iamprakashom

The Neo4j Knowledge Graph

Our friends of Neueda have been doing more and more work with Neo4j. One of the artefacts of that work (see their github repo for more info) has been an unbelievably wonderful page called Awesome Neo4j. This is a webpage with links and other resources that can be useful for people doing Neo4j projects - whether you are looking for tips and tricks, developer resources, language bindings, frameworks, visualization solutions, graph algorithm components, etc…​ all kinds of links are on this page…​ Truly great work of all the Contributors. And inspiring at that.

A Google Sheet as the main repository

Based on that work, I have tried to add some additional links that I use/know about on

iamprakashom

Using Zeppelin with Neo4j to analyse the FinCEN Files

Last week, we got another great and widely publicised case of Graph Databases' usefullness throw our way. The ICIJ published their FinCEN Files research, and on top of allowing you to explore the data on their website they also published an anonymised subset of the data as a series of CSV/JSON files. My friends and colleagues Michael Hunger, Will Lyon and the rest of the team, helped with the process of making this subset available as a Neo4j database (see this github repo), and there's even a super easy FinCEN Files Neo4j Sandbox that you can spin up in no time for some investigation fun.

So of course I had to take this data for a spin myself - it seems real

iamprakashom

#!/usr/bin/env python
import subprocess
import optparse
import platform

#---------------------------------------Globals----------------------------------------------------
install = []
PLATFORM = platform.system()
ARCHITECTURE = platform.architecture()[0]

iamprakashom

• http://ita.ee.lbl.gov/html/contrib/NASA-HTTP.html
• http://www.almhuette-raith.at/apache-log/access.log
• http://ita.ee.lbl.gov/html/traces.html
• https://www.google.co.uk/search?q=inurl:access.log+filetype:log

iamprakashom

import pandas as pd
import matplotlib.pyplot as plt
import seaborn
from sklearn.cluster import KMeans
import numpy as np
from scipy.spatial.distance import cdist, pdist

def eblow(df, n):
    kMeansVar = [KMeans(n_clusters=k).fit(df.values) for k in range(1, n)]
    centroids = [X.cluster_centers_ for X in kMeansVar]

iamprakashom

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Matlab code to produce PCA animations shown here:
% http://stats.stackexchange.com/questions/2691
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

% Static image

clear all
rng(42)

iamprakashom

Commands Linux-Terminal

• type : git clone --recursive https://github.com/dmlc/xgboost.git
• type : cd xgboost
• type : make
• type : cd python-package
• type : python setup.py install --user

How to use inside python scripts

iamprakashom

#include <opencv/cv.h>
#include <opencv/highgui.h>
#include <opencv/ml.h>

void doMosaic(IplImage* in, int x, int y,
              int width, int height, int size);

int main (int argc, char **argv)
{
  int i, c;

iamprakashom

install_opencv:
	sudo dnf install --best --allowerasing                             \
		cmake python-devel numpy gcc gcc-c++                             \
		python3-devel python3-numpy                                      \
		gtk2-devel libdc1394-devel libv4l-devel ffmpeg-devel             \
		gstreamer-plugins-base-devel libpng-devel libjpeg-turbo-devel    \
		jasper-devel openexr-devel libtiff-devel libwebp-devel           \
		tbb-devel eigen3-devel python-sphinx texlive git

	if [ ! -d opencv/ ]; then                                          \

iamprakashom

crypto = require('crypto');

#Quick MD5 of text
text = "MD5 this text!"
md5hash1 = crypto.createHash('md5').update(text).digest("hex")

#MD5 of text with updates
m = crypto.createHash('md5') 
m.update("MD5 ")
m.update("this ")