Kontextsensitivität (engl. context awareness, auch Kontextabhängigkeit) bezeichnet das Verhalten von Anwendungsprogrammen, die Informationen über ihren „Kontext“, also ihre Umgebung, benutzen, um ihr Verhalten darauf abzustimmen.
Die Basis, auf der diese Systeme arbeiten, sind Informationen, welche durch unterschiedlichste Quellen oder Sensoren zur Verfügung gestellt werden. Mit Hilfe dieser Informationen werden Schlüsse über den Kontext gezogen. Der ermittelte Kontext wird von der Anwendung verwendet, um ihr Verhalten anzupassen, insbesondere das Verhalten der Benutzungsschnittstelle. Kontext wird z. B. definiert als „... jegliche Information, die genutzt werden kann, um die Situation einer Entität zu charakterisieren.“[1]. Der Gebrauch von Kontextinformationen ist am häufigsten mit dem Zeit- und Ortsaspekt von Personen verbunden. Jedoch können beliebig weitere Aspekte in ein Kontextmodell aufgenommen werden, wenn entsprechende Quellen oder Sensoren dazu existieren. Dieses können beispielsweise Archivdaten oder Vitalwerte von Personen, die Temperatur in einer Umgebung oder auch die Beziehungen zwischen Personen sein.
Das Ziel der Entwicklung kontextsensitiver Anwendungen ist es, eine höhere Nutzwert als mit klassischen Anwendungen zu erreichen.
Following up on KMeans Clustering Now Running on Elastic MapReduce, Stephen Green has generously documented the steps that was necessary to get an example of k-Means clustering up and running on Amazon’s Elastic MapReduce (EMR) on the Apache Lucene Mahout wiki.
The image above show the segments or classification of these standards. By asking basic questions such as How, What, When, Who and Why we can also simplify this a bit. Starting from the bottom up lets talk about what was discussed and I'll add some of my thoughts as well.
Let's say you've identified a microdecision or two that has economic leverage. What can you do to improve it? There are many possible interventions, and it's important not just to always use the same one. One approach is to automate it entirely. This is the focus of James Taylor and Neil Raden's book Smart Enough Systems, and of Taylor's blog on enterprise decision management. . If the decision is structured enough, that may be a good idea.
One common problem I see in the IT industry is the qualification of IT decisions. I talk to architects from all around the world and hear a lot of creative and innovate ways of solving problems. More often than not, what I don’t hear is more concerning. When I have asked: Why did we approach the problem in this manner? How does this align to the business? Does this fulfill the business, functional and non-functional requirements Why is this the optimal architecture? Obviously there are a lot of other questions, but to keep this concise above are some sample questions. The last question is particularly interesting. I have heard a broad range of fluffy answers such as: “trust me, I know what I am doing”, “I have been doing this for 20 years, I know how to do this”, “I am the expert of [X]”. All of these responses may be completely true but doesn’t quantify the solution. It doesn’t demonstrate that there was a process or a clear level of due diligence that was performed.
I don’t know whether you said that a CEP application must necessarily have a model. It may have, or it may not. A rule-based approach (in its general acceptation) is not considered as a model. In the AI terminology, rules are considered as “shallow knowledge”, while models are considered as “deep knowledge”. Shallow knowledge expresses the people’s experience, links symptoms to causes directly, while deep knowledge establishes the links using a model, and the model can be interpreted. Shallow knowledge is very helpful in many cases, and as deep knowledge it also allows detecting situations. Of course, the cooperation of both is desirable to build more powerful systems. I did a rapid search, and below are 3 entries for reference:
The MS Rules Framework was a spirited attempt by MS to create a wide-ranging environment that could integrate rules held in different forms in different repositories, mange the deployment of rule sets out across an enterprise environment and even target a range of different rules engines.
D. Rosca, S. Greenspan, M. Feblowitz, and C. Wild. Requirements Engineering, 1997., Proceedings of the Third IEEE International Symposium on, (January 1997)
E. Mohyeldin, M. Fahrmair, W. Sitou, and B. Spanfelner. The 16th Annual IEEE International Symposium on Personal Indoor and Mobile Radio Communications (PIMRC05), 11-14 September 2005, Berlin, Germany, (2005)
M. LeMay, R. Nelli, G. Gross, and C. Gunter. HICSS '08: Proceedings of the Proceedings of the 41st Annual Hawaii International Conference on System Sciences, page 174. Washington, DC, USA, IEEE Computer Society, (2008)
T. Bucher, R. Fischer, S. Kurpjuweit, and R. Winter. Enterprise Distributed Object Computing Conference Workshops, 2006. EDOCW '06. 10th IEEE International, (October 2006)