Conference Papers

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    Equal channel angular pressing of aluminum-alumina insitu metal matrix composite
    (Trans Tech Publications Ltd ttp@transtec.ch, 2012) Athreya, C.N.; Mahesh, V.P.; Brahmakumar, M.; Rajan, T.P.D.; Prabhu, K.N.; Pai, B.C.; Gupta, R.K.; Ramkumar, P.
    The present investigation is on synthesis of in situ Al-alumina composite and to evaluate the effect of equal channel angular pressing on the refinement of the grain structure and enhancement in the hardness and the strength. The billets pressed in as cast condition has shown cracks during first pass. The billets pressed immediately after solution treatment for one pass and followed by ageing treatment immediately after pressing exhibited very high hardness of 125BHN against 95 BHN to that of the T6 condition of 6061 aluminium alloy. The microstructural refinement from 35 μm to 11 μm is obtained in annealed and ECAP 2 pass condition. © (2012) Trans Tech Publications.
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    An Approach for Efficient Graph Mining from Big Data Using Spark
    (Springer Science and Business Media Deutschland GmbH, 2023) Gupta, R.K.; Shetty D, D.; Chakraborty, S.
    Huge amount of data is generated and accumulated over the last decade, and therefore, the use of data mining techniques is required to extract usable information from these massive data sets. Gaining important connections between data helps in getting useful insights. Depiction of relationships between the data using graphical approach is observed to be a helpful method. It provides an effective technique for demonstrating the working in a variety of situations, including biological networks, social networks, Web networks, and so on. Clustering techniques used in graph mining can be helpful for accumulating significant information. In this paper, an approach for graph mining from big data in Spark (AGMBS) is proposed on the basis of label propagation. The suggested technique enhances the efficiency of the conventional label propagation algorithm by making it more resilient. In addition to this, AGMBS employs a sparse matrix as its primary data structure, resulting in quicker performance. Thereafter, GraphX is used for managing the processing of the graphical data. The experiments were conducted on two graph data sets from the real world, and it is observed that the suggested AGMBS gives faster results as compared to the best available clustering algorithms. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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    Temporal Variability Analysis of Domain Names using Graph Techniques on Big Data
    (Institute of Electrical and Electronics Engineers Inc., 2023) Gupta, R.K.; Shetty D, D.
    The internet domain name system (DNS) is essential to the internet infrastructure, facilitating communication between devices and applications. Over the time, changes in the web graph structure, which represents the interconnections between web pages, can provide valuable insights into the evolution of the internet ecosystem. By analyzing the dynamics of web graph connectivity, we can gain a deeper understanding of how the relationships between domains have evolved over the time. By analyzing the relationships between domain names and their connections in the web graph, we can track changes over the time, identify patterns and clusters, and better understand how the internet landscape is evolving. This research paper focuses on leveraging big data techniques to perform temporal variability analysis of domain names using graph techniques on website data. The main objective of this research is to identify clusters of related web pages using only URL information, and links between them are analyzed using web graphs built from the website database. The research focuses on three main aspects: extracting and cleaning URL data from the website data, analyzing the relationships between domain names to identify how they are related to each other, and finally, visualizing these relationships using graph visualization techniques. We first extract domain names from the dataset and construct a graph with domains as nodes and links representing the relationships between them. Our approach combines graph algorithms such as ensemble page-rank and label propagation to identify significant changes in the domain name system over the time. © 2023 IEEE.