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Archive for January, 2012

PhD Thesis – Introduction

January 23rd, 2012 by Martin 1 Comment

Principles of Service Oriented Architectures (SOA) have been successfully applied for the solutions of business problems and related challenges regarding the integration of heterogeneous software systems in business processes. For this purpose, the so-called Big Web Services technology stack with standards like SOAP, WSDL, WS-Addressing or WS-Security was devised and supporting tools were implemented. These standards ensure that there is a level of common understanding between businesses partners and allow for platform independent communication between remote software Services and their composition/coordination with dedicated languages like BPEL or YAWL.

However, due to the perceived complexity of Big Web Services, Restful Web Services that build on well known W3C/IETF standards such as HTTP, XML or URI have been proposed as (lightweight) alternative. In particular, so-called Service Mashups were introduced, which provide solutions to very specific and narrow business integration problems. The ability of non experts to create Mashup applications is considered to increase the productivity of employees in their daily routine work and when working with different companies on joined projects.

In this context, a growing number of Services can be observed that represent real-world business activities which are performed by humans rather than Software. A prominent example is Amazon Mechanical Turk where tasks are distributed to human workers (Turkers) that offer all kind of Services. As a result, additional standards for requesting work of humans via standardized Service interfaces have been devised, including WS-Human Task and Human Provided Services (HPS). These standards allow for the seamless integration of humans into existing workflows and foster the collaboration in so called mixed Service environments of human and software Services.

The parallel rapid development of mobile technologies and their wide spread adoption, led to a situation, where many employees actually do their job while they are moving. Thus, mobile employees often perform Service work remotely at the location of the customer and their work require the availability of mobile handheld devices such as tablets and smartphones. While early mobile Services focused on simple tasks of entering the data in a dedicated handheld device, modern mobile workers often face complex tasks that require the coordination of several activities of different mobile employees.

Consequently, a lightweight approach for Service compositions is also attractive for mobile workers, because existing SOA standards are difficult to implement on mobile devices because of the volatile characteristics of mobile devices in terms of network connectivity and availability due to limitations in terms of power supply. These are the main reasons that past efforts of adopting SOA on mobile devices focused on the consumption of remote (Web) Services from mobile devices, whereas the provision and composition of Services on mobile devices did not receive as much support. Instead, Apps have emerged as primary means for Service provision on mobile devices. Apps are independent pieces of software that offer a well defined set of functionality are controlled by the user. On average, users install approximately between 14 and 40 Apps on their devices that cover a broad range of functionality: social networking, weather, sports, location information, dictionaries, photography and games are among the most common classes of Apps being used.

In our work, we study the applicability of SOA principles with regard to existing (mobile) infrastructures like App Stores and mobile Apps. Specifically, we analyze established SOA principles like Service provision, Service binding, Service discovery and Service composition in the context of mobile devices and investigate the mapping of SOA infrastructure to a mobile infrastructure. We also address the social aspect of mobile Service provision: mobile Services – being bound to a human – run on devices which are controlled by the owner who can decide if a Service is executed (provided) or not.

To address core mobility aspects like limitations of connectivity, we introduce a lightweight programming language called Tweetflows that provides communication mechanisms to invoke Apps remotely and consequently the means to compose Apps. In this regard, we study the application of microblogging services (e.g., Twitter) as communication backbone for the use of mobile Services in a social context.

your ikangai phd writing team

(c) 2012 Martin Treiber – All rights reserved.

iBooks Author – A first Look

January 19th, 2012 by Martin No Comments

I was playing with the new iBook Author App from Apple a bit. Its purpose is to let you create your own books in a simple manner. If you are familiar with Keynote, you’ll be able to use this tool very quickly. The look and feel is very similar to Keynote and even the symbols in the toolbar are the same. If you start the App and select a template you can immediately start to add your content.

After you finish editing, you can preview your masterpiece directly on the iPAD (you need to install iBooks first). The preview adds your book to the bookshelf as shown below.

There are additional tools available to make your book interactive (e.g., quizzes, animations). Advanced users can also play with HTML and Javascript and add Dashboard Widgets to the book. There are some limitations, for example, the widget is not allowed to access the file system and not all media types are supported. But this is not an issue if you add a simple Dashboard Widget to the book. I downloaded a game called DashClicko and added it to the book.

Dashboard Widgets automatically enter the full screen mode, after they are started (see below).

I believe that iBook Author will shake things up a bit in the publishing business. iBooks Author makes it possible to create professional looking books with little effort and little if any involvement of publishers.

your ikangai team

The Day The LOLcats Died

January 18th, 2012 by Martin No Comments

Searching Invisible Things

January 16th, 2012 by Martin No Comments

Users are often confronted with “invisible things” when using a technical device like a computer. A typical example for invisible things for users is meta information about files. This data is hidden away from the standard user (not from us geeks), because it is widely considered that users do not need to know the permissions of a file, i.e., whether it is hidden (Windows) or the file’s attributes are 644 (Unix, Linux, MacOS).
Attributes can also be set for smaller things on computers, such as paragraphs, words or even single letters. However, setting an attribute of a text, has an effect of the text that can be observed by the user. For example, if a text is set to bold, the user expects the text the be rendered bold.
But there is a class of data that is not directly visible to the user and nevertheless is very important to the user; it can be even distracting when permanently on display. The class is data which describes data on a different level – on a semantic level. To give you an example:

The blog entry that you are reading now discusses meta data and how to display it in an iPAD App (an editor/notebook) to make it easy for the user to handle and manipulate it.

In the example, the sentence describes the content of the blog entry on a more abstract level – it is meta.
In my current project (the ideal editor/notebook for the iPad) I’m facing a problem that is closely related to invisible things. More specifically, I’m wondering what to do with tags that a user can give to portions of text to make the retrieval easier (and faster). Since tags are not per se part of the text, they are obviously meta data – they are about the text. Furthermore, tags may be given a posteriori (after the text was entered) to classify of the content. This kind if data is also called folksonomy. So, if you write your text, where do you display this data? Or, do you even display the data at all? But, if the data is not visible, how can you search for it? In my case, I’ll experiment with displaying tags on a separate window and displaying them when the text is being scrolled. The search for tags will be handled in a separate window as well, because it is difficult to handle search for different semantic concepts in a single search box without confusing the user. After all, even if tags are not part of the text, tags can appear in the text. This makes the decision complex: where do you jump to? To the part of the text that is marked with a tag? Or to the tag in the text?

your wondering ikangai editor/notebook development team

Analyzing Bought Twitter Followers

January 11th, 2012 by Martin No Comments

After analyzing the 1000 bought Twitter followers, one can safely assume that all of them can be regarded as fake (“fake” meaning not representing a human user or a company) and that they were created for this purpose. The content of a typical follower has little or no meaning and is never directed to anybody else (which of course true for a lot of Tweets :-) ). Here are some examples from the dataset:

Marietteszm – I luv ur slippers!!
Krystleia – Highlight of the day: DONT TOUCH THAT! WE DONT KNOW WHAT IT IS! .. Wtf mum, its your car.
Sherlyp52 – O değilde arka planım çoğ seksi :D hgadfhgdfh
Darnellct92 – My kids had a problem listening to me today,my husband was being an ass and even though my tubes are tied i might be pregnant.. what a day!
Clotildetan – asked by about saying about himself being an underdog

Another evidence for the assumption that these followers are fake, is the fact that these Twitter followers never retweet. They are completely passive. Furthermore, all of their Tweets were either posted between October 15th and 20th, on October 25th or on November 13th 2011 which cannot be a coincidence. This suggests that some kind of database in combination with a tool was used to generate these Tweets.

However, the creator of the Twitter users has put some effort into making the Twitter follower appear human. All of them had pictures and locations and short descriptions (some with sexual references) like this:

well i think i have the longest list of people who hate me ever, i call myself a villain because i fuck shit up, you aint got nothing to do ? shit give me head

The structure of network itself does not exhibit any clustering behavior: all bought Twitter followers just point to MartinTreiber_ and have only a few connections among each other. This is reflected in statistics of the network structure:

Number of vertices: 1010
Number of edges: 1179
Average degree: 2.335
Clustering coefficient: 0.112
Average embeddedness: 0.433
Graph density: 0.001
Connected components (weak/strong): 1/1010

If we move the MartinTreiber_ node to the center, apply the noverlap algorithm and zoom in, it is clearly observable that the vast majority of followers are fake, as the ratio of the number of relations to MartinTreiber_ with respect to the number of relations among the other 100 nodes is negligible.

So, what can we learn from this? Actually, it is pretty simple: if you want to boost your Twitter network stats, you can buy Twitter followers. But, if you want to extend your true reach in your Twitter network, you cannot rely on external services that boost your just numbers. You will have to make the effort to look for real people that are actually interested in your Tweets.

your ikangai science team

Buying Twitter Followers

January 8th, 2012 by Martin No Comments

After some considerations, I decided not to use the ikangai Twitter account. Instead, I created a new Twitter account MartinTreiber_ and decided to buy 1000 Twitter followers for it. This morning I got the notification that the account has 1000 new followers. Now it is time to look at the network structure and the followers in more detail.
The first thing I noticed – after a random sample – was that many of them tweeted on November 13th for the last time. Furthermore, lots of the new followers have a low number of followers (around 15-50) and higher number of people they follow (around 140-450) – a factor approximately around 1:10. When I looked at some of the profiles in more detail (specifically at the picture), I found pictures of attractive women and descriptions that contained sexual references. Apparently, this attracts some followers ;-) .

Here is a nice graphical representation of the follower network that I bought on Twitter:

Actually, it looks really cool, in comparison with the content. Basically, almost all nodes point to one single node and there are almost no connections between bought followers. My suspicion is that the companies that offer followers to buy just created several thousand of fake Twitter users, added some Tweets to each of them (in batches) to make them look “real” and that’s it. Interestingly, the network is similar to the network that is jokingly considered as Apple’s company structure:

your ikangai science team