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Data Analytics and Social Computing Research Group

Data Analytics and Social Computing Research Group

NESCOT Computing Department has an active research group called Data Analytics and Social Computing. It aims to perform both theoretical and applied ‘cutting edge’ research. The Research Group works with local and multinational companies to try to solve their problems by performing original research to provide effective and efficient solutions. The main focus areas of the group are:

  • Big data for health and businesses 
  • Social computing

Big Data for health and businesses

In the last few years data volumes grew by 800% and 80% of the data is unstructured data, the question is: is an organisation both in public and private sectors up to the challenge of seizing the big data opportunity? The research group will focus on providing theories and frameworks to be used by organisations to gain benefit from big data. Some of the sub areas on which the research group focus are:

  • Implementing Big Data architectures 
  • Data security and privacy
  • Business intelligence and analytics
  • Big data and emerging technologies 
  • Harnessing insights from unstructured data
  • Data-driven mobile and web applications 
  • Role of big data for health sector

Social Computing

Social computing is a wide research area which is situated at the intersection of computer science and social science. It involves harnessing human intelligence for computational tasks and the design of computational systems which support social behaviour and interactions. TopCoder a code development community, the Amazon Mechanical Turk platform a market for “human intelligence tasks”, lowa Electronic Markets a group for eliciting and aggregating information for political events, and Zooiverse a crowdsourcing platform for scientific problems are all examples of social computing.
The group will focus on methodologies for designing effective and efficient social computing systems and their limitations. It will focus on both theoretical and experimental research aimed at understanding the design of social computing and behavioural models of participants of social computing systems.

Members of the group

The group members are:

  • Sage Lal (Head of Computing, Media and Games)
  • Dr. Imran U Khan (High Level Computing Lecturer) 
  • Dr. Rajiv Attanayake (High Level Computing Lecturer) 
  • Joseph Hanke (Deputy Head of Computing)
  • Dr. Isabella Jones (High Level Computing Lecturer)

Visiting members:


UKIERI is an Education and Research initiative between UK and India. It aims to enhance the educational links between the two countries.
The group has successfully completed a UKIERI project. The aim of the project was to develop and implement a robust, secure and integrated learning system for the Government Polytechnic College Palakkad Kerala, India. The system will be later extended to the whole state of Kerala and about one million students and staff will benefit from it.


1. Bashir, S., Usoro, A., & Khan, I. (2012). “Knowledge sharing in virtual communities: a conceptual view of the societal culture perspective”. School of Computing & Information Systems Journal. 16(1):39-46.
2. Khan, U. I., Usoro, A., & Majewski, G. (2012). “Organisational culture model for assessing knowledge sharing and IT projects”. IJHCITP. 3(2):63-84.
3. Majewski, G., & Usoro, A., & Khan, I. (2010). “Knowledge sharing in immersive virtual communities of practice”. VINE. 41(1): 41-62.
4. Khan, U. I., & Usoro. A (2010). “Trust as an Organization Factor that Influences Knowledge Sharing in Virtual Communities”. International Journal of Human Capital and IT Professionals (IJHCITP). 1(4): 1-21.
5. Khan, U. I., Usoro, A., Majewski, G., & Mathew, K. (2010). “An Organizational Culture Model for Comparative Studies: A Conceptual View”. International Journal of Global Business. 3(1):53-82.
6. Khan, I., & Usoro, A. (2008). “Operationlization of organizational culture as a major influence on knowledge sharing”. School of Computing & Information Systems Journal 12(3): 27-37.
7. J.Isabella,R.M.Suresh  “Improving  User  Experience  by mining Usage patterns” published in the International Journal of Computer science and Information technologies, Volume 2(6),2011,2725-2727  (ISSN:0975-9646)
8. J.Isabella,R.M.Suresh  “A Radial Basis Function Approach for Opinion Mining “ is  published    in the International Journal of Data mining and Emerging Technologies, Year 2011, Volume-1,Issue-2(November) (ISSN 2249-3220).
9. J.Isabella,R.M.Suresh “Mining Two Class Opinions Using Optimized Recurrent Neural Network “ in the   International Journal of Modern Engineering Research, Year 2012 Volume 2, Issue 5 (Sep.-Oct. 2012) (ISSN 2249-6645) indexed in ANED-DDL (Digital Data link) number is 02.6645/IJMER-AY2532653270
10. J.Isabella, R.M.Suresh “An Optimized Support Vector Machine Classifier for opinion Mining “in the European Journal of Scientific Research (ISSN 1450-216X) Vol. 92 No 4 December, 2012, pp.566-571.
11. J.Isabella, R.M.Suresh “Analysis and evaluation of Feature selectors in opinion mining “in the  Indian Journal of Computer Science and Engineering (IJCSE) Vol. 3 No.6 Dec 2012-Jan 2013 (ISSN : 0976-5166 )
12. J.Isabella,R.M.Suresh “ Application of Feature extraction techniques to unstructured  texts”  in the  National Journal of  Computing and    management ,Sathyabama University,  Volume 3,Issue 2 (ISSN 0975-7295).
13. J.Isabella,R.M.Suresh Efficacy of Feature Selectors in Opinion Mining” in Journal of Computer Science year 2012(Sep-Oct)issue.
14. J.Isabella, R.M.Suresh “An SVM Classifier using Correlation based Feature Selection for Opinion Mining,” ICISCEA   International conference,in Singapore on  November 28, 2011(ISSN  No: 2091-0266)
15. J.Isabella,R.M.Suresh  “Opinion mining using Correlation based feature selection “, 2nd annual international conference on Software engineering and Applications ,Singapore. on     December 12th and 13th of 2011(ISSN : 2251-2217)
16. J.Isabella,R.M.Suresh  “A comparative  study on the application of classifier for Opinion   Mining using correlation based feature selection “ in the  National Conference On Recent trends and advancements in Information  Technology, Karpagam University, on  October  20,2012.( ISBN 978-1-4675-4610-2)
17. J.Isabella, R.M.Suresh, S.Sanoj Subramanian “Impact of Feature selectors in opinion mining “in the   National Conference On Frontiers in Applied sciences and Computer Technology” held at National Institute of Technology, Trichy, on May 23-24,2013.
18. J.Isabella,R.M.Suresh” Comparison of Feature extraction techniques for opinion Mining” in the National Conference On Fuzzy Soft Computing Mathematical Analysis (FSCMA) from 4th  to 5th October 2012.







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