Research Article

Analysis of Crime Data and finding frequent patterns using Hadoop and Data Analytic Techniques

by  Aniket B. Wakde, Shravani J. Uttarwar, Sudarshan D. Waydande, Purvesh Shende, Ganesh Deshmukh
journal cover
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 178 - Issue 27
Published: Jun 2019
Authors: Aniket B. Wakde, Shravani J. Uttarwar, Sudarshan D. Waydande, Purvesh Shende, Ganesh Deshmukh
10.5120/ijca2019918985
PDF

Aniket B. Wakde, Shravani J. Uttarwar, Sudarshan D. Waydande, Purvesh Shende, Ganesh Deshmukh . Analysis of Crime Data and finding frequent patterns using Hadoop and Data Analytic Techniques. International Journal of Computer Applications. 178, 27 (Jun 2019), 17-20. DOI=10.5120/ijca2019918985 

                        @article{ 10.5120/ijca2019918985,
                        author  = { Aniket B. Wakde,Shravani J. Uttarwar,Sudarshan D. Waydande,Purvesh Shende,Ganesh Deshmukh },
                        title   = { Analysis of Crime Data and finding frequent patterns using Hadoop and Data Analytic Techniques },
                        journal = { International Journal of Computer Applications },
                        year    = { 2019 },
                        volume  = { 178 },
                        number  = { 27 },
                        pages   = { 17-20 },
                        doi     = { 10.5120/ijca2019918985 },
                        publisher = { Foundation of Computer Science (FCS), NY, USA }
                        }
                        %0 Journal Article
                        %D 2019
                        %A Aniket B. Wakde
                        %A Shravani J. Uttarwar
                        %A Sudarshan D. Waydande
                        %A Purvesh Shende
                        %A Ganesh Deshmukh
                        %T Analysis of Crime Data and finding frequent patterns using Hadoop and Data Analytic Techniques%T 
                        %J International Journal of Computer Applications
                        %V 178
                        %N 27
                        %P 17-20
                        %R 10.5120/ijca2019918985
                        %I Foundation of Computer Science (FCS), NY, USA
Abstract

With continually increasing population and violations, rate of crime is also increasing. Analyzing such rapidly increasing data regularly is a huge issue for police department. This is extremely important to guard the residents of the nation from violations. Certain patterns must be discovered, examined and talked about to take well informed decisions so that law and orders can be kept up legitimately. Hadoop is one of an open source programming framework that supports the processing and storage of extremely large data sets in a distributed computing environment. Pig Latin is the scripting language to construct MapReduce programs for an Apache project which runs on Hadoop. The benefit of using Pig Latin is that fewer lines of code has to be written which reduces overall development and testing time. Therefore, pig technology works more efficiently than MapReduce which is presented in this paper using case study of Crime Data Analysis.

References
  • K. Goodhope, J. Koshy, et al, Building LinkedIn's Real-time Activity Data Pipeline, Data Engineering, volume 35, issue 2, pp. 33-45, 2012.
  • Yeonhee Lee and Youngseok Lee, Toward scalable internet traffic measurement and analysis with Hadoop, ACM SIGCOMM Computer Communication, 2013, volume 43, issue 1.
  • Doug Howe, Maria Costanzo, Petra Fey, Takashi Gojobori, Linda Hannick, Winston Hide, David P. Hill, Renate Kania, Mary Schaeffer, Susan St Pierre, Simon Twigger, Owen White and Seung Yon Rhee , Big data: The future of biocuration, Nature, international weekly journal of science 455, 47-50,4 September 2008
  • Clifford Lynch, Big data: How do your data grow?, Nature , international weekly journal of science ,455, 28-29
  • Adam Jacobs, The pathologies of big data, Communications of the ACM - A Blind Person's Interaction with Technology ,Volume 52 Issue 8, August 2009
  • Min Chen, Shiwen Mao and Yunhao Liu, Big Data: A Survey, Springer- Mobile Networks and Applications, Volume 19, Issue 2, pp 171-209, 2014
  • Alexandros Labrinidis and H. V. Jagadish, Challenges and opportunities with big data, ACM- Proceedings of the VLDB Endowment, Volume 5 Issue 12, August 2012
  • Shadi Ibrahim, Hai Jin, Lu Lu, Li Qi, Song Wu, and Xuanhua Shi, Evaluating MapReduce on Virtual Machines: The Hadoop Case, Springer: Cloud Computing Lecture Notes in Computer Science, Volume 5931, 2009, pp 519-528
  • Lecture Notes in Computer Science, 2013.
  • www.edureka.co/
  • https://www.wikipedia.org/
  • http://www.guru99.com/introduction-to-pig-and hive.html
  • .http://tutorialshadoop.com/pig-interview-questions-answers-part-3/
  • Aggarwal Sonal, and Vishal Bhatnagar, Technological applications of data mining and virtual reality: a literature survey and classification, International Journal of Intercultural Information Management, 2013.
  • Ngai, E.W.T, Application of data mining techniques in customer relationship management: A literature review and
  • classification", Expert Systems With Applications, 200903
  • Bhardwaj, Vibha, and Rahul Johari, Big data analysis: Issues and challenges, 2015 International Conference on Electrical Electronics Signals Communication and Optimization (EESCO), 2015.
  • Ding, Zhiyang, Xunfei Jiang, Shu Yin, Xiao Qin, Kai-Hsiung Chang, Xiaojun Ruan, Mohammed I. Alghamdi, and Meikang Qiu, Multicore-Enabled Smart Storage for Clusters, 2012 IEEE International Conference on Cluster Computing, 2012.
Index Terms
Computer Science
Information Sciences
No index terms available.
Keywords

Big Data Hadoop Map-Reduce Pig etc.

Powered by PhDFocusTM