Meenakshi Sundaram S

Work place: GSSS Institute of Engineering and Technology for Woman / Department of CSE, Mysuru, 570016, India



Research Interests: Computational Science and Engineering, Computer systems and computational processes, Network Architecture, Network Security, Data Mining, Data Structures and Algorithms


Meenakshi Sundaram S, received the B.E. degree in Computer Science and Engineering from Bharathidasan University, Tiruchirappalli, Tamilnadu, India in 1989 and the M.E. degree in VLSI Systems from National Institute of Technology, Tiruchirappalli in 2006. He obtained Ph.D. degree in Information and Communication Technology from Anna University, Chennai, India in 2014. He has 28 years of experience in teaching and research. Currently he is working as Professor and Head in the Department of Computer Science and Engineering, GSSS Institute of Engineering and Technology for Woman, Mysuru, and Karnataka, India. He has published   32 research papers International Journals and Conferences. He is presently guiding 5 research scholars for Ph.D. from Visvesvaraya Technological University, Belagavi, and Karnataka State, India.  His current research includes Data Mining Concepts, Data Warehouse, Networking, Wireless Sensor Network, Cryptosystem and Network Security.

Author Articles
A New Dynamic Data Cleaning Technique for Improving Incomplete Dataset Consistency

By Sreedhar Kumar S Meenakshi Sundaram S

DOI:, Pub. Date: 8 Sep. 2017

This paper presents a new approach named Dynamic Data Cleaning (DDC) aims to improve incomplete dataset consistency by identifying, reconstructing and removing inconsistent data objects for future data analysis process. The proposed DDC approach consists of three methods:  Identify Normal Object (INO), Reconstruct Normal Object (RNO) and Dataset Quality Measure (DQM).  The first method INO divides the incomplete dataset into normal objects and abnormal objects (outliers) based on degree of missing attributes values in each individual object. Second, the  (RNO) method reconstructs missed attributes values in the normal objects by the closest object based on a distance metric and removes inconsistent data objects (outliers) with higher missed data. Finally, the DQM method measures the consistency and inconsistency among the objects in improved dataset with and without outlier. Experimental results show that the proposed DDC approach is suitable to identify and reconstruct the incomplete data objects for improving dataset consistency from lower to higher level without user knowledge.

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