In the year 2011, Han, Jung 265described the Aspect Oriented Programming (AOP) is well suited to clustercomputing software by using simple, intuitive, and reusable aspects. Throughoutqualitative and performance evaluations, AOP significantly improves the codereadability as well as the modularity, and AOP-based software has the sameperformance and scalability as similar software that is developed without usingAOP. Guabtni, Ranjan 266 concernedwith data provisioning services (information search, retrieval, storage, etc.)deal with a huge and assorted information repository. Increasingly, this classof benefit is being hosted and delivered through Cloud infrastructures. Awang268 proposed an algorithm and analytical model based on asynchronous approachto improve the feedback time, throughput, purity and availability of clusteringin Web Server Cluster. The provision of high reliability in this model is byimposing a neighbor logical structure on data copies. Data from one server willbe replicated to its neighboring server and vice versa in the face of failures.
Soni, Ganatra 284 provided a categorization of some well known clustering algorithms. Italso describes the clustering process and overview of the different clusteringmethods. Bahmani,Moseley 289 proposed algorithm of initialization k – means obtains approximately optimal solution aftera mathematical log number of passes, and then visible that in activity a constantnumber of passes suffices.The “data mining extensions” (DMX) 2 is aSQL-like language for coding data-mining models in the Microsoft platform, andtherefore it is difficult to gain understanding of the data-mining domain. Datamining is a highly complex task which requires a great effort in preprocessingdata under analysis, e.
g., data exploration, cleansing, and integration9. The 10 provides an entire framework to carry out data mining but, onceagain, they are situated at very low-abstraction level, since they arecode-oriented and they do not contribute to facilitate understanding of thedomain problem. Research papers 11 and 12 provide a modeling framework tode ne data-mining techniques at a high-abstraction level by using UML. However,these UML-based models are mainly used as documentation.
Parsaye 13 examinedthe relationship between OLAP and data mining and proposed an architectureintegrating OLAP and data mining and discussed the need for different levels ofaggregation for data mining.