An analysis of Crime data under Apache Pig on Big Data

Monika, .; Bhat, Aruna

doi:10.1109/i-smac47947.2019.9032565

Cited by 8 publications

(2 citation statements)

References 13 publications

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“…Gupta and Barhaiya (2021) used Apache Pig and Pig Latin to examine the crime rates in five different Indian states over five years. The authors remarked that “Apache Pig can additionally be optically discerned as this immensely colossal data processed in a short time with precision.” Monika and Bhat (2019) adopted a state-of-the-art method using a crime data set and analyzed it with Apache Pig and Jain and Bhatnagar (2016) also considered Apache Pig for their study of crime data.…”

Section: Literature Reviewmentioning

confidence: 99%

Developing a big data analytics platform using Apache Hadoop Ecosystem for delivering big data services in libraries

Singh

2024

DLP

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Purpose Although the challenges associated with big data are increasing, the question of the most suitable big data analytics (BDA) platform in libraries is always significant. The purpose of this study is to propose a solution to this problem. Design/methodology/approach The current study identifies relevant literature and provides a review of big data adoption in libraries. It also presents a step-by-step guide for the development of a BDA platform using the Apache Hadoop Ecosystem. To test the system, an analysis of library big data using Apache Pig, which is a tool from the Apache Hadoop Ecosystem, was performed. It establishes the effectiveness of Apache Hadoop Ecosystem as a powerful BDA solution in libraries. Findings It can be inferred from the literature that libraries and librarians have not taken the possibility of big data services in libraries very seriously. Also, the literature suggests that there is no significant effort made to establish any BDA architecture in libraries. This study establishes the Apache Hadoop Ecosystem as a possible solution for delivering BDA services in libraries. Research limitations/implications The present work suggests adapting the idea of providing various big data services in a library by developing a BDA platform, for instance, providing assistance to the researchers in understanding the big data, cleaning and curation of big data by skilled and experienced data managers and providing the infrastructural support to store, process, manage, analyze and visualize the big data. Practical implications The study concludes that Apache Hadoops’ Hadoop Distributed File System and MapReduce components significantly reduce the complexities of big data storage and processing, respectively, and Apache Pig, using Pig Latin scripting language, is very efficient in processing big data and responding to queries with a quick response time. Originality/value According to the study, there are significantly fewer efforts made to analyze big data from libraries. Furthermore, it has been discovered that acceptance of the Apache Hadoop Ecosystem as a solution to big data problems in libraries are not widely discussed in the literature, although Apache Hadoop is regarded as one of the best frameworks for big data handling.

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Section: Literature Reviewmentioning

confidence: 99%

Developing a big data analytics platform using Apache Hadoop Ecosystem for delivering big data services in libraries

Singh

2024

DLP

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“…4-Veracity: Incompatible and discordant are identified Details that can make data messy at times, consistency Accuracy and precision are hard to handle. 5-Value: It is below 5Vs when there is a massive amount of The company's invaluable data and all, unless you turn it into something useful 4 .…”

Section: Characteristic Of Big Datamentioning

confidence: 99%

New and Existing Approaches Reviewing of Big Data Analysis with Hadoop Tools

Mutasher,

Aljuboori

2022

Baghdad Sci.J

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Everybody is connected with social media like (Facebook, Twitter, LinkedIn, Instagram…etc.) that generate a large quantity of data and which traditional applications are inadequate to process. Social media are regarded as an important platform for sharing information, opinion, and knowledge of many subscribers. These basic media attribute Big data also to many issues, such as data collection, storage, moving, updating, reviewing, posting, scanning, visualization, Data protection, etc. To deal with all these problems, this is a need for an adequate system that not just prepares the details, but also provides meaningful analysis to take advantage of the difficult situations, relevant to business, proper decision, Health, social media, science, telecommunications, the environment, etc. Authors notice through reading of previous studies that there are different analyzes through HADOOP and its various tools such as the sentiment in real-time and others. However, dealing with this Big data is a challenging task. Therefore, such type of analysis is more efficiently possible only through the Hadoop Ecosystem. The purpose of this paper is to analyze literature related analysis of big data of social media using the Hadoop framework for knowing almost analysis tools existing in the world under the Hadoop umbrella and its orientations in addition to difficulties and modern methods of them to overcome challenges of big data in offline and real -time processing. Real-time Analytics accelerates decision-making along with providing access to business metrics and reporting. Comparison between Hadoop and spark has been also illustrated.

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Big Data Analytics for Market Prediction via Consumer Insight

Soni

Sharma

2021

Big Data Analytics in Cognitive Social Media and Literary Texts

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An analysis of Crime data under Apache Pig on Big Data

Cited by 8 publications

References 13 publications

Developing a big data analytics platform using Apache Hadoop Ecosystem for delivering big data services in libraries

Developing a big data analytics platform using Apache Hadoop Ecosystem for delivering big data services in libraries

New and Existing Approaches Reviewing of Big Data Analysis with Hadoop Tools

Big Data Analytics for Market Prediction via Consumer Insight

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