Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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Author: search.filters.author.Patil, N.Subject: search.filters.subject.Hadoop

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    Genome Data Analysis Using MapReduce Paradigm
    (Institute of Electrical and Electronics Engineers Inc., 2015) Pahadia, M.; Srivastava, A.; Srivastava, D.; Patil, N.
    Counting the number of occurences of a substringin a string is a problem in many applications. This paper suggests a fast and efficient solution for the field of bioinformatics. Ak-mer is a k-length sub string of a biological sequence. K-mercounting is defined as counting the number of occurences of all the possible k-mers in a biological sequence. K-mer counting has uses in applications ranging from error correction of sequencing reads, genome assembly, disease prediction and feature extraction. The current k-mer counting tools are both time and space costly. We provide a solution which uses MapReduce and Hadoop to reduce the time complexity. After applying the algorithms on real genome datasets, we concluded that the algorithm using Hadoopand MapReduce Paradigm runs more efficiently and reduces the time complexity significantly. © 2015 IEEE.
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    Frequent pattern mining on stream data using Hadoop CanTree-GTree
    (Elsevier B.V., 2017) Kusumakumari, V.; Sherigar, D.; Chandran, R.; Patil, N.
    The need for knowledge discovery from real-time stream data is continuously increasing nowadays and processing of transactions for mining patterns needs efficient data structures and algorithms. We propose a time-efficient Hadoop CanTree-GTree algorithm, using Apache Hadoop. This algorithm mines the complete frequent item sets (patterns) from real time transactions, by utilizing the sliding window technique. These are used to mine for closed frequent item sets and then, association rules are derived. It makes use of two data structures - CanTree and GTree. The results show that the Hadoop implementation of the algorithm performs 5 times better than in Java. © 2017 The Author(s).
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    Mining closed colossal frequent patterns from high-dimensional dataset: Serial versus parallel framework
    (Springer Verlag service@springer.de, 2018) Sureshan, S.; Penumacha, A.; Jain, S.; Vanahalli, M.; Patil, N.
    Mining colossal patterns is one of the budding fields with a lot of applications, especially in the field of bioinformatics and genetics. Gene sequences contain inherent information. Mining colossal patterns in such sequences can further help in their study and improve prediction accuracy. The increase in average transaction length reduces the efficiency and effectiveness of existing closed frequent pattern mining algorithm. The traditional algorithms expend most of the running time in mining huge amount of minute and midsize patterns which do not enclose valuable information. The recent research focused on mining large cardinality patterns called as colossal patterns which possess valuable information. A novel parallel algorithm has been proposed to extract the closed colossal frequent patterns from high-dimensional datasets. The algorithm has been implemented on Hadoop framework to exploit its inherent distributed parallelism using MapReduce programming model. The experiment results highlight that the proposed parallel algorithm on Hadoop framework gives an efficient performance in terms of execution time compared to the existing algorithms. © Springer Nature Singapore Pte Ltd. 2018.