Performance Evaluation of Complex Data Sets with Heterogeneity Using Particle Swarm Optimization

Mishra Jyoti Prakash¹ and Mishra Sambit Kumar^1,

¹Gandhi Institute for Education and Technology, Baniatangi, Bhubaneswar, Odisha, India

Cite this paper:
Mishra Jyoti Prakash and Mishra Sambit Kumar. Performance Evaluation of Complex Data Sets with Heterogeneity Using Particle Swarm Optimization. Journal of Computer Sciences and Applications. 2015; 3(6):130-133. doi: 10.12691/jcsa-3-6-4

Abstract

Traditional query processing applications may not be adequate with large or complex data sets with heterogeneity. Challenges to this context may include analysis, capture, search, sharing, storage, transfer, visualization, and information privacy. Cloud computing refers to the practice of transitioning computer services such as computation or data storage to multiple redundant offsite locations available on the internet, that allows application software to be operated using internet enabled devices. Cloud computing usually focuses on maximizing the effectiveness of the shared resources. Cloud resources are generally not only shared by multiple users but are dynamically reallocated as per demand. The present cloud services realize improved execution efficiency by aggregating application execution environments. Now a day it is in the phase of expanding from application aggregation and sharing data aggregation and utilization. In this paper, the query evaluation strategies have been proposed by considering partially correlated data in heterogeneous databases of concern. The main idea behind this strategy is to retrieve the data from heterogeneous databases linked with the declarative query I interface implementing data access methods and optimization mechanisms. The indexing and query processing strategies may be applied to the integrated components of the database systems with heterogeneity. As a result, it may be convenient and useful to analyze and evaluate the data using efficient functional evaluations implemented inside the database systems. Usually the index structures are generated to coordinate the result analysis without duplicating the query evaluation result. It is also aimed to provide an end-to-end solution for scalable access to big data integration, where end users may formulate queries based on a familiar conceptualization of the domain. It has also been proposed to process the distributed query in the heterogeneous environment to evaluate scalable solution for executing queries in the cloud.

Keywords:
query plan cloud big data tuple aggregation temporal data swarm pbest gbest

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