Big Data Analytics Framework for Predictive Analytics using Public Data with Privacy Preserving

Big Data Analytics has changed the way organizations make decisions, manage business processes, and create new products and services. Business analytics is the use of data, information technology, statistical analysis, and quantitative methods and models to support organizational decision making and problem solving. The main categories of business analytics are descriptive analytics, predictive analytics, and prescriptive analytics. Big Data is data that exceeds the processing capacity of conventional database systems and is typically defined by three dimensions known as the Three V's: Volume, Variety, and Velocity. Big Data brings big challenges. Big Data not only has influenced the analytics that are utilized but also has affected technologies and the people who use them. At the same time Big Data brings challenges, it presents opportunities. Those who embrace Big Data and effective Big Data Analytics as a business imperative can gain competitive advantage.

Privacy-Preserving Cryptographic Model for Big Data Analytics

Services and Business Process Reengineering - Privacy and Security Issues in Big Data ◽

10.1007/978-981-16-1007-3_7 ◽

2021 ◽

pp. 109-127

Author(s):

Lambodar Jena ◽

Rajanikanta Mohanty ◽

Mihir Narayan Mohanty

Keyword(s):

Big Data ◽

Data Analytics ◽

Big Data Analytics ◽

Privacy Preserving

Privacy-preserving big data analytics a comprehensive survey

Journal of Parallel and Distributed Computing ◽

10.1016/j.jpdc.2019.08.007 ◽

2019 ◽

Vol 134 ◽

pp. 207-218 ◽

Cited By ~ 9

Author(s):

Hong-Yen Tran ◽

Jiankun Hu

Keyword(s):

Big Data ◽

Data Analytics ◽

Big Data Analytics ◽

Privacy Preserving ◽

Comprehensive Survey

Privacy Preserving Unstructured Big Data Analytics: Issues and Challenges

Procedia Computer Science ◽

10.1016/j.procs.2016.02.020 ◽

2016 ◽

Vol 78 ◽

pp. 120-124 ◽

Cited By ~ 11

Author(s):

Brijesh B. Mehta ◽

Udai Pratap Rao

Keyword(s):

Big Data ◽

Data Analytics ◽

Big Data Analytics ◽

Privacy Preserving

Privacy-Preserving in Big Data Analytics: State of the Art

SSRN Electronic Journal ◽

10.2139/ssrn.3713826 ◽

2020 ◽

Author(s):

Hidayath Ali Baig ◽

Dr. Yogesh Kumar Sharma ◽

Syed Zakir Ali

Keyword(s):

Big Data ◽

Data Analytics ◽

State Of The Art ◽

Big Data Analytics ◽

Privacy Preserving

Optimal Privacy Preserving Technique Over Big Data Analytics Using Oppositional Fruit Fly Algorithm

Recent Advances in Computer Science and Communications ◽

10.2174/2213275911666181119113913 ◽

2020 ◽

Vol 13 (2) ◽

pp. 283-295

Author(s):

Ajmeera Kiran ◽

Vasumathi Devara

Keyword(s):

Big Data ◽

Data Analytics ◽

Input Data ◽

Clustering Algorithm ◽

Big Data Analytics ◽

Fruit Fly ◽

Privacy Preserving ◽

Sensitive Information ◽

Convolution Process ◽

Fuzzy C Means Clustering

Background: Big data analytics is the process of utilizing a collection of data accompanied on the internet to store and retrieve anywhere and at any time. Big data is not simply a data but it involves the data generated by variety of gadgets or devices or applications. Objective: When massive volume of data is stored, there is a possibility for malevolent attacks on the searching data are stored in the server because of under privileged privacy preserving approaches. These traditional methods result in many drawbacks due to various attacks on sensitive information. Hence, to enhance the privacy preserving for sensitive information stored in the database, the proposed method makes use of efficient methods. Methods: In this manuscript, an optimal privacy preserving over big data using Hadoop and mapreduce framework is proposed. Initially, the input data is grouped by modified fuzzy c means clustering algorithm. Then we are performing a map reduce framework. And then the clustered data is fed to the mapper; in mapper the privacy of input data is done by convolution process. To validate the privacy of input data the recommended technique utilizes the optimal artificial neural network. Here, oppositional fruit fly algorithm is used to enhancing the neural networks. Results: The routine of the suggested system is assessed by means of clustering accuracy, error value, memory, and time. The experimentation is performed by KDD dataset. Conclusion: A result shows that our proposed system has maximum accuracy and attains the effective convolution process to improve privacy preserving.

Predict and Surveil

10.1093/oso/9780190684099.001.0001 ◽

2020 ◽

Cited By ~ 1

Author(s):

Sarah Brayne

Keyword(s):

Big Data ◽

Law Enforcement ◽

Los Angeles ◽

Data Analytics ◽

Predictive Analytics ◽

Civil Liberties ◽

Big Data Analytics ◽

Police Department ◽

Law Enforcement Agencies ◽

Criminal Suspects

The scope of criminal justice surveillance, from policing to incarceration, has expanded rapidly in recent decades. At the same time, the use of big data has spread across a range of fields, including finance, politics, health, and marketing. While law enforcement’s use of big data is hotly contested, very little is known about how the police actually use it in daily operations and with what consequences. This book offers an inside look at how police use big data and new surveillance technologies, leveraging on-the-ground fieldwork with one of the most technologically advanced law enforcement agencies in the world—the Los Angeles Police Department. Drawing on original interviews and ethnographic observations from over two years of fieldwork with the LAPD, the text examines the causes and consequences of big data and algorithmic control. It reveals how the police use predictive analytics and new surveillance technologies to deploy resources, identify criminal suspects, and conduct investigations; how the adoption of big data analytics transforms police organizational practices; and how the police themselves respond to these new data-driven practices. While big data analytics has the potential to reduce bias, increase efficiency, and improve prediction accuracy, the book argues that it also reproduces and deepens existing patterns of inequality, threatens privacy, and challenges civil liberties.

An Evaluation of Big Data Analytics Projects and the Project Predictive Analytics Approach

Oriental journal of computer science and technology ◽

10.13005/ojcst12.04.01 ◽

2020 ◽

Vol 12 (4) ◽

pp. 132-146

Author(s):

Gabriel Kabanda

Keyword(s):

Big Data ◽

Data Analytics ◽

Predictive Analytics ◽

Big Data Analytics ◽

Document Analysis ◽

Heterogeneous Data ◽

Discourse Theory ◽

Mining Machine ◽

Data Types ◽

Qualitative Research Methodology

Big Data is the process of managing large volumes of data obtained from several heterogeneous data types e.g. internal, external, structured and unstructured that can be used for collecting and analyzing enterprise data. The purpose of the paper is to conduct an evaluation of Big Data Analytics Projects which discusses why the projects fail and explain why and how the Project Predictive Analytics (PPA) approach may make a difference with respect to the future methods based on data mining, machine learning, and artificial intelligence. A qualitative research methodology was used. The research design was discourse analysis supported by document analysis. Laclau and Mouffe’s discourse theory was the most thoroughly poststructuralist approach.

Survey of the Different Type of Data Analytics Algorithms

International Journal of Strategic Information Technology and Applications ◽

10.4018/ijsita.2017010104 ◽

2017 ◽

Vol 8 (1) ◽

pp. 50-64 ◽

Cited By ~ 1

Author(s):

Michael Kevin Hernandez

Keyword(s):

Big Data ◽

Data Analytics ◽

Predictive Analytics ◽

Big Data Analytics ◽

High Volume ◽

Data Driven ◽

Advantages And Disadvantages ◽

Definition Of ◽

Data Driven Decisions ◽

Quantitative Definition

From as early as 1854 to today, society has been gathering, processing, transforming, modeling and visualizing data to help drive data-driven decisions. The qualitative definition of big data can be defined more conclusively as data that has high volume, velocity, and variety. Whereas, the quantitative definition of big data does vary with respect to time due to the dependence of the time's technology and processing capabilities. However, making use of that big data to facilitate data-driven decisions, one should employ either descriptive, predictive, or prescriptive analytics. This article has discussed and summarized the advantages and disadvantages of the algorithms that fell under descriptive and predictive analytics. Given the sheer number of the different types of algorithms and the amount of versatile data mining software available sometimes, the best big data analytics results can come from mixing two to three of the mentioned algorithms.