High Traffic Employee Network Analysis using Navigation

High Traffic Employee Network Analysis using Navigation

Introduction Imagine an employee network the way that you might connections between highly trafficked websites? Of course, we all don’t work for a “highly trafficked” companies, but many employees feel drawn towards “highly trafficked” companies. It is interesting to imagine whether this attraction is justified. Likewise, many employees imagine the freedom that becoming an independent contract might bring, but they may neglect imagine thinking of the higher taxes and the shorter contracts that might come along with it. It would be interesting to see the flow of employees in and out of highly trafficked companies and likewise, to compare it with the flow in and out of non-highly trafficked companies. It is interesting to think how a person might be pulled from company to company. I think that there are many values that tend to be perceived as a stronger part of a smaller company like collaboration in smaller teams, higher visibility for individual contribution, flexibility, participating in different roles, etc. Likewise, there are many values that trend among larger companies like better salaries, more overall market visibility, working on cutting edge technologies, etc. For this reason, I believe that employees are pulled like gravity to companies that tend to be of the same size. On the other hand, if there is a trend, I would say that it is probably towards larger companies because of perceived job stability. But am I right? Does the data support this? Survey of the problem How do we study complex and highly connected networks? Many times, it is impossible to analyze the entire graph all at once to pull relevant statistics. Some...

Easy Twitter & Rotten Tomatoes integration via REST API’s & Qualifiers

Introduction Have you seen a list of the top 100 movies or the top 25 best actors or actresses? Do you ever wonder how those are selected? I have long felt that these lists are not very democratic and can quickly go out of relevancy. In contrast, I can find out ratings on Rotten Tomatoes on movies before they even come out in the theater and the ratings are, in my experience, pretty spot on. Also, more and more, people are taking to social media like Twitter to see what their friends might say about a new movie in order to judge. How can your friend’s be wrong? After all, they know that they can be blamed if you don’t like it. I have been using the IMDB data set a lot lately to view activity around various Hollywood heavys. I have discovered that the IMDB data set while being massive in size and connectedness, is actually made up of rather lightweight objects. The data set gives a stripped down version available on the IMDB website and limits the kind of rich queries and navigations that one might want to perform. Having lightweight objects in the database can be good because it may allow you to do lookups and simple navigations very quickly and easily, but without the data in the database, it can restrict that types of deep analysis that may want to perform. Alternatively, there are a number of open and free REST API’s that are available for sites like Twitter and Rotten Tomatoes. Interfacing to the data contained in these sites allows us to fill out...

InfiniteGraph Goes Global Through Certified Partner Program

Back in July we launched the Objectivity Global Certification Program to help educate companies around the world about the power of the graph database in Big Data analytics and the differences between InfiniteGraph the only commercial distributed graph database or what we call DGDB.  All around the world we are seeing a movement to move beyond Big Data management to analytics.  But simple Business Intelligence solutions today cannot handle the type of deep real-time analysis needed.  In addition, analysis has gone beyond single server to distributed architectures.  We are educating our partners so they can help their clients do more with their data than ever before.  And the program is growing quickly with partners that have deep history and expertise with data management all over the world.  Below are some of our program’s first graduates that can help you make the most out of your Big Data investment:



Nextgen Distribution – nextgendistribution.com.au



 King ICT  – Croatia –  www.king-ict.hr


 Austria, Germany, Switzerland

Business Software Solutons GMBH – www.b-s-s.de

HM Informatik AG- www.hm-ag.de 


Europe, Africa, Middle East

Minx Software – Germany –  www.minx-soft.de


Engineering Software Labs – Israel – www.eswlab.com


Interested in joining our certification program visit our link for more information and contact us! : https://objectivity.com/resources/certification-programs


Now You Node!

With so many new technologies, solutions, and projects sprouting daily, the graph database market is a bit like the wild, wild west.   Even with our 24 years of experience in the NoSQL database and Big Data space, we still have a hard time keeping track of it all. To combat the confusion in the market, and to educate our clients and prospects, we drafted a graph database comparison chart last year based on publicly available project/company and reference websites like Wikipedia, to explore the many different options and features within graph databases today.

We have since developed a publicly editable version of this graph database comparison chart located at  http://en.wikipedia.org/wiki/Graph_database and invite all community experts in graph database technologies to assist in maintaining the chart with the latest updates on features, benefits and new technologies.   We hope this will become a valuable, unbiased, reference to anyone looking for information on understanding and deploying a graph database solution.  

What Facebook’s Graph Search Means To The Graph Database Space

While the world may have been disappointed Facebook didn’t announce a phone, InfiniteGraph is head over heels about the Graph Search announcement.  Over the years our Objectivity teams have had several conversations with companies like Facebook, Ebay and LinkedIn about the power of the graph database and leveraging the graph to recognize opportunities in Big Data.  We have shown our customers that InfiniteGraph can go beyond 7 degrees of relationships to enable complex, distributed, customized searches within Big Data, farther than most solutions can go today. Heck, a year ago, we even showed you how to build your own Facebook graph search free! “Get started w/ our free FB sample @ http://ow.ly/gQsn9”.   … But the gold is in this line here: “The graph we’re talking about includes a billion people, 240 billion photos, and 1 trillion connections” Zuckerberg said according to Venturebeat .  InfiniteGraph is already analyzing connections between petabytes and exabytes of data in real-time. Objectivity’s solutions are able to analyze up to the equivalent of the Library of Congress in hours. 

 It’s Time for the Graph to Get Serious

A light has been shed on the power of the graph to move the Big Data space beyond management and slow analysis.  We have long known of the graph databases’ ability to manage larger volumes of complex data and provide real-time insight that is truly important – the discovery and understanding of the connections between people, places and content within data. And while InfiniteGraph recognized exponential sales and growth in 2012, with this single announcement the graph database just became a household name overnight.

 Now it’s time for this space to make accessing the graph simple for the world by establishing a single graph language that can help customers and the graph reach its true potential.  So graph database companies, are you ready?  Then let’s talk.