Mini Search Engine - Just the basics, using Neo4j, Crawler4j, Graphstream and Encog

Continuing to chapter 4 of Programming Collection Intelligence  (PCI) which is implementing a search engine.
I may have bitten off a little more than I should of in 1 exercise. Instead of using the normal relational database construct as used in the book, I figured, I always wanted to have a look at Neo4J so now was the time. Just to say, this isn't necessarily the ideal use case for a graph db, but how hard could to be to kill 3 birds with 1 stone.

Working through the tutorials trying to reset my SQL Server, Oracle mindset took a little longer than expected, but thankfully there are some great resources around Neo4j.

Just a couple:
neo4j - learn
Graph theory for busy developers
Graphdatabases

Since I just wanted to run this as a little exercise, I decided to go for a in memory implementation and not run it as a service on my machine. In hindsight this was probably a mistake and the tools and web interface would have helped me visualise my data graph quicker in the beginning.

As you can only have 1 writable instance of the in memory implementation, I made a little double lock singleton factory to create and clear the DB.


Then using Crawler4j created a graph of all the URLs starting with my blog, their relationships to other URLs and all the words and indexes of the words that those URLs contain.

After the data was collected, I could query it and perform the functions of a search engine. For this I decided to use java futures as it was another thing I had only read about and not yet implemented. In my day to day working environment we use Weblogic  / CommonJ work managers within the application server to perform the same task.
I then went about creating a task for each of the following counting the word frequency, document location, Page Rank and neural network (with fake input / training data) to rank the pages returned based on the search criteria. All the code is in my public github blog repo.

Disclaimer: The Neural Network task, either didn't have enough data to be affective, or I implemented the data normalisation incorrectly, so it is currently not very useful, I'll return to it once I have completed the journey through the while PCI book.

The one task worth sharing was the Page Rank one, I quickly read some of the theory for it, decided I am not that clever and went searching for a library that had it implemented. I discovered Graphstream a wonderful opensource project that does a WHOLE lot more than just PageRank, check out their video.

From that it was then simple to implement my PageRank task of this exercise.



In between all of this I found a great implementation of sorting a map by values on Stackoverflow.

The Maven dependencies used to implement all of this


Now to chapter 5 on PCI... Optimisation.