", `side`:"left", `subname`:3}) | |
| create (_3:`LEG`:`BONES` {`condition`:"dry", `id`:4, `name`:"toe bone", `side`:"left", `subname`:4}) | |
| create (_4:`LEG`:`BONES` {`condition`:"dry", `id`:5, `name`:"toe bone", `side`:"left", `subname`:5}) | |
| create (_5:`LEG`:`BONES` {`condition`:"dry", `id`:6, `name`:"toe bone", `side`:"right", `subname`:1}) | |
| create (_6:`LEG`:`BONES` {`condition`:"dry", `id`:7, `name`:"toe bone", `side`:"right", `subname`:2}) | |
| create (_7:`LEG`:`BONES` {`condition`:"dry", `id`:8, `name`:"toe bone", `side`:"right", `subname`:3}) | |
| create (_8:`LEG`:`BONES` {`condition`:"dry", `id`:9, `name`:"toe bone", `side`:"right", `subname`:4}) |
| //DEM BONES queries | |
| // show dem bones | |
| match (n:BONES) return n; | |
| //condition of the bones | |
| match (n:BONES) return distinct n.condition | |
| //where is the lord? | |
| match (n {name:"Lord"}) return n |
This gist is a complement to blogpost that I wrote, and the presentation that I did at the London GraphPub about Graph Playlists and the Ultimate Dem Bones Demo. Let’s see if we can reproduce that over here.
Let’s play some music:
or if you don’t use Spotify, use this one:
| //the small dataset | |
| create (_0:`OBSERVATION_LOCATION` {`latitude`:50.737160, `location_id`:1378, `longitude`:-3.405790, `name`:"EXETER AIRPORT", `postcode`:"EX5 2"}), | |
| (_1:`OBSERVATION_LOCATION` {`latitude`:50.736540, `location_id`:57263, `longitude`:-3.532200, `name`:"EXETER UNIVERSITY", `postcode`:"EX4 4"}), | |
| (_2:`OBSERVATION_LOCATION` {`latitude`:58.453700, `location_id`:32, `longitude`:-3.089960, `name`:"WICK AIRPORT", `postcode`:"KW1 4"}), | |
| (_3:`OBSERVATION_LOCATION` {`latitude`:58.287710, `location_id`:44, `longitude`:-4.442370, `name`:"ALTNAHARRA NO 2", `postcode`:"IV27 4"}), | |
| (_4:`OBSERVATION_LOCATION` {`latitude`:57.612760, `location_id`:66, `longitude`:-5.306250, `name`:"KINLOCHEWE", `postcode`:"IV22 2"}), | |
| (_5:`OBSERVATION_LOCATION` {`latitude`:56.866560, `location_id`:105, `longitude`:-4.707970, `name`:"TULLOCH BRIDGE", `postcode`:"PH31 4"}), | |
| (_6:`OBSERVATION_LOCATION` {`latitude`:57.711260, `location_id`:137, `longitude`:-3.323350, `name`:"LOSSIEMOUTH", `postcode`:"IV31 6"}), | |
| (_7:`OBSERVATION_LOCATION |
| begin | |
| create (_0:`Race` {`distance`:"3663.5", `edition`:"101", `from`:"05/07/2014", `id`:1, `name`:"TOUR DE FRANCE", `number_of_stages`:"21", `to`:"27/07/2014", `website`:"http://www.letour.com/le-tour/2014/us/"}) | |
| create (_1:`Team` {`country`:"GBR", `id`:1, `name`:"TEAM SKY", `sportingDirectors`:"PORTAL Nicolas, KNAVEN Servais"}) | |
| create (_2:`Rider` {`country`:"GBR", `name`:"FROOME Christopher"}) | |
| create (_3:`Rider` {`country`:"AUT", `name`:"EISEL Bernhard"}) | |
| create (_4:`Rider` {`country`:"BLR", `name`:"KIRYIENKA Vasili"}) | |
| create (_5:`Rider` {`country`:"ESP", `name`:"LOPEZ GARCIA David"}) | |
| create (_6:`Rider` {`country`:"ESP", `name`:"NIEVE ITURRALDE Mikel"}) | |
| create (_7:`Rider` {`country`:"USA", `name`:"PATE Danny"}) | |
| create (_8:`Rider` {`country`:"AUS", `name`:"PORTE Richie"}) |
| //loading countries | |
| load csv with headers from "https://docs.google.com/a/neotechnology.com/spreadsheets/d/1yGt3jb-tnhPCCpNoyCgJV2ChcxoDToLal4GEQWkwvLI/export?format=csv&id=1yGt3jb-tnhPCCpNoyCgJV2ChcxoDToLal4GEQWkwvLI&gid=1243842209" | |
| as countries | |
| fieldterminator ',' | |
| merge (country:Country {short: countries.Short, cowcode: countries.COWcode, name: countries.Name}); | |
| //load some more detailed country data | |
| load csv with headers from "https://docs.google.com/a/neotechnology.com/spreadsheets/d/1yGt3jb-tnhPCCpNoyCgJV2ChcxoDToLal4GEQWkwvLI/export?format=csv&id=1yGt3jb-tnhPCCpNoyCgJV2ChcxoDToLal4GEQWkwvLI&gid=10793080" | |
| as countries | |
| fieldterminator ',' |
| //create song | |
| create (n:Song {name:'Waterloo'}); | |
| // create songparts | |
| match (s:Song {name:'Waterloo'}) create (s)-[:STARTS_WITH]->(sp:SongPart {name:'SongPart1', type:'Verse'}); | |
| match (sp1:SongPart {name:'SongPart1'}) create (sp1)-[:PRECEDES]->(sp2:SongPart {name:'SongPart2', type:'Chorus'}); | |
| match (sp2:SongPart {name:'SongPart2'}) create (sp2)-[:PRECEDES]->(sp3:SongPart {name:'SongPart3', type:'Verse'}); | |
| match (sp3:SongPart {name:'SongPart3'}) create (sp3)-[:PRECEDES]->(sp4:SongPart {name:'SongPart4', type:'Chorus'}); | |
| match (sp4:SongPart {name:'SongPart4'}) create (sp4)-[:PRECEDES]->(sp5:SongPart {name:'SongPart5', type:'Bridge'}); | |
| match (sp5:SongPart {name:'SongPart5'}) create (sp5)-[:PRECEDES]->(sp6:SongPart {name:'SongPart6', type:'Chorus'}); |
Yesterday night, I stumbled upon yet another interesting graph:
The "interactive" version of this image can be found on on this website: they basically try to interactively visualise the interplay of parties that are intervening in the many conflicts currently happening in the Middle East. It’s very, very well done.
So I did a little digging, and found that the original data is in a google doc that you can find over here. Look at the second tab: it’s basically an adjacency matrix of relationships between parties. In other words: a graph.