A Review on Big Data Analytics in Business

2018 ◽  
pp. 210-214
Author(s):  
Manbir Sandhu ◽  
Purnima, Anuradha Saini
Keyword(s):  
Big Data ◽  
Big Data Analytics ◽  
Smart Devices ◽  
Data Streaming ◽  
Huge Amount ◽  
Business Units ◽  
Enormous Amount ◽  
Tools And Techniques ◽  
Heath Care ◽  
Data Analytic

Big data is a fast-growing technology that has the scope to mine huge amount of data to be used in various analytic applications. With large amount of data streaming in from a myriad of sources: social media, online transactions and ubiquity of smart devices, Big Data is practically garnering attention across all stakeholders from academics, banking, government, heath care, manufacturing and retail. Big Data refers to an enormous amount of data generated from disparate sources along with data analytic techniques to examine this voluminous data for predictive trends and patterns, to exploit new growth opportunities, to gain insight, to make informed decisions and optimize processes. Data-driven decision making is the essence of business establishments. The explosive growth of data is steering the business units to tap the potential of Big Data to achieve fueling growth and to achieve a cutting edge over their competitors. The overwhelming generation of data brings with it, its share of concerns. This paper discusses the concept of Big Data, its characteristics, the tools and techniques deployed by organizations to harness the power of Big Data and the daunting issues that hinder the adoption of Business Intelligence in Big Data strategies in organizations.

Integrating Big Data Analytics into Advertising Curriculum

2017 ◽  
pp. 228-250
Author(s):  
Kenneth C. C. Yang ◽  
Yowei Kang
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
The World ◽  
Challenges And Opportunities ◽  
The Status ◽  
Consumer Data ◽  
Tools And Techniques ◽  
Data Analytic ◽  
Collection Methods

With the assistance of new computing technologies and consumer data collection methods, advertising professionals are capable of generating better targeted advertising campaigns. Big Data analytics are particularly worth noticing and have presented ample opportunities for advertising researchers and practitioners around the world. Although Big Data analytic courses have been offered at major universities, existing advertising curricula have yet to address the opportunities and challenges offered by Big Data. This chapter collects curricular data from major universities around the world to examine what Big Data has posed challenges and opportunities to existing advertising curricula in an international context. Curricula of 186 universities around the world are reviewed to describe the status of integrating these developments into better preparing advertising students for these changes. Findings show that only selected advertising programs in the U.S. have begun to explore the potential of the data analytics tools and techniques. Practical and educational implications are discussed.

Integrating Big Data Analytics into Advertising Curriculum

2016 ◽  
pp. 131-152 ◽  
Author(s):  
Kenneth C. C. Yang ◽  
Yowei Kang
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
The World ◽  
Challenges And Opportunities ◽  
The Status ◽  
Consumer Data ◽  
Tools And Techniques ◽  
Data Analytic ◽  
Collection Methods

With the assistance of new computing technologies and consumer data collection methods, advertising professionals are capable of generating better targeted advertising campaigns. Big Data analytics are particularly worth noticing and have presented ample opportunities for advertising researchers and practitioners around the world. Although Big Data analytic courses have been offered at major universities, existing advertising curricula have yet to address the opportunities and challenges offered by Big Data. This chapter collects curricular data from major universities around the world to examine what Big Data has posed challenges and opportunities to existing advertising curricula in an international context. Curricula of 186 universities around the world are reviewed to describe the status of integrating these developments into better preparing advertising students for these changes. Findings show that only selected advertising programs in the U.S. have begun to explore the potential of the data analytics tools and techniques. Practical and educational implications are discussed.

scholarly journals A survey and analysis of big data management based on computational methodologies

2020 ◽  
Vol 6 (20) ◽  
pp. 48-53
Author(s):  
Shweta Kumari
Keyword(s):  
Big Data ◽  
Data Management ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Business Enterprise ◽  
Enormous Amount ◽  
Current Scenario ◽  
Data Points ◽  
The Impact ◽  
Day By Day

n a business enterprise there is an enormous amount of data generated or processed daily through different data points. It is increasing day by day. It is tough to handle it through traditional applications like excel or any other tools. So, big data analytics and environment may be helpful in the current scenario and the situation discussed above. This paper discussed the big data management ways with the impact of computational methodologies. It also covers the applicability domains and areas. It explores the computational methods applicability scenario and their conceptual design based on the previous literature. Machine learning, artificial intelligence and data mining techniques have been discussed for the same environment based on the related study.

Insight Into Big Data Analytics

2018 ◽  
pp. 67-79
Author(s):  
Mohd Vasim Ahamad ◽  
Misbahul Haque ◽  
Mohd Imran
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Healthcare Services ◽  
Huge Amount ◽  
Business Decision ◽  
Digital Era ◽  
Data Formats ◽  
Recent Trends ◽  
Analytical Approaches

In the present digital era, more data are generated and collected than ever before. But, this huge amount of data is of no use until it is converted into some useful information. This huge amount of data, coming from a number of sources in various data formats and having more complexity, is called big data. To convert the big data into meaningful information, the authors use different analytical approaches. Information extracted, after applying big data analytics methods over big data, can be used in business decision making, fraud detection, healthcare services, education sector, machine learning, extreme personalization, etc. This chapter presents the basics of big data and big data analytics. Big data analysts face many challenges in storing, managing, and analyzing big data. This chapter provides details of challenges in all mentioned dimensions. Furthermore, recent trends of big data analytics and future directions for big data researchers are also described.

Big Data and Analytics

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.

Big Data Analytics in Aquaculture Using Hive and Hadoop Platform

2018 ◽  
pp. 29-35
Author(s):  
P. Venkateswara Rao ◽  
A. Ramamohan Reddy ◽  
V. Sucharita
Keyword(s):  
Big Data ◽  
Data Management ◽  
Single Machine ◽  
Data Analytics ◽  
Historical Data ◽  
Big Data Analytics ◽  
Huge Amount ◽  
Hadoop Platform ◽  
Different Sources

In the field of Aquaculture with the help of digital advancements huge amount of data is constantly produced for which the data of the aquaculture has entered in the big data world. The requirement for data management and analytics model is increased as the development progresses. Therefore, all the data cannot be stored on single machine. There is need for solution that stores and analyzes huge amounts of data which is nothing but Big Data. In this chapter a framework is developed that provides a solution for shrimp disease by using historical data based on Hive and Hadoop. The data regarding shrimps is acquired from different sources like aquaculture websites, various reports of laboratory etc. The noise is removed after the collection of data from various sources. Data is to be uploaded on HDFS after normalization is done and is to be put in a file that supports Hive. Finally classified data will be located in particular place. Based on the features extracted from aquaculture data, HiveQL can be used to analyze shrimp diseases symptoms.

Introduction to Use and Analysis of Big Data in E-Learning

2019 ◽  
pp. 75-87
Author(s):  
Renuka Mahajan
Keyword(s):  
Big Data ◽  
Big Data Analysis ◽  
Vital Role ◽  
Interaction Data ◽  
The World ◽  
Learner Interaction ◽  
E Learning ◽  
One Year ◽  
Tools And Techniques ◽  
Data Analytic

In today's world everything is connected and is either consuming data or generating data. The world is changing so fast that even one-year-old data may not be useful, and hence, big data analysis plays a very vital role for higher management of any organizations for decision making. Data warehousing helps in gathering and storing verifiable information into a single entity. Data can be of different types like speech, text, etc. It can be structured or unstructured. Each data point is characterized in terms of volume or variety. This chapter gives an overview of how to utilize the learner interaction data from a particular website and how patterns can be captured by analyzing learner interaction data with big data analytic tools. Big data has risen in the field of education and has many challenges like storage, combining, analysis, and scalability of big data. It covers tools and techniques that can be used. The results from this study will have implications for new learners to the e-learning website, website designers, and academicians.

Big Data Applications in Healthcare

Big Data ◽  
2016 ◽  
pp. 1247-1259 ◽  
Author(s):  
Jayanthi Ranjan
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Healthcare Industry ◽  
Big Data Applications ◽  
Business Functions ◽  
Automated Algorithms ◽  
Almost All ◽  
Tools And Techniques ◽  
Research Centres

Big data is in every industry. It is being utilized in almost all business functions within these industries. Basically, it creates value by converting human decisions into transformed automated algorithms using various tools and techniques. In this chapter, the authors look towards big data analytics from the healthcare perspective. Healthcare involves the whole supply chain of industries from the pharmaceutical companies to the clinical research centres, from the hospitals to individual physicians, and anyone who is involved in the medical arena right from the supplier to the consumer (i.e. the patient). The authors explore the growth of big data analytics in the healthcare industry including its limitations and potential.

scholarly journals A Big Data Analytics Approach for the Development of Advanced Cardiology Applications

Information ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 60 ◽  
Author(s):  
Lorenzo Carnevale ◽  
Antonio Celesti ◽  
Maria Fazio ◽  
Massimo Villari
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Response Times ◽  
Distributed Processing ◽  
Big Data Analytics ◽  
Reliable System ◽  
Huge Amount ◽  
Ecg Signals ◽  
Big Data Applications ◽  
Electrocardiogram Ecg

Nowadays, we are observing a growing interest about Big Data applications in different healthcare sectors. One of this is definitely cardiology. In fact, electrocardiogram produces a huge amount of data about the heart health status that need to be stored and analysed in order to detect a possible issues. In this paper, we focus on the arrhythmia detection problem. Specifically, our objective is to address the problem of distributed processing considering big data generated by electrocardiogram (ECG) signals in order to carry out pre-processing analysis. Specifically, an algorithm for the identification of heartbeats and arrhythmias is proposed. Such an algorithm is designed in order to carry out distributed processing over the Cloud since big data could represent the bottleneck for cardiology applications. In particular, we implemented the Menard algorithm in Apache Spark in order to process big data coming form ECG signals in order to identify arrhythmias. Experiments conducted using a dataset provided by the Physionet.org European ST-T Database show an improvement in terms of response times. As highlighted by our outcomes, our solution provides a scalable and reliable system, which may address the challenges raised by big data in healthcare.

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