The domain model is fairly simple and straight forward. Each :Phone node is connected to at least one :Manufacturer, :Type, :OS and :Category node. Different 'weight' were given to different type of relation between these nodes. These 'weight' are the factors that will affect the ranking of the recommendation. It rates what people are more concerned with when considering for a new phone.
I’ve been working on Police UK Open Data dataset for quite some time. But it requires server side scripting to do the scraping and importing the data into database for further process. It was a pain. Fortunately Neo4j has now come with a data import functionality. Therefore I’m now taking this challenge to re-do a small part of my existing application using Neo4j. :)
brew install docker-machine-driver-xhyve
sudo chown root:wheel $(brew --prefix)/opt/docker-machine-driver-xhyve/bin/docker-machine-driver-xhyve
sudo chmod u+s $(brew --prefix)/opt/docker-machine-driver-xhyve/bin/docker-machine-driver-xhyve
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| // Available variables: | |
| // - Machine | |
| // - interpret | |
| // - assign | |
| // - send | |
| // - sendParent | |
| // - spawn | |
| // - raise | |
| // - actions |