Plagiarism or the expropriation of another author’s data and the presentation of it as one’s own, is a serious violation of ethics of professionalism. Attempting to take other person’s works, without proper citation is considered as one way of Plagiarism. With the rapid use of internet access and large amounts of big data, copying of content partially or fully has become a common practice. The proposed technique, map-reduced N-Tuple algorithm for distributed computing platform compares the number of attributes of the comparing tuples, at first. If the number of attributes is different, we are sure that the tuples cannot be equal. If the number of attributes is the same, we further sort the values inside of each tuple of both relations. This sorting is necessarily to make sure, that afterwards we can use the equal functionality, provided by Standard Library to find out, whether all corresponding pairs of two tuples compare equal. Here different capacity data sets are tested for plagiarism, which gives output within short time and more accuracy compared to the Standard Copy Analysis Mechanism. Our proposed algorithm is used to compare documents for processing big data using Hadoop and detect plagiarism for performance enhancement.

Plagiarism, N-Tuple, Big data, Hadoop, MapReduce

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