An empirical comparison of graph databases

In recent years, more and more companies provide services that can not be anymore achieved efficiently using relational databases. As such, these companies are forced to use alternative database models such as XML databases, object-oriented databases, document-oriented databases and, more recently graph databases. Graph databases only exist for a few years. Although there have been some comparison attempts, they are mostly focused on certain aspects only.
In this paper, we present a distributed graph database comparison framework and the results we obtained by comparing four important players in the graph databases market: Neo4j, OrientDB, Titan and DEX.

 

Salim Jouili, and Valentin Vansteenberghe, An empirical comparison of graph databases, proceedings of the 2013 ASE/IEEE International Conference on Big Data, Washington D.C., USA, September 2013.

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Impossibility of change?

As time goes by and information technologies infrastructures are built, historical decisions accumulate. This accumulation put constraints on architectural options for the future. In other words, the more history you have to manage, the less time you have to make your system progress. And time is money.

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They are renovating the scaffoldings of Brussels’ Law Courts that were put in place 25 years ago.

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EclipseCON Boston 2013

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© EclipseCON Boston 2013

Last week I was presenting a talk at EclipseCON US at Boston US. As usual this is an excellent opportunity to give you an overview of the different trends of the conference.

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IEEE CloudCom Conference on Cloud Computing Technology and Science

Last week I was at Tapei with Nam-Luc for presenting the AROM paper. I wanted to come back on the trends of this year at the conference which, by the way, are a really good insight of the hot topics in cloud, distributed computing and HPC. I will not dive into details for each of them, if you have any question just post a comment or send me a mail!

CloudCom2012Article

 

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Arom: processing big data with data flow graphs and functional programming

The development in computational processing has driven towards distributed processing frameworks performing tasks in parallel setups. The recent advances in Cloud Computing have widely contributed to this tendency. The MapReduce model proposed by Google is one of the most popular despite the well-known limitations inherent to the model which constrain the types of jobs that can be expressed. On the other hand models based on Data Flow Graphs (DFG) for the processing and the definition of the jobs, while more complex to express, are more general and suitable for a wider range of tasks, including iterative and pipelined tasks. In this paper we present AROM, a framework for large scale distributed processing based on DFG to express the jobs and which uses paradigms from functional programming to define the operators. The former leads to more natural handling of pipelined tasks while the latter enhances genericity and reusability of the operators, as shown by our tests on a parallel and pipelined job performing the calculation of PageRank.

Nam-Luc Tran, Sabri Skhiri, Esteban Zimányi, and Arthur Lesuisse. AROM: Processing Big Data With Data Flow Graphs and Functional Programming, proceedings of the 4th IEEE International Conference on Cloud Computing Technology and Science, IEEE CloudCom 2012. IEEE Computer Society Press, Taipei, Taiwan, December 2012.

Click here to access the paper.

EclipseCon 2011

In this post we have invited Charles Bonneau, software architect & Eclipse addict at Euranova. Charles will share his feedback from EclipseCon EU 2011. Welcome Charles !

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