A Survey on Big Data Analytics Using HADOOP

In this digital world, as organizations are evolving rapidly with data centric asset the explosion of data and size of the databases have been growing exponentially. Data is generated from different sources like business processes, transactions, social networking sites, web servers, etc. and remains in structured as well as unstructured form. The term ― Big data is used for large data sets whose size is beyond the ability of commonly used software tools to capture, manage, and process the data within a tolerable elapsed time. Big data varies in size ranging from a few dozen terabytes to many petabytes of data in a single data set. Difficulties include capture, storage, search, sharing, analytics and visualizing. Big data is available in structured, unstructured and semi-structured data format. Relational database fails to store this multi-structured data. Apache Hadoop is efficient, robust, reliable and scalable framework to store, process, transforms and extracts big data. Hadoop framework is open source and fee software which is available at Apache Software Foundation. In this paper we will present Hadoop, HDFS, Map Reduce and c-means big data algorithm to minimize efforts of big data analysis using Map Reduce code. The objective of this paper is to summarize the state-of-the-art efforts in clinical big data analytics and highlight what might be needed to enhance the outcomes of clinical big data analytics tools and related fields.

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SQL Versus NoSQL Databases to assess their appropriateness for Big Data Application.

Recent Patents on Computer Science ◽

10.2174/2213275912666191028111632 ◽

2019 ◽

Vol 12 ◽

Author(s):

Mohammad Abu Kausar ◽

Mohammad Nasar

Keyword(s):

Big Data ◽

Data Analytics ◽

High Performance ◽

Big Data Analytics ◽

Large Data ◽

Data Sets ◽

Nosql Databases ◽

Digital World ◽

Nosql Database ◽

Complete Study

Background: Nowadays, the digital world is rising rapidly and becoming very difficult in nature's quantity, diversity, and speed. Recently, there have been two major changes in data management, which are NoSQL databases and Big Data Analytics. While evolving with the diverse reasons, their independent growths balance each other and their convergence would greatly benefit organization to make decisions on-time with the amount of multifaceted data sets that might be semi structured, structured, and unstructured. Though several software solutions have come out to support Big Data analytics on the one hand, on the other hand, there have been several packages of NoSQL database available in the market. Methods: The main goal of this article is to give comprehension of their perspective and a complete study to associate the future of the emerging several important NoSQL data models. Results: Evaluating NoSQL databases for Big Data analytics with traditional SQL performance shows that NoSQL database is a superior alternative for industry condition need high-performance analytics, adaptability, simplicity, and distributed large data scalability. Conclusion: This paper conclude with industry's current adoption status of NoSQL databases.

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A Survey on Duplicate Data Filtering Methods in Big Data

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i3.1.16805 ◽

2018 ◽

Vol 7 (3.1) ◽

pp. 90 ◽

Cited By ~ 1

Author(s):

S P Godlin Jasil ◽

V Ulagamuthalvi

Keyword(s):

Big Data ◽

Data Analytics ◽

Network Performance ◽

False Negative ◽

Big Data Analytics ◽

Large Data ◽

Bloom Filter ◽

Data Set ◽

Redundant Data ◽

Different Sources

Big Data analytics is the process of collecting heterogeneous huge sets of data for analyzing .The data are fetched from different sources and can be in heterogeneous form. Data arriving in the big data system will be in giga-bytes for every second. Since, the data are in huge volume, there is a possibility of redundant data that affect the network performance. This article presents the review of different filtering methods and algorithms that are used for duplicate elimination such as Bloom filter, Stable Bloom Filter, multi-layer bloom filter, Counting Bloom Filter with some disadvantages such as false positive and false negative. The aim of this paper is to propose an algorithm for eliminating the duplicate Data in a large data set by using big data analytics.

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An Effective Model for Consumer Need Prediction Using Big Data Analytics

Journal of Interconnection Networks ◽

10.1142/s0219265921430088 ◽

2021 ◽

Author(s):

Yihao Tian

Keyword(s):

Big Data ◽

Consumer Behavior ◽

Data Analytics ◽

Big Data Analytics ◽

Customer Behavior ◽

Data Set ◽

Customer Data ◽

Demand Prediction ◽

Management Ratio ◽

The Veil

Big data is an unstructured data set with a considerable volume, coming from various sources such as the internet, business organizations, etc., in various formats. Predicting consumer behavior is a core responsibility for most dealers. Market research can show consumer intentions; it can be a big order for a best-designed research project to penetrate the veil, protecting real customer motivations from closer scrutiny. Customer behavior usually focuses on customer data mining, and each model is structured at one stage to answer one query. Customer behavior prediction is a complex and unpredictable challenge. In this paper, advanced mathematical and big data analytical (BDA) methods to predict customer behavior. Predictive behavior analytics can provide modern marketers with multiple insights to optimize efforts in their strategies. This model goes beyond analyzing historical evidence and making the most knowledgeable assumptions about what will happen in the future using mathematical. Because the method is complex, it is quite straightforward for most customers. As a result, most consumer behavior models, so many variables that produce predictions that are usually quite accurate using big data. This paper attempts to develop a model of association rule mining to predict customers’ behavior, improve accuracy, and derive major consumer data patterns. The finding recommended BDA method improves Big data analytics usability in the organization (98.2%), risk management ratio (96.2%), operational cost (97.1%), customer feedback ratio (98.5%), and demand prediction ratio (95.2%).

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Architecture of modern platforms for big data analytics

Advanced Information Technology ◽

10.17721/ait.2021.1.09 ◽

2021 ◽

pp. 67-74

Author(s):

Liudmyla Zubyk ◽

Yaroslav Zubyk

Keyword(s):

Big Data ◽

Data Analytics ◽

Business Processes ◽

Service Providers ◽

Big Data Analytics ◽

Business Value ◽

Third Party ◽

Unstructured Data ◽

Business Decision ◽

World Industry

Big data is one of modern tools that have impacted the world industry a lot of. It also plays an important role in determining the ways in which businesses and organizations formulate their strategies and policies. However, very limited academic researches has been conducted into forecasting based on big data due to the difficulties in capturing, collecting, handling, and modeling of unstructured data, which is normally characterized by it’s confidential. We define big data in the context of ecosystem for future forecasting in business decision-making. It can be difficult for a single organization to possess all of the necessary capabilities to derive strategic business value from their findings. That’s why different organizations will build, and operate their own analytics ecosystems or tap into existing ones. An analytics ecosystem comprising a symbiosis of data, applications, platforms, talent, partnerships, and third-party service providers lets organizations be more agile and adapt to changing demands. Organizations participating in analytics ecosystems can examine, learn from, and influence not only their own business processes, but those of their partners. Architectures of popular platforms for forecasting based on big data are presented in this issue.

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Analytics-as-a-Service (AaaS)

Web Services ◽

10.4018/978-1-5225-7501-6.ch065 ◽

2019 ◽

pp. 1262-1281

Author(s):

Chitresh Verma ◽

Rajiv Pandey

Keyword(s):

Big Data ◽

Case Studies ◽

Data Analytics ◽

Data Science ◽

Business Processes ◽

Service Oriented Architecture ◽

Big Data Analytics ◽

Major Branch ◽

Implementation Level ◽

Industrial Level

Big Data Analytics is a major branch of data science where the huge amount raw data is processed to get insight for relevant business processes. Integration of big data, its analytics along with Service Oriented Architecture (SOA) is need of the hour, such integration shall render reusability and scalability to various business processes. This chapter explains the concept of Big Data and Big Data Analytics at its implementation level. The Chapter further describes Hadoop and its technologies which are one of the popular frameworks for Big Data Analytics and envisage integrating SOA with relevant case studies. The chapter demonstrates the SOA integration with Big Data through, two case studies of two different scenarios are incorporated that integrates real world implementation with theory and enables better understanding of the industrial level processes and practices.

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Big Data and Analytics

Big Data Analytics for Sustainable Computing - Advances in Data Mining and Database Management ◽

10.4018/978-1-5225-9750-6.ch003 ◽

2020 ◽

pp. 47-65

Author(s):

Sheik Abdullah A. ◽

Priyadharshini P.

Keyword(s):

Social Media ◽

Big Data ◽

Predictive Analytics ◽

Data Warehousing ◽

Big Data Analytics ◽

Structured Data ◽

Social Media Analytics ◽

Data Set ◽

Data Volume ◽

Tools And Techniques

The term Big Data corresponds to a large dataset which is available in different forms of occurrence. In recent years, most of the organizations generate vast amounts of data in different forms which makes the context of volume, variety, velocity, and veracity. Big Data on the volume aspect is based on data set maintenance. The data volume goes to processing usual a database but cannot be handled by a traditional database. Big Data is stored among structured, unstructured, and semi-structured data. Big Data is used for programming, data warehousing, computational frameworks, quantitative aptitude and statistics, and business knowledge. 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. This chapter mainly deals with the tools and techniques that corresponds to big data analytics of various applications.

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Analytics-as-a-Service (AaaS)

Advances in Business Information Systems and Analytics - Exploring Enterprise Service Bus in the Service-Oriented Architecture Paradigm ◽

10.4018/978-1-5225-2157-0.ch003 ◽

2017 ◽

pp. 26-45

Author(s):

Chitresh Verma ◽

Rajiv Pandey

Keyword(s):

Big Data ◽

Case Studies ◽

Data Analytics ◽

Data Science ◽

Business Processes ◽

Service Oriented Architecture ◽

Big Data Analytics ◽

Major Branch ◽

Implementation Level ◽

Industrial Level

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Business Analytics and Big Data

Advances in Business Information Systems and Analytics - Handbook of Research on Organizational Transformations through Big Data Analytics ◽

10.4018/978-1-4666-7272-7.ch001 ◽

2015 ◽

pp. 1-11 ◽

Cited By ~ 1

Author(s):

Dennis T. Kennedy ◽

Dennis M. Crossen ◽

Kathryn A. Szabat

Keyword(s):

Big Data ◽

Data Analytics ◽

Quantitative Methods ◽

Business Processes ◽

Predictive Analytics ◽

Big Data Analytics ◽

Database Systems ◽

Three Dimensions ◽

Business Analytics ◽

The People

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.

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Research Challenges in Big Data Analytics

Decision Management ◽

10.4018/978-1-5225-1837-2.ch006 ◽

2017 ◽

pp. 83-99

Author(s):

Sivamathi Chokkalingam ◽

Vijayarani S.

Keyword(s):

Big Data ◽

Data Analytics ◽

Large Scale ◽

New Technologies ◽

Big Data Analytics ◽

Large Data ◽

Data Sets ◽

Data Types ◽

Customer Preferences ◽

Research Challenges

The term Big Data refers to large-scale information management and analysis technologies that exceed the capability of traditional data processing technologies. Big Data is differentiated from traditional technologies in three ways: volume, velocity and variety of data. Big data analytics is the process of analyzing large data sets which contains a variety of data types to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful business information. Since Big Data is new emerging field, there is a need for development of new technologies and algorithms for handling big data. The main objective of this paper is to provide knowledge about various research challenges of Big Data analytics. A brief overview of various types of Big Data analytics is discussed in this paper. For each analytics, the paper describes process steps and tools. A banking application is given for each analytics. Some of research challenges and possible solutions for those challenges of big data analytics are also discussed.

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