Assistant Professor
Areas of Interest : Data Mining,Pattern identification,Big Data
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  • Designation : Assistant Professor
  • Mobile No : 04842463825
  • Date Of joining : 19-07-2017

Sequential pattern mining and Document analysis is an important data mining problem in Big Data with broad applications. This paper investigates a specific framework for managing distributed processing in dataset pattern match and document analysis context. MapReduce programming model on a Hadoop cluster is highly scalable and works with commodity machines with integrated mechanisms for fault tolerance. In this paper, we propose a Knuth Morris Pratt based sequential pattern matching in distributed environment with the help of Hadoop Distributed File System as efficient mining of sequential patterns. It also investigates the feasibility of partitioning and clustering of text document datasets for document comparisons. It simplifies the search space and acquires a higher mining efficiency. Data mining tasks has been decomposed to many Map tasks and distributed to many Task trackers. The map tasks find the intermediate results and send to reduce task which consolidates the final result. Both theoretical analysis and experimental result with data as well as cluster of varying size shows the effectiveness of MapReduce model primarily based on time requirements

Qualification Details
M-TECH  MGU 2015
B-TECH mgu 2013



Working as Assistant Professor from july 2017

Publications Details

International “Efficient Pattern-Based Query search in Text Documents” International Journal of Modern

Trends in Engineering and Research(IJMTER). ,Volume 02, Issue 06, [July– 2015].

Projects Undertaken

1. Distributed Inverted Index based PDF Searching in MapReduce framework

2. Distributed Pattern Indexing model for Map-Reduce in Text Mining JAVA

3. Efficient pattern Based Query Search In Text Documents

4. Enhanced prediction and control of Heart attack using Genetic Algorithm

5. Gas Cylinder Tracking management System