Open Access Open Access  Restricted Access Subscription or Fee Access

A Idea to Provide Optimistic Solution for Perceived Latency

Dr. N. Kavitha, V. Vadivu


Data is a powerful term for the non -traditional strategies and technologies needed to collect, systemize operation and gather insights from large datasets. The growth of Big Data be about as data privacy, data security and data discrimination will be priority items to accept for governments, business owners, consumers and cause the problem of working with data that exceeds the computing power of a single computer, the ubiquity, scale, value of this type of computing and perceived latency has greatly expanded in recent years. This paper gives a idea about how to provide the optimistic solution for the perceived latency in big data infrastructure by its technology.


The Big Data, Ant Based Clustering, Longest Common Subsequence, Map-Reduce, Distributed File System, Hadoop

Full Text:



Seema Maitrey, C.K.Jha Map Reduce: "Simplified Data Analysis of Big Data", Elsevier Procedia Computer Science 57, 2015, p.563 – 571s.

Bhagyashri s.Gandhi, Leena A.Deshpande “The Survey on Approaches to Efficient Clustering and Classification Analysis of Big Data” IEEE Systems.

Dawen Xia, Zhuobo Rong, Yanhui Zhou, Binfeng Wang Yantao Li and Zili Zhang “Discovery and Analysis of Usage Data based on Hadoop for Personalized Information Access”IEEE 16th International Conference on Computational Science and Engineering ,2013. p. 917-924.

S.Kalaivani and K. Shyamala “Clustering of web users behavior based on session identification through web log files “International Journal of control theory and applications, 2017, ISSN: 0974–5572, Volume 10 • Number 23 •. P.7-16.

Jalali, M., Mustapha, N., Sulaiman, N.B. and Mamat, A. (2008c) A web usage mining approach based on LCS algorithm in online predicting recommendation systems, 12th International Conference Information Visualization, IEEE Computer Society, Pp. 302-307.


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.