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Performing Process Mining by analyzing event logs generated by various systems is a very computation and I/O intensive task. Distributed computing and Big Data processing frameworks make it possible to distribute all kinds of computation tasks to multiple computers instead of performing the whole task in a single computer. This paper assesses whether contemporary structured query language (SQL) supporting...
Performing Process Mining by analyzing event logs generated by various systems is a very computation and I/O intensive task. Distributed computing and Big Data processing frameworks make it possible to distribute all kinds of computation tasks to multiple computers instead of performing the whole task in a single computer. This paper assesses whether contemporary structured query language (SQL) supporting...
In this paper, we propose a high capacity text steganography method using Huffman compression. The forward email platform is used to hide the secret data. We make use of the number of characters used in email id to indicate the hidden secret data bits. So, to make optimal utilization of number of characters in email ids, the characters added to the email id to indicate the secret data bits are taken...
Inquiry based Inductive learning methodology is one of the best technique especially for the engineering students who are expected to solve real world problems. But it is very difficult to standardize a particular learning methodology for an institution with diverse attitude, diverse characteristics, diverse languages, diverse financial back grounds, and diverse cultural scenario with variable educational...
Itemset mining identifies group of frequent itemsets that signify possibly of relevant information. Unique constraints are usually forced to emphasis the analysis on most interestingness itemsets. In this paper we proposed unique constraint based mining on relational dataset. The constrained-based mining helps us to merge all itemsets, which are interrelated to each other. Specifically it chooses...
In today's digital world scenario, digital data is coming in and going out faster than ever before. This data is of no use until we extract some useful content from it. But, it is impractical and inefficient to use traditional database management techniques on big data. That's why, big data technologies like Hadoop comes to existence. Hadoop is an open source framework, which can be used to process...
In this research study, our major interest is to test the significant association between selected variables which otherwise invisible. These variables have indirect impact on performance of students. We have devised out our own dataset for the experimental purpose. Our study has made these variables and their relationship visible. The results enable us to determine characteristics of learning environment...
Finding Strongly Connected Components (SCCs) in graphs is one of the important research topics of graph data mining. Traditional methods of finding SCCs need to fully load the whole graph into the main memory of a computer before actual processing. However, with the rapid growth of real-world graphs, the sizes of graphs easily exceed the main memory space of an ordinary computer. The distributed graph...
The paper proposes a new scheme named as WPP (Web Page Personalization) for effective web page recommendations. WPP consist of page hit count, total time spent in every link, number of downloads and link separation. Based on these parameters the personalization has been proposed. The system proposes a new implicit user feedback and event link access schemes for effective web page customization along...
Organization of transactional data is one of the important steps in Knowledge Discovery. Compact Pattern Tree (CPTree) organization of the data is apt for the FP-Tree, CAN-Tree, CATS-Tree etc., Construction of CPTree has been dealt within two phase method. This paper exploits the transactional data representation in a structured form using one of the data structures for subsequent representation of...
In view of today's information available, recent progress in data mining research has lead to the development of various efficient methods for mining interesting patterns in large databases. It plays a vital role in knowledge discovery process by analyzing the huge data from various sources and summarizing it into useful information. It is helpful for analyzing the volumes of data in different domains...
In this study a reversible multilevel data hiding algorithms using histogram has been proposed. Here multilevel hiding is used to improve hiding capacity and reduce the distortion. Using the property of reversibility the proposed method can recover the embedded message without distorting the original media. By evaluating joint imperceptibility and hiding capacity, three different approaches was carried...
Financial stock Data Analysis and future prediction in terms of Sentiments is great challenge in the big data research. Among the unlabelled opinion, opinion classification in terms of unsupervised learning algorithm will lead to classification error as data is sparse and high dimensional. To overcome this problem, the sentiment analysis to extract the opinion of each word in the stock data has been...
Documents usually contain temporal expressions such as ‘morning’ and ‘yesterday’, and they also often contain spatial expressions such as ‘house’ and ‘the South Pole’. Extracting temporal information or spatial information from the documents is important because such information can be useful for various applications. Although there have been many studies aiming at extracting temporal information...
We present an interactive graphical tool for assisted curation of knowledge bases from unstructured text data. Given text input, the user can create a knowledge base from scratch, including sub-tasks of entity mention annotation, matching mentions that refer to the same entity, and extracting relations between entities. The interface is designed to enable organizations to extract valuable knowledge...
Recent advances in using computer with different fields of sciences produced huge amounts of data. These data represent as an analysis tool and key to overcome many problems. Clustering is a primary process to analyze the data as well as, it's a preprocessing step before other techniques like classification. Density-Based clustering algorithms have advantages like clustering any arbitrary shapes and...
Thermal convection and fluid flow in porous media has gained increasing research interest in recent years due to the presence of porous media in many engineering applications. Rough set theory has been regarded as a powerful feasible and effective methodology in the performance of data mining and knowledge discovery activities. This paper introduce a method for building knowledge for the rate of heat...
Today internet has become easily accessible which allows the user to perform multiple tasks such as access information, do study, make friends, online shopping, search for anything they want and many more. Similarly people do use internet to know the better options and find out relevant alternatives of product, services, places like wise. But searching for better choices may become frustrating and...
Outlier detection is an important issue in the realm of data mining. Several applications relay on outlier detection such as intrusion detection, fraud detection, medical and public health data, image processing, etc. Clustering-based outlier detection algorithms are considered as the most important outlier detection approaches. They provide high detection rate, however, they suffer from high false...
Both educational data mining (EDM) and learning analytics (LA) focus on applying analytics and data mining techniques to extract useful information from large data sets. EDM is generally more interested in automated methods for discovery within the educational data while LA is relatively keen on applying human-led methods to understand the involved learning processes. Among the various fields of challenging...
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