For three years now, NoSQL as a piece of technologies for Big Data has spread over the world and is challenging the centralized world of RDBMS. The space of distributed storages is yet not new and banks, online gaming platforms are using for several years technologies called “data grid” to address latencies and throughput issues. And to be completely franc, “Big Data” is not far from being the “new SOA”: a radical paradigm shift lost in the middle of commercial buzz words but that’s another story…

What are the common points? The main differences?

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ASIHttpRequest, AFNetworking, MKNetworkKit,… The iOS/MacOSX programming landscape is full of helper libraries to deal with asynchronous network communications in your applications.

RestKit can be seen as yet another library to do it. However, its approach is radically different since it does not only address asynchronous networking but also object mapping and a seamless persistance of such mappings locally through CoreData.

This approach is quite interesting when it comes to interact with Restful web services because of the time (and code!) you can save.

In this article, we are going to look at RestKit’s main functionalities through the code to do it. The objective is to give you a precise idea of what can be done and how to do it.

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The problem

Why untar?

Network connections cause latencies on your mobile app. Replace 10 downloads of 1Mb by 1 download of 10Mb is a good improvement to your app.

There’s a well-known unix tool for that: tar.

What is tar? Wikipedia says: “Tar is now commonly used to collect many files into one larger file for distribution or archiving, while preserving file system information such as user and group permissions, dates, and directory structures.” That’s exactly what we need with some extra features.

How to untar an archive file on your iOS device? (more…)

After applying as much as possible the DevOps principles for more than a year on the run of a highly business critical project of one of our customers, here are some golden rules we eventually found out and tried to stick with. Items are here presented from an Ops perspective.

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One of the biggest Java conference in the world took place in Antwerpen, Belgium on last November. This year, Devoxx main themes were (in no particular order)

• The future of Java
• Alternative languages on the JVM
• HTML5
• JavaFX
• Android
• A bit of Cloud, NoSQL and high performance architecture

We also had a great announcement about a new conference coming in 2012: Devoxx France!

Obviously, OCTO was there. In this article we won’t cover the sessions in details. A lot of blogs already did it. We’ll however summarize this edition’s main trends and give our impressions.

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Today’s information society abounds in a myriad of information flows, computer-based human collaborations, software agent interactions, electronic business transactions, and the explosion of data on the Internet. Understanding what is happening in these environments is becoming increasingly difficult. In other words, we need to find the best ways to make sense of this wealth of data, to improve the quality and availability of information, and to ensure effective responses. Traditional storage and data analysis technologies are not adapted to this exponential growth of volume and event rates.

In addition, the value of information may decay rapidly over time. For instance, some events that may help anticipate a production outage have no value once the outage happened. There is a need to process data as soon as events occur, with respect to latency constraints. We need to move away from traditional client-server (query-response) interaction models, to more asynchronous, event-oriented, loosely coupled push model, with applications able to take decisions based on events data.

Complex Event Processing (CEP) is a set of technologies that allows exploring temporal, causal, and semantic relationships among events to make sense of them in a timely fashion.

This article is the first of a serie exploring these technologies, their capabilities and possible applications.

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Face Recognition is definitely one of the most popular computer vision problems. Thanks to its popularity it has been well studied over the last 50 years. The first intents to explore face recognition were made in the 60′s however it was until the 90′s when Turk and Pentland implemented the “Eigenfaces” algorithm, that this field showed some really exciting and useful results.

Bright future

Face recognition is recently getting more and more attention and we can anticipate bright future of this field.

Security was historically and will stay in the future the main application of face recognition in practice. Here face recognition can help with both: identification and authentication. Good example is the Frankfurt airport security system which uses face recognition to automatize passenger control. Another application can be the security analysis of videos purchased by external city cameras systems. Potential suspects can get identified before committing crime. Take a look at the integration of face recognition to London Borough of Newham, already in 1998.

Face recognition can be also used to speedup the identification of persons. We can image a systems which would recognize the client as soon as he walks into branch store (bank, assurance), and the front-office worker can than welcome the client by has name and prepare his folder before he actually gets to the counter.

Advertising companies are working on ad-boards which would adapt their content to the persons passing by. After analyzing the persons face, commercials would adapt to the gender, age, or even personal style. This usage however, might not conform to privacy laws. Private companies do not have rights to film persons in public places (of course, depending on the country.

Not to forgot, that Google and Facebook had both implemented algorithms to identify users in the huge database of photos which they maintain as part of their social network services. Third party services, such as Face.com offer Image base searching, which allow you search for example for picture which contain together your best friends.

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Face recognition is exciting machine learning task which during the last decade has brought some good results, used mainly in security applications to perform person identification.
However it used to be reserved only to university research and implemented only by companies specializing in this field.

But the time invested into this field by universities, companies and independent developers has brought its result in the means of several open-source libraries which any developer can use to perform image processing tasks including face recognition. Take a look at the complete overview of existing computer vision algorithms and libraries.

This article will show you how to incorporate face recognition into your web page using EmguCV image processing library and Silverlight Web Camera features. EmguCV is a .NET wrapper for OpenCV library, written in C++ by Intel and published as open-source. The method which will be used is Eigenfaces algorithm. If you are interested in the details of this algorithm, please refer to the previous article on this topic: The basics of face recognition.

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This year, we had the opportunity to go to the Droidcon in London, a great conference about mobility and Android. The two-day event was organized like in 2010: a barcamp the first day, and conferences with great speakers the second one. Main topics were user experience and interface, Android development, mobile apps business and distribution.
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MVVM is a great pattern to enable (among other things) unit testing of a XAML based application GUI. If you’re not familiar with it, this article explains it well and describes its use with Greenpepper on a real life project.

Functional testing works great with MVVM, but several issues prevent the use of functional testing tools (e.g. Fitnesse) with a Silverlight project out of the box.

If the previous article convinced you to implement automated functional testing on your application, but its GUI is in silverlight instead of WPF, let’s see how you can circumvent those issues.

The following has been implemented with Fitnesse, but should also work with Greenpepper or any other .Net automated testing framework.

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