## Pages

### Using 3D Visualization to Debug a Graph-based Algorithm

Recently I have been working on an idea for an algorithm that discovers patterns in raw streams of data. This pattern recognition algorithm uses deep learning to classify certain combinatorial features that uniquely identify an input stream.

I'm going to first talk a bit about the algorithm so it makes sense as to why visualization is such an important step in iterating and tweaking code that most efficiently implements the algorithm.

### The Algorithm

In a previous post I introduce the idea for the algorithm and how a graph-based approach might work.

### Hierarchical Pattern Recognition

About a year ago I read about Ray Kurzweil's "Pattern Recognition Theory of Mind", which he articulates in his book, "How to Create a Mind". I picked up the book after struggling with the idea of implementing a deep learning algorithm for parsing natural language text on Wikipedia. My goal was to discover links in volumes of text that were not already linked. I ended up developing all kinds of cool heuristics to do this, mostly by a lot of trial and error. King of these heuristics was a pretty simple algorithm at the core of the library that would find redundancies in batches of text content.

How this worked is if a phrase was mentioned repeatedly in a collection of about 50 sentences, then I could extract that phrase as a node and link it back to the pieces of content it belonged to. Every now and then you'll get a reference to another article's name, which can then be verified against Wikipedia's site index, which would provide more sentences to find repeated phrases within.

I struggled with persistency because I knew how ugly my problem was for a relational database. I created some entity-relationship models, and implemented them using Entity Framework over Microsoft SQL Server. It worked, kind of. I waited patiently to happen upon a better solution. Thankfully I did, and using a graph database I was able to take my cool little algorithms and solve my persistency problem at scale.

### Building a Neo4j Reporting Service Part II

It is a capital mistake to theorize before one has data. Insensibly one begins to twist facts to suit theories, instead of theories to suit facts.

Sir Arthur Conan Doyle, Author of Sherlock Holmes stories
 A Subgraph From Neo4j's Browser
Just as Sir Arthur Conan Doyle's character, Sherlock Holmes, manically collects facts and evidence to prove theories, we find ourselves doing much of the same today except on a much larger scale web scale. The web is an ever growing expanse of facts and evidence. It is at our disposal to observe without much of a challenge, but to store it and retrieve it in a way that answers the big questions, that's challenging.

Continuing on from Building a Graph-based Reporting Platform: Part I, I posed some questions related to understanding how to build great community experiences around Neo4j using Meetup.com for local events. I presented an idea to use Neo4j to build a platform that could help us understand the demand for presenting compelling content at events.

Compelling content is at the core of great community experiences. That content fuels the conversations between people, ideas begin to flow, and innovation is born.

My idea was to build an open-source platform that would poll public APIs, translate collected data into a graph, and store it in a graph database to be analyzed, queried, and visualized over time. The first component of this architecture is the Data Import Scheduler, which this post describes in detail.

## Polling Data From Public APIs

Let's start out by answering a basic question.

What does the data import scheduler do?
The analytics data import scheduler is a Node.js process that can be hosted for free on Heroku and is responsible for collecting time-based statistics from a public API. In this case, the Meetup.com REST API exposes a set of methods that provide a momentary snapshot into the number of members that a group has at the time of the request. The data import scheduler polls this endpoint once a day to retrieve Meetup group statistics to later be used for time-based analysis from our graph database, Neo4j.

As illustrated in the diagram below, the Node.js application wakes up once a day and checks in with the Meetup.com REST API.

The scheduler process polls Meetup.com's REST API daily. An HTTP GET request is dispatched for each city we're tracking, returning a JSON formatted response for groups in those cities. The JSON data for each group is then translated into a subgraph, formatted as Neo4j's Cypher query language. The Cypher query is then sent as a transaction to Neo4j and updates a snapshot of the group's stats for that day.

## Importing a Meetup Group's Subgraph

The image below is a visualization of a Meetup group's subgraph, translated from JSON data polled on an arbitrary date.

 Graph Database - San Francisco on 4/28/2014

We see that the group has a set of topic nodes, which may already exist within the database. The subgraph must be merged into the larger graph without duplicating any nodes. Using Cypher's MERGE clause we can get or create nodes, which is useful for expanding our graph's connected data. Each topic will collect more groups as new subgraphs are merged for daily imports. The same is also true for both day and location nodes.

After a few days of scheduled imports, a group's subgraph begins to take shape. As day nodes are connected to the previous day's node, membership statistics are connected.

 A Meetup Group Statistics Subgraph, 4/23 to 4/28

The data import scheduler application is open-source and available on GitHub. Also, full documentation is available to help you get started with customizing your own graph-based reporting platform.

All analysis on the temporal stats collected from the data import scheduler is performed within the REST API module of the reporting platform. It also safely exposes the graph database to a front-end web dashboard, consumed from client-side JavaScript. The REST API uses Swagger, which is a specification and complete framework for describing, producing, consuming, and visualizing RESTful web services.