Advanced Placement Configurations

Advanced Placement Configurations

Introduction InfiniteGraph 3.0 let’s you configure placement of your data in a completely unique way. In particular, you can configure placement of your distributed data by type and by related type. Consider the use case where an insurance company wants to place all instances of hospital records in a main storage location, and then store all doctor and patient records with their corresponding hospital. Also, consider the use case where an international investment company wants to store all trade data in the NYSE (New York Stock Exchange) in a remote data center in New York, and trade data for the TSE (Tokyo Stock Exchange) in a remote data center in Tokyo, Japan. With a distributed database, data localization is the process of storing data where it most makes sense, which is usually local to the applications that are accessing it most frequently. A major reason why you might like to localize your data is because it reduces the “distance” that you would need to go retrieve the data. Some other reasons for creating and implementing a custom placement model in InfiniteGraph would be: To improve query performance (we place it, we find it), To improve indexing performance, To potentially improve the read/write performance due to reduced lock contention, To maintain and organize data in a logical manner.   Survey of the Problem Managed data localization has become important with the onset of distributed databases because data is not of value unless it can be accessed. Terms like sharding, partitioning and custom placement have become part of our vocabulary. Without the ability to manage placement strategies at an administrative level,...

Objectivity Goes Open-Use!

This has been a busy few weeks at Objectivity with the recent announcement of version 3.1 of InfiniteGraph. As usual we focused on performance enhancements making 3.1 the fastest distributed commercial graph database. But we also announced our first open-use (free, commercially supported open source tools) offering tIGOutput. tIGOutput, is a new output connector that enables you to easily and quickly import data into InfiniteGraph from sources such as Cassandra, HBase or MySQL using Talend data integration products. Talend is a provider of open source software whose products provide an extensible set of tools to access, transform and integrate data. With InfiniteGraph’s open-use connector, you can now import data from any source supported by Talend. To learn more about tIGOutput and how is can help you find paths within your big data, please visit our...
Meaningful Visualizations of Connected Data

Meaningful Visualizations of Connected Data

Introduction I recently watched a TED talk by David McCandless on The Beauty of Data Visualization. It was all about finding meaning in data sets by visualizing them in creative ways. This was done mostly by aggregating scraped data from different sources on the internet and displaying them in different, interesting or useful formats. As a visual thinker, this speaks to me. I see the value in showing meaning by appealing to the visual brain to think about something because it is easier for me to comprehend it that way. Imagine the federal budget. So much money is spent each year that the dollar amount is virtually incomprehensible. Likewise, it is difficult to imagine how to ask the right questions or to avoid jumping to the wrong conclusions, but with a visual aid like Jess Bachman’s “Death & Taxes” poster, the concepts become so much easier to digest. Like the federal budget, most data sources are static and boring. This makes it superbly important to use the right visualization toolkit to show value and meaning to the consumer. Data that is connected or graphical in nature requires the use of some kind of graphical visualization tool. Since InfiniteGraph is a graph database and we offer a simple but powerful visualization tool, IG Visualizer, it made me think of different uses cases with connected data and how we could show meaning using visualization. Survey of the problem Of course, there are many visualization tools that could be used to do some fantastic (and very cool!) visualizations. One thing that these visualization tools can do very well is give context to...
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 –



 King ICT  – Croatia –


 Austria, Germany, Switzerland

Business Software Solutons GMBH –

HM Informatik AG- 


Europe, Africa, Middle East

Minx Software – Germany –


Engineering Software Labs – Israel –


Interested in joining our certification program visit our link for more information and contact us! :