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Applicability of Concurrent Data Structures for Data Intensive Data Mining Applications in Cloud Computing Environment

Asheesh Dixit, Akanksha Kherdikar Kurlekar


Data Mining is fast becoming a pervasive technology that is poised to touch all aspects of our lives. This can be mainly attributed to the fact that data in the world is growing at an unprecedented rate. Today, decision making is more data-centric and complex than ever before. Computational requirements for such complex and data-intensive decision support systems are also increasing exponentially. All these data mining applications are sequential in nature and predominantly used in house; however there is underlying architecture that is multi-core and can be leveraged for data mining applications and same can be developed through concurrent data structures which will use this infrastructure (multi-core). This infrastructure can be easily provided through cloud computing environment by using IaaS. This paper attempts to develop algorithms which will overcome the drawbacks of sequential Data Structures and provide parallelization by using concurrent data structures. The usage of concurrent Data Structures will help in improving the Performance of Data Mining applications. The Applications and Algorithms suggested is an Approach to help gather, manage, store and present the Huge Date locally as well as on Cloud Computing environment.


Cloud Computing (SAAS, PAAS, IAAS, DSAAS), Cluster Computing, Concurrent Data Structures, Data Intensive Data Mining, Grid Computing.

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