Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item Alignment based similarity distance measure for better web sessions clustering(Elsevier B.V., 2011) Poornalatha, G.; Raghavendra, P.S.The evolution of the internet along with the popularity of the web has attracted a great attention among the researchers to web usage mining. Given that, there is an exponential growth in terms of amount of data available in the web that may not give the required information immediately; web usage mining extracts the useful information from the huge amount of data available in the web logs that contain information regarding web pages accessed. Due to this huge amount of data, it is better to handle small group of data at a time, instead of dealing with entire data together. In order to cluster the data, similarity measure is essential to obtain the distance between any two user sessions. The objective of this paper is to propose a technique, to measure the similarity between any two user sessions based on sequence alignment technique that uses the dynamic programming method. © 2011 Published by Elsevier Ltd.Item Query-oriented unsupervised multi-document summarization on big data(Association for Computing Machinery acmhelp@acm.org, 2016) Sunaina; Kamath S․, S.S.Real time document summarization is a critical need nowadays, owing to the large volume of information available for our reading, and our inability to deal with this entirely due to limitations of time and resources. Oftentimes, information is available in multiple sources, offering multiple contexts and viewpoints on a single topic of interest. Automated multi-document summarization (MDS) techniques aim to address this problem. However, current techniques for automated MDS suffer from low precision and accuracy with reference to a given subject matter, when compared to those summaries prepared by humans and takes large time to create the summary when the input given is too huge. In this paper, we propose a hybrid MDS technique combining feature based algorithms and dynamic programming for generating a summary from multiple documents based on user provided query. Further, in real-world scenarios, Web search serves up a large number of URLs to users, and the work of making sense of these with reference to a particular query is left to the user. In this context, an efficient parallelized MDS technique based on Hadoop is also presented, for serving a concise summary of multiple Webpage contents for a given user query in reduced time duration. © 2016 ACM.Item Process Logo: An Approach for Control-Flow Visualization of Information System Process in Process Mining(Springer Science and Business Media Deutschland GmbH, 2022) Manoj Kumar, M.V.; Bs, B.S.; Sneha, H.R.; Thomas, L.; Annappa, B.; Vishnu Srinivasa Murthy, Y.V.S.This paper proposes a new technique named “Process Logo†for visualizing the causal relationship between the activities of a process (Control flow). Traditional process mining algorithms rely on representing the activity as a sequence of operations modeled using nodes and edges, as the number of activities increases, the representation of the entire control flow becomes quite tedious. Process logo is a compact yet highly informative method for visually representing the process model. It visually summarizes the number of activities, sequence of execution, relative significance, and dependency between activities. It uses a dynamic programming method—sequence alignment and clustering approach with Levenshtein measure as a distance measure. The proposed method is evaluated on the synthetic event log, the experimental result is promising. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.