In but, once again, they are situated at very

In the year 2011, Han, Jung 265
described the Aspect Oriented Programming (AOP) is well suited to cluster
computing software by using simple, intuitive, and reusable aspects. Throughout
qualitative and performance evaluations, AOP significantly improves the code
readability as well as the modularity, and AOP-based software has the same
performance and scalability as similar software that is developed without using
AOP. Guabtni, Ranjan 266 concerned
with data provisioning services (information search, retrieval, storage, etc.)
dealing with a large and heterogeneous information repository. Increasingly,
this class of services is being hosted and delivered through Cloud
infrastructures. Awang 268 proposed an algorithm and analytical model based
on asynchronous approach to improve the response time, throughput, reliability
and availability in Web Server Cluster. The provision of high reliability in
this model is by imposing a neighbor logical structure on data copies. Data
from one server will be replicated to its neighboring server and vice versa in
the face of failures. 


Senguttuvan, Krishna 283 reviewed
five of the most representative off-line clustering techniques: K-means
clustering, Fuzzy C-means clustering, Mountain clustering, Subtractive
clustering and Extended Shadow Clustering. The techniques were implemented and
tested against a medical problem of heart disease diagnosis. Performance and
accuracy of the four techniques are presented and compared. Soni,
Ganatra 284 provided a
categorization of some well known clustering algorithms. It also describes the
clustering process and overview of the different clustering methods. Bahmani, Moseley 289 proposed
initialization algorithm k – means  
obtains a nearly optimal solution after a logarithmic number of passes,
and then show that in practice a constant number of passes suffices. 

We Will Write a Custom Essay Specifically
For You For Only $13.90/page!

order now

The “data mining extensions” (DMX) 2 is a
SQL-like language for coding data-mining models in the Microsoft platform, and
therefore it is difficult to gain understanding of the data-mining domain. Data
mining is a highly complex task which requires a great effort in preprocessing
data under analysis, e.g., data exploration, cleansing, and integration
9. The 10 provides an entire framework to carry out data mining but, once
again, they are situated at very low-abstraction level, since they are
code-oriented and they do not contribute to facilitate understanding of the
domain problem. Research papers 11 and 12 provide a modeling framework to
de ne data-mining techniques at a high-abstraction level by using UML. However,
these UML-based models are mainly used as documentation. Parsaye 13 examined
the relationship between OLAP and data mining and proposed an architecture
integrating OLAP and data mining and discussed the need for different levels of
aggregation for data mining.