Abstract
Nowadays as data volume is increasing, it is becoming difficult to access the data within span of time. So, our aim is to process required data as fast as possible. Though we have variety of algorithms but none of them are specially designed to manage the large data (e.g. peta byte size of data). In this research paper, authors have proposed an algorithm based on hashing technique which uses Hadoop framework to reduce search time.
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References
Patel, A.B., Birla, M., Nair, U.: Addressing Big Data Problem Using Hadoop and Map Reduce. In: Nirma University International Conference on Engineering, IEEE-Ahemdabad (2012)
Pandey, S.K., Dubey, N.K., Sharma, S.: A study on string matching methodologies. Int. J. Comput. Sci. Inf. Technol. 5(3), 4732–4735 (2014)
Dwivedi, K., Dubey S.K.: Analytical review on Hadoop distributed file system. In: 5th International Conference—Confluence The Next Generation Information Technology Summit (Confluence), IEEE (2014)
Pandey, S., Tokekar, V.: Prominence of map reduce in big data processing. In: 4th International Conference on Communication Systems and Network Technologies, IEEE (2014)
Hinson J.: Simulation of Map Reduce with the Hash of Hashes Technique, pp. 1748–2014. Accenture Life Sciences, Berwyn, PA, USA (2012)
Singla, N., Garg, D.: String matching algorithms and their applicability in various applications. Int. J. Soft Comput. Eng. 1(6), 218–222 (2012)
Pandey, S.K., Tiwari, H.K., Tripathi, P.: Hybrid approach to reduce time complexity of string matching algorithm using hashing with chaining. In: Proceedings of International Conference on ICT for Sustainable Development, vol. 1, pp. 185–193 (2015)
Yuan, L.: An improved algorithm for Boyer–Moore String matching in Chinese information processing. In: International Conference on Computer Science and Service System (CSSS), IEEE (2011)
Katsoulis, S.: Implementation of parallel Hash Join algorithms over Hadoop. Master of Science, School of Informatics, University of Edinburgh (2011)
Fuyao, Z.: A string matching algorithm based on efficient hash function. In: International Conference on Information Engineering and Computer Science, IEEE (2009)
Charras, C., Lecroq, T.: Exact String Matching Algorithm Animation Java, Laboratoire d’Informatique de Rouen, Faculté des Sciences et des Techniques, Université de Rouen, Mont-Saint-Aignan Cedex, France. http://www-igm.univmlv.fr/~lecroq/string/node1.html
Zha, X., Sahni, S.: Multipattern string matching on a GPU. In: IEEE Symposium on Computers and Communications, IEEE (2011)
Sunarso, F., Venugopal, S., Lauro, F.: Scalable Protein Sequence Similarity Search using Locality-Sensitive Hashing and Map Reduce. School of Computer Science and Engineering, The University of New South Wales, Australia (2013)
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Pandey, S.K., Tripathi, P. (2018). Evolving the Efficiency of Searching Technique Using Map-Reduce Hashing Technique. In: Satapathy, S., Bhateja, V., Das, S. (eds) Smart Computing and Informatics . Smart Innovation, Systems and Technologies, vol 77. Springer, Singapore. https://doi.org/10.1007/978-981-10-5544-7_34
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DOI: https://doi.org/10.1007/978-981-10-5544-7_34
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