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Prof. Dr. M. Jarke
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Arnab Chakrabarti, M.Sc. - Theses


Master
A Neural Network Model for Visualization Recommendation System - Running
A Recommendation Tool for Data Visualization using Artificial Neural Networks
Master
A Visualization Recommendation Tool for Production Systems - Running
Supervised by Prof. Dr. Christoph Quix, Günther Schuh; Advisor(s): Arnab Chakrabarti, M.Sc., Frederick Sauermann (WZL, RWTH)
Bachelor
Comparison of Open Source Big Data Integration tools - Finished
Completed by Frauke Schattner in 2019; Supervised by Prof. Dr. Matthias Jarke, Prof. Dr. Christoph Quix; Advisor(s): Arnab Chakrabarti, M.Sc.
Master
Feature Clustering and Visualization of High Dimensional Data using Clique Cover Theory - Finished
Completed by Das, Abhijeet in 2019; Supervised by Prof. Dr. Christoph Quix; Advisor(s): Arnab Chakrabarti, M.Sc., Dr. Michael Cochez
Approaches such as clustering and classification that are analytically or computationally manageable in low dimensions become intractable as the dimensions increases. This happens because of a phenomenon known as “the curse of dimensionality” which is commonly observed in high dimensional data. Thus the aim of this thesis is to come up with a novel approach for feature clustering, selection, and visualization using the graph theoretical approach of Clique Covers.
Master
Master
Efficient Visualization of High Dimensional Data Sets - Finished
Completed by Oxana Kamidova in 2017; Supervised by Prof. Dr. Matthias Jarke, Prof. Dr. Christoph Quix; Advisor(s): Arnab Chakrabarti, M.Sc.
Data in high dimension are difficult to visualize and understand. With an increasing growth of such high dimensional data sets it is imperative to look into the current state of the art algorithms and analyze how they handle such challenges.
Bachelor
Master
A Comparative Study of Data Transformation Technologies between Heterogenous Data Stores - Finished
Completed by Jaypal, Manasi in 2016; Supervised by Prof. Dr. Matthias Jarke, Prof. Dr. Christoph Quix; Advisor(s): Arnab Chakrabarti, M.Sc.
A big data ecosystem hosts a multitude of technologies that offer different processing capabilities to satisfy every organisations’s requirement. The aim of this thesis is to compare the data transformation technologies between the most widely used heterogeneous data stores. This comparative study will provide an effective way of selecting the most efficient tools and technologies for transforming data for different players in the industry willing to exchange electronic data directly among themselves.
Person Publications

Arnab Chakrabarti, Tanuj Kulshrestha, Christoph Quix

A Visualization System for High Dimensional Data Streams using Complex Event Processing

Information Visualization of Geospatial Networks, Flows and Movement(MoVIS2020) held in conjugation with IEEE VIS2020

Arnab Chakrabarti, Christoph Quix, Sandra Geisler, Jaroslav Pullmann, Matthias Jarke

Goal-Oriented Modelling of Relations and Dependencies in Data Marketplaces

Proceedings of the 11th International i* Workshop co-located with the 30th International Conference on Advanced Information Systems Engineering (CAiSE 2018)

Arnab Chakrabarti, Manasi Jayapal

Data Transformation Methodologies between Heterogeneous Data Stores : A Comparative Study

Proceedings of the 6th International Conference on Data Management Technologies and Applications, DATA 2017, Madrid, Spain

More publications…