Crime Data Analysis Using Pig with Hadoop

  title={Crime Data Analysis Using Pig with Hadoop},
  author={Arushi Jain and Vishal Bhatnagar},
  journal={Procedia Computer Science},

Systematic review of crime data analytics

  • Sheena RewariWilliamjeet Singh
  • Computer Science
    2017 IEEE International Conference on Power, Control, Signals and Instrumentation Engineering (ICPCSI)
  • 2017
The voluminous data that's generated on a daily basis from varied sources by applying Big Data Analytics (BDA) helps to investigate sure trends that has to be discovered, in order that law and order are often maintained properly and there's a way of safety and well-being among the voters of the country.

crime reduction by big data analytics

Hadoop is an open source and durable platform for Big Data which can process huge data with high speed, scalability and reliability and Hadoop framework is capable to develop applications that run on clusters of computers and they could also perform complete statistical analysis for huge amounts of data.

Crime Analysis and Intelligence System Model Design using Big Data

The results analysis supports the hypothesis of the research by revealing the manual and traditional techniques of policing and crime analysis, and aims at designing a crime analysis and intelligence model using big data.

Real-time Twitter data analysis using Hadoop ecosystem

A method for finding recent trends in tweets and sentiment analysis on real-time tweets is proposed and conclusion can be drawn that Pig is more efficient than Hive as Pig takes less time for execution than Hive.

Comparative Analysis of Hadoop Tools and Spark Technology

This paper focuses on use of Hadoop tools and technologies like Map-Reduce, Apache flume, Apache Pig and Apache Spark technology and their comparative analysis.

Systematic Literature Review of Crime Prediction and Data Mining

The systematic review present in this study focuses on crime prediction and data mining as well as the techniques employed in the past studies, and it is found that more studies adopted supervised learning approaches toCrime prediction and control compared to other methods.

Empirical Aspects to Analyze Population of India using Apache Pig in Evolutionary of Big Data Environment

The gender ratio of India according to the age group of 0 to 24 from the year of 2001-2018 is analyzed through Pig Latin scripts and results are represented in the pictorial form.

Use of Hadoop for Sentiment Analysis on Twitter’s Big Data

This research is going to analyze nature of a particular person on the basis of their behavior on social sites using Hadoop, and the result shows sentiment analysis with good accuracy.

Monitoring the Impact of Economic Crisis on Crime in India Using Machine Learning

Abstract Trends of crimes in India keep changing with the growing population and rapid development of towns and cities. The rise in crimes at any place especially crimes against women, children and

Mining the crime data using naïve Bayes model

A massive number of documents on crime has been handled by police departments worldwide and today's criminals are becoming technologically elegant. One obstacle faced by law enforcement is the



Challenges and Opportunities with Big Data

The controversies and myths surrounding Big Data are explored, to try to explore the controversies and debunk the myths around Big Data.

Big Data: A Survey

The background and state-of-the-art of big data are reviewed, including enterprise management, Internet of Things, online social networks, medial applications, collective intelligence, and smart grid, as well as related technologies.

Technological applications of data mining and virtual reality: a literature survey and classification

An extensive survey is carried out, classifying the literature dealing with the synchronous application of data mining and virtual reality according to various topics by proposing a classification framework which identified the contribution of datamining in virtual reality and vice-versa.

Toward scalable internet traffic measurement and analysis with Hadoop

This paper presents a Hadoop-based traffic monitoring system that performs IP, TCP, HTTP, and NetFlow analysis of multi-terabytes of Internet traffic in a scalable manner and explains the performance issues related with traffic analysis MapReduce jobs.

Building LinkedIn's Real-time Activity Data Pipeline

The design and engineering problems the authors encountered in moving LinkedIn’s data pipeline from a batch-oriented file aggregation mechanism to a real-time publish-subscribe system called Kafka are discussed.

Evaluating MapReduce on Virtual Machines: The Hadoop Case

A series of experiments are conducted to measure and analyze the performance of Hadoop on VMs and outline several issues that will need to be considered when implementing MapReduce to fit completely in the cloud.

The pathologies of big data

Scale up your datasets enough and your apps come undone; scale up too much and they come undone.

Multicore-Enabled Smart Storage for Clusters

It is demonstrated that the integration of multicore-enabled smart storage with MapReduce clusters is a promising approach to improving overall performance of data-intensive applications on clusters.

Big data analysis: Issues and challenges

  • V. BhardwajR. Johari
  • Computer Science
    2015 International Conference on Electrical, Electronics, Signals, Communication and Optimization (EESCO)
  • 2015
How traditional DBMS couldn't compete with large data and big data began to emerge in a big way is explored and a broad comparison of different data mining techniques too has been presented.