Top Research Papers on Big Data Analytics

This panel will try to explore detailed analysis on big data analytics, a term used to identify the datasets that whose size is beyond the ability of typical database software tools to store, manage and analyze.

This research discusses big data, the available big data analytical tools, the need to use big data analytics with its benefits and challenges, and the need to use big data analytics with its benefits and challenges in a small island state.

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This survey aims to provide an insightful overview of state-of-the-art development in the Lie group machine learning, to comprehensively understand the state of the state, identify the most appropriate tools for particular applications, and identify directions for future research.

A large number of multi-view clustering algorithms are summarized, a taxonomy according to the mechanisms and principles involved is provided, and a few examples for how these techniques are used are given.

A novel automatic background subtraction algorithm for urban surveillance systems in which the computer can automatically renew an image as the new background image when no object is detected is proposed.

DHA-RS combines stacked denoising auto-encoders with neural collaborative filtering, which corresponds to the process of learning user and item features from auxiliary information to predict user preferences, and performs better than state-of-the-art methods.

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This article presents a comprehensive survey of the matrix completion methods used in recommendation systems, focusing on the mathematical models for matrix completion and the corresponding computational algorithms as well as their characteristics and potential issues.

A Hypertext-Induced Topic Search (HITS) based Topic-Decision method (TD-HITS), and a Latent Dirichlet Allocation (LDA) based Three-Step model (TS-LDA).

This chapter described technology that is used to handle the big data like, NoSQL, Hadoop, MangoDB, MapReduce, etc.

The concept of Big data is defined and the importance of Big Data Analytics is stressed in order to support decision for their strategies.

The definitions and the challenges of big data systems are described, a systematic framework decomposes the architecture ofbig data systems into four stages like data generation, data acquisition, data storage and data analytics and some solutions are discussed.

The Book give a description about Big Data Definitions, Characteristics, Applications and the Technology Hadoop which handles it and a detailed literature review is presented.

This paper presents a literature survey of the Big Data and Big Data analytics, a detailed survey of analytics platform like Apache Hadoop, and delineates the evaluation and research direction for the Big data System.

Big data analytics have the potential to identify efficiencies that can be made in a wide range of sectors, and to lead to innovative new products and services, greater competitiveness and economic growth.

This course introduces the student to concepts of big data management, database management, data mining techniques and the underlying statistics that support big data analytics and uses the programming language R as the primary tool for analysis.

This breakthrough book demonstrates the importance of analytics, defines the processes, highlights the tangible and intangible values and discusses how you can turn a business liability into actionable material that can be used to redefine markets, improve profits and identify new business opportunities.

Upon considering the analytics in the Big Data sector, predictive analytics and social media analytics are widely used for determining the pattern or trend which is about to happen.

Benefits of Spark over MapReduce are explained at great depth to reap benefits of in-memory speeds and Readers will also get an opportunity to get started with web based notebooks such as Jupyter, Apache Zeppelin and data flow tool Apache NiFi to analyze and visualize data.

This chapter focuses on understanding the meaning and concept ofbig data, analytics of big data, its role in healthcare, various application areas, trends and tools used to process big data along with open problem challenges.

This chapter proposes a support system that can guide the student to choose and to focus on the right course(s) based on their personal preferences, and provides the requisite information about educational frameworks and related data mining.

This article presents an opportunity to utilize pre-existing data to explore a novel way of assessing the impact of known health hazards, and demonstrates how big data analytics can be used in such a scenario to identify environmental public health risk.

This analysis illustrates that the Big Data analytics is a fast-growing, influential practice and a key enabler for the social business and is critical for success in the age of social media.

This book extensively explores all facets of Big Data Analytics and comprehensively addresses the topic matter.

This book deals with understanding of big data and use cases from selected business domains to understand big data, and the usage of Hadoop-related tools such as HBase, Cassandra, Pig, and Hive for big data analytics.

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This paper highlights new technologies and services that move the methodologies of old into the data-centric world of high volume and velocity that defines the modern information landscape.

In this chapter, the concepts of big data analytics along with the relevant description of different categorization, capabilities, challenges are firstly explained, and then big data Analytics techniques and methods are introduced and discussed to make the readers familiar with the way big data is applied in the enterprises.

What is big data, how it is analysed, and some case studies illustrating the potentials and pitfalls of big data analytics are given.

The purpose of this paper to design a customized Java based application which enables analysts to capture the traffic at the bottleneck under the mean field communication environment where a large number of devices are communicating with each other.

The authors discuss these trends and evaluate some current approaches to big data processing and analytics, identify the current challenges, and suggest possible research directions.

The study investigates the cognitive mechanisms through which data analysts’ commitment to BDA implementation is shaped and affected and draws on the premises of the transactional theory of stress and the literature on the commitment to organizational change.

A mobile app that automates the very labor-intensive and therefore time-heavy and expensive and expensive process of manually cataloging and cataloging individuals and storing data.

The architecture of the Ipsos Research Insight Scout (IRIS) is discussed and examples of it in action are provided to identify insights for scientific and practical discovery in public opinion and business data.

Challenges with high human costs such as the privacy of patient data and the thoughtful design of technological interventions for at-risk populations are emphasized and it is shown how a situated perspective is a necessary tool in building next generation healthcare information systems.

Diverse new approaches, methods, frameworks and systems are proposed for data collection, storage, transport, processing and analysis in the selected papers in a systematic and extensive review of journal publications about big data from 2011 to 2015.

It is discussed that big data have business value and a model for measuring its value is developed and an implementing framework for big data collection is designed as the first step forbig data analytics.

Aim of this paper is to provide a framework that can analyse the performance of the students and provide a scalable MapReduce-based analytics model using Spark framework that was experimented with a first semester course.

This chapter outlines the key principles of machine learning and predictive analytics and explains the new fundamentals of big data and the evolving technology.

This paper provides a discussion on why the traditional data mining tools cannot be used for big data mining and how the big data is different from data mining.

This research paper has addressed about Hadoop tool that how it analyses Big Data and how Big Data Security Analytics is applied to analyze the various threats and securing the business data more accurately.

The content focuses on concepts, principles and practical applications that are applicable to any industry and technology environment, and the learning is supported and explained with examples that you can replicate using open-source software.

Quality problems of disparate data is introduced and methods and technology progress regarding Big Data analytics for disparate data are presented.

This study has addressed certain aspects of such a large topic and can be improved by presenting information about the tools and techniques used in data analytics, the problems encountered in data Analytics, and the trends that can be observed in the future in future studies.

In this short visioning article, the authors are analyzing the main aspects of Big Data and Data Analytics Research and they provide their own metaphor for the next years as well as a new roadmap towards the evolution of Big data to Smart Decisions and Cognitive Computing.

The benefits of the confluences among Big Data analytics, deep learning, high performance computing (HPC), and heterogeneous computing are presented and challenges of dealing with heterogeneous data and Big data analytics are discussed.

An overview of two open source big data analytic tools is provided followed by a simple demonstration of application of these tools on transport dataset.

This chapter specifies a brief study of big data techniques to analyze these types of data, and includes a wide study of Hadoops characteristics, Hadoop architecture, advantages ofbig data and big data eco system.

This review paper is discussing about technique how Big Data is analyzed by using the technique of Hadoop and why the Big Data Security Analytics is important to mitigate the security threats to secure the enterprise data more efficiently.