Swagata Paul
@tint.edu.in
Assistant Professor, Dept of CSE
Techno International New Town
EDUCATION
ME, PhD Submitted.
RESEARCH INTERESTS
Big Data Framework Security
Scopus Publications
Scopus Publications
Swagata Paul, Sajal Saha, and Radha Tamal Goswami
Springer Singapore
Swagata Paul, Sajal Saha, and R. T. Goswami
Springer Singapore
Sajib Chowdhury, Swagata Paul, Debraj Chatterjee, Somenath Mukherjee, Sandipan Ghosal, and Radha Tamal Goswami
IEEE
Two vital statistics of wireless network namely peak hour call initiated and call drop have been chosen to examine the self similarity and stationarity behaviour of typical wireless network data in this paper. The scaling pattern and nature of fluctuating frequency are exposed through these two parameters. For exposing the scaling nature of the time series that has been taken from the period 3rd March, 2005 to 31st October, 2015, from the local mobile switching server. Statistical methodologies like Rescaled Analysis (R/S) and General Hurst Estimation (GHE) method are being used to detect the scaling nature of the data-series. Both the time series represent the Short Range Dependency (SRD) and anti-persistency behaviour. The stationarity or non-stationarity behaviour of the time series have been examined by Kwiatkowski Phillips Schmidt Shin (KPSS) test and Continuous Wavelet Transform (CWT). Here both the time series shows non stationarity behaviour.
Nabanita Das, Swagata Paul, Bidyut Biman Sarkar, and Satyajit Chakrabarti
Springer Singapore
Bidyut Biman Sarkar, Swagata Paul, Barna Cornel, Noemi Rohatinovici, and Nabendu Chaki
Springer International Publishing
Swagata Paul, Nabanita Das, and Bidyut Biman Sarkar
IEEE
Building a big data processing cluster needs extra care on selecting right storage device, Operating System(OS) and their configuration. A wrong strategy may lead to a very slow cluster which becomes inefficient to processing data in considerable amount of time. In this work we will show how the performance varies with different setup using Hadoop Distributed File System(HDFS) and related tools.
Nizamuddin Laskar, Nabanita Das, and Swagata Paul
IEEE
Surveying knowledge distribution of different subjects among students is an important device for indirect assessment of student learning. To perform this survey, another important concept introduces-knowledge gap. The knowledge gap can result in an increased gap between student of lower and higher socioeconomic status. In this paper we have taken students' marks obtained in an examination with respect to some predefined subjects. Our objective is to find the most important characteristic-feature which is able to distinguish the student community very clearly. We determine the weak student sub class using Mahalanobis distance, Euclidian distance, SGPA1 and SGPA2 and the corresponding set of subjects using orthogonal transformation of variance-covariance matrix of marks vector into diagonal matrix. This set of subjects are the most important feature and then necessary action may be taken in form of additional training to reduce the overall knowledge gap.