Optimized Data Retrieval in Big Data Environment using PPFC Approach Thenmozhi K.*, Dr. Visalakshi N. Karthikeyani**, Dr. Shanthi S.*** *Research Scholar, Department of Computer Applications, Anna University, Chennai, Tamilnadu, India. thenmegu@gmail.com **Assistant Professor, Department of Computer Science, N.K.R. Government Arts College for Women, Namakkal, Tamilnadu, India. karthichitru@gmail.com ***Assistant Professor, Department of Computer Applications, Kongu Engineering College, Perundurai, Tamilnadu, India. shanthis@kongu.ac.in Online published on 23 March, 2017. Abstract Big data is an emerging field where its application increases in more number of fields like medical, transaction, marketing and purchasing assistance, multimedia, molecular biology as well as many others. Due to the enhancing nature of database it leads to the issues such as efficient and effective retrieval of data, meaning full data from a huge dataset. Parallel clustering reduces the time for processing the huge set of data for efficient data retrieval. In this proposed PPFC (Parallel Probabilistic Fuzzy Clustering) approach, parallel processing is implemented by probabilistic clustering using its mean and variance. Once clustering is formed for exact retrieval fuzzy logic is implemented, in general cluster creations produce maximum ratio of relevant content. All this process is implemented in LIR (Local Instance Repository) hence the proposed method retrieves the required data from a huge database (Big data) in effective and efficient manner. Top Keywords Big data, PPFC, parallel processing, probabilistic clustering and LIR. Top |