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Genome sequence alignment data processing

To deal with the deluge of data produced by modern sequencers, life science researchers are turning to analytical databases. To help with this (really) big data problem, the MonetDB team has been working together with the CWI Life Sciences group [2]. The result collaboration is the MonetDB/BAM module - a bioinformatics extension, designed to easily load and quickly process genomic data. The authors have also tested the new module, publishing a case study on Ebola virus diversity [1].

MonetDB embraces Node.js

As the Node.js community is growing stronger, and we have started to experiment with the framework ourselves, we deem it useful for Node.js users to have a native MonetDB connector. Using MonetDB as the data store for your analytical web app can improve your user appreciation significantly.

This is the current list of Node.js modules developed by the MonetDB team:

"Data Rotting" concept receives CIDR 2015 Wildest Idea award

Martin Kersten received "The Wildest Idea" award at the Gong Show of the Conference on Innovative Data Systems Research (CIDR) 2015. The award is given for his thought provoking "Big Data Space Fungus", which addresses data rotting in modern data management pipelines.

MonetDB and R sandbox for VirtualBox

The MonetDB team has been working on ways to simplify the deployment of MonetDB. As a first step, we are releasing a virtual machine image of our MonetDB Analytical Database with R.

MonetDB SQL transaction management scheme

The transaction management scheme deployed in MonetDB sometimes creates confusion with application developers. In particular how are transactions committed, persisted and isolated to guarantee the ACID properties of SQL. Most of the confusion stems from the difference between OnLine-Analytical-Processing (OLAP) and OnLine-Transaction-Processing (OLTP).

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