KDubiq CA is about supporting and coordinating
research & networking activities within an emerging area called
Ubiquitous Knowledge Discovery.
* WG1: Application Environments.
* WG2: Ubiquitous Technologies
* WG3: (Learning Components) Resource-aware distributed algorithms
* WG4: (Data types) Ubiquitous Data Interaction and Data Collection
* WG5: Security and Privacy
* WG6: HCI and Cognitive Modelling
The i2010 strategy is the EU policy framework for the information society and media. It promotes the positive contribution that information and communication technologies (ICT) can make to the economy, society and personal quality of life. The European Commission presented it in June 2005 as the new initiative for the years up to 2010.
S. Parthasarathy, и S. Dwarkadas. Distributed and Parallel Databases, (2002)Read again (not completed). Good article. They define some design
goals: clients have different needs to be notified about changes
in the data state, client-controlled memory management, and identifying
what part of the data that has been modified. They have an approach
to the design goal of client controlled coherence. It means that
the client can decide which data update scheme it needs. There are
three similar update schemes that ansures that the client gets the
data only once, when it changes, and at certain time intervals.
The system is called Interact. The articture, figure
1, is that the client maps to a virtual dataspace, which then maps
to the data server. They use association and sequence mining as
applicatins of the Interact system. Association mining is
to discover itemsets that frequently occour together. They also
use sequence mining, which aims to discover events that commonly
occour over a period of time. They call this a temporal database.
They actually use summary structures, which makes this a litte more
to the modelling side than my scope which is more on the primary
modelling side. They seem to to have focused partly on the delivery
of the results to the clients. They use 2-dimansional discretation.
I might have to do the same as it is problematic to make calculations
on higherdimensional structures. It also seems to be diffucult to
visualize higher dimensions than that..
M. Zaki, и Y. Pan. Distributed and Parallel Databases, (2002)This is an introduction to four articles about Knowledge Discovery
in Databases, KDD. It makes the statement that the key challenge
in data mining is the extraction of knowledge and insight from massive
databases. Data mining has the two primary goals of prediction and
verification. Prediction is about predicting the unknown values
of attributes of interest based on a trained model. The data mining
process is being divided into data selection, data cleansing, data
transformation, data reduction, data mining, algorithm selection,
post processing, and interpolation..