OpenMRS Meeting

During today’s OpenMRS meeting, the developers discussed things including security issues and bugs as well as many changes they have made in the past month. One of the developers noted that many of the problems OpenMRS has been having is due to many recent commits that were a bit sloppy.

One of the developers got into the registration aspect of OpenMRS and provided a demo. He noted that they made the system keyboard friendly so that the arrows could be used for easy drop down menu navigation. He also noted the added editing in real time to patient medical records, and added address hierarchies for different countries such as Haiti. A few other issues they noted fixing include adding earlier years for older patients because before,¬†OpenMRS wouldn’t allow admins to input birthdays if it would make the patient age over a hundred.

Some issues the developers hope to work on next include adding a format for patient phone numbers such as (xxx) xxx-xxxx rather than just xxxxxxxxxx.

In conclusion, the usage of Java 8 was mentioned. One developer thought it would be better to use with OpenMRS because it has better language features to fit the needs of developers.

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MapReduce

I tried to read the article provided at the Yahoo link; however, it kept telling me that there was an internal error so I could not get the webpage to open. I was able to skim through Hadoop Apache’s tutorial on MapReduce. It was extremely detailed and very good for someone who wants to get down to the nitty gritty about how to use it. It touches on various sections of MapReduce such as inputs and outputs, user interfaces, job configuration and even gives a thorough example of a WordCount program that uses many features provided by the MapReduce framework. It is found at this website:
http://hadoop.apache.org/docs/r1.2.1/mapred_tutorial.html#Example%3A+WordCount+v2.0

The explanation of MapReduce that I liked a little bit more was found on IBM’s website. It was direct and to-the-point and quick to get one caught up on the wonders of MapReduce. It nicely describes the two tasks of MapReduce and also provides examples. The example using a city and corresponding temperatures does a good job at showing exactly how MapReduce works. This information can be found at the following link:

http://www-01.ibm.com/software/data/infosphere/hadoop/mapreduce/