Recent Development in Big Data Analytics

This chapter describes how big data consist of an extreme volume of data, velocity, and more complex variable data that demands current technology changes in capturing, storage, distribution, management, analysis data. Business facing more struggles in identifying the pragmatic approach in capturing the data about customer, products, and services. Usage of big data mainly with the analytical method, but it specifically compares with features of an analytical method based on unstructured data contributed around 95% of big data. The analytical approach depends on heterogeneous data and unstructured data's like text, audio, video format. It demands new effective tool for predictive analysis for big data with the unstructured format. This chapter describes explanation of big data and characteristics of big data compress of Volume, Velocity, Variety, Variability, and Value. Recent trends in the development of big data that applies in real time application perspectives like health care agriculture, education etc.

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IOT And Big Data Analytics In E-Learning: A Technological Perspective and Review

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i1.8.11540 ◽

2018 ◽

Vol 7 (1.8) ◽

pp. 164 ◽

Cited By ~ 3

Author(s):

S Kusuma ◽

D Kasi Viswanath

Keyword(s):

Big Data ◽

Data Analytics ◽

Big Data Analytics ◽

Digital Culture ◽

Educational Institutions ◽

Learning Approaches ◽

E Learning ◽

Recent Trends ◽

Conducting Research ◽

The Internet Of Things

The internet of things & Big data analytics in eLearning brings tremendous challenges & opportunities to educational institutions & students. In recent trends, the growth of Pervasive computing, Social media, evolving IoT capabilities, technologies such as cloud computing, and big data and analytics are improving the core values of teaching and conducting research but also instilling a new digital culture and developing an IoT-centric society. The primary purpose of this paper is to provide an impact of IoT & Big data analytics in the area of E-learning and study on different E-learning approaches.

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Big Data Predictive and Prescriptive Analytics

Advances in Data Mining and Database Management - Handbook of Research on Cloud Infrastructures for Big Data Analytics ◽

10.4018/978-1-4666-5864-6.ch015 ◽

2014 ◽

pp. 370-391 ◽

Cited By ~ 3

Author(s):

Ganesh Chandra Deka

Keyword(s):

Big Data ◽

Business Intelligence ◽

Data Analytics ◽

Action Plan ◽

Big Data Analytics ◽

Clear Understanding ◽

Future Planning ◽

Prescriptive Analytics ◽

Hybrid Data ◽

Recent Trends

The Analytics tools are capable of suggesting the most favourable future planning by analyzing “Why” and “How” blended with What, Who, Where, and When. Descriptive, Predictive, and Prescriptive analytics are the analytics currently in use. Clear understanding of these three analytics will enable an organization to chalk out the most suitable action plan taking various probable outcomes into account. Currently, corporate are flooded with structured, semi-structured, unstructured, and hybrid data. Hence, the existing Business Intelligence (BI) practices are not sufficient to harness potentials of this sea of data. This change in requirements has made the cloud-based “Analytics as a Service (AaaS)” the ultimate choice. In this chapter, the recent trends in Predictive, Prescriptive, Big Data analytics, and some AaaS solutions are discussed.

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Insight Into Big Data Analytics

Advances in Systems Analysis, Software Engineering, and High Performance Computing - Handbook of Research on Pattern Engineering System Development for Big Data Analytics ◽

10.4018/978-1-5225-3870-7.ch005 ◽

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.

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Big Data Predictive and Prescriptive Analytics

Big Data ◽

10.4018/978-1-4666-9840-6.ch002 ◽

2016 ◽

pp. 30-55 ◽

Cited By ~ 2

Author(s):

Ganesh Chandra Deka

Keyword(s):

Big Data ◽

Business Intelligence ◽

Data Analytics ◽

Action Plan ◽

Big Data Analytics ◽

Clear Understanding ◽

Future Planning ◽

Prescriptive Analytics ◽

Hybrid Data ◽

Recent Trends

The Analytics tools are capable of suggesting the most favourable future planning by analyzing “Why” and “How” blended with What, Who, Where, and When. Descriptive, Predictive, and Prescriptive analytics are the analytics currently in use. Clear understanding of these three analytics will enable an organization to chalk out the most suitable action plan taking various probable outcomes into account. Currently, corporate are flooded with structured, semi-structured, unstructured, and hybrid data. Hence, the existing Business Intelligence (BI) practices are not sufficient to harness potentials of this sea of data. This change in requirements has made the cloud-based “Analytics as a Service (AaaS)” the ultimate choice. In this chapter, the recent trends in Predictive, Prescriptive, Big Data analytics, and some AaaS solutions are discussed.

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A High-Order CFS Algorithm for Clustering Big Data

Mobile Information Systems ◽

10.1155/2016/4356127 ◽

2016 ◽

Vol 2016 ◽

pp. 1-8 ◽

Cited By ~ 4

Author(s):

Fanyu Bu ◽

Zhikui Chen ◽

Peng Li ◽

Tong Tang ◽

Ying Zhang

Keyword(s):

Big Data ◽

Deep Learning ◽

Clustering Algorithm ◽

Big Data Analytics ◽

Learning Model ◽

Heterogeneous Data ◽

High Order ◽

Tensor Model ◽

Industrial Internet ◽

Deep Learning Model

With the development of Internet of Everything such as Internet of Things, Internet of People, and Industrial Internet, big data is being generated. Clustering is a widely used technique for big data analytics and mining. However, most of current algorithms are not effective to cluster heterogeneous data which is prevalent in big data. In this paper, we propose a high-order CFS algorithm (HOCFS) to cluster heterogeneous data by combining the CFS clustering algorithm and the dropout deep learning model, whose functionality rests on three pillars: (i) an adaptive dropout deep learning model to learn features from each type of data, (ii) a feature tensor model to capture the correlations of heterogeneous data, and (iii) a tensor distance-based high-order CFS algorithm to cluster heterogeneous data. Furthermore, we verify our proposed algorithm on different datasets, by comparison with other two clustering schemes, that is, HOPCM and CFS. Results confirm the effectiveness of the proposed algorithm in clustering heterogeneous data.

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Big Data Analytics in Online Structural Health Monitoring

International Journal of Prognostics and Health Management ◽

10.36001/ijphm.2016.v7i4.2462 ◽

2020 ◽

Vol 7 (4) ◽

Author(s):

Guowei Cai ◽

Sankaran Mahadevan

Keyword(s):

Big Data ◽

Structural Health Monitoring ◽

Health Monitoring ◽

Data Analytics ◽

Structural Damage ◽

Big Data Analytics ◽

High Volume ◽

Heterogeneous Data ◽

Sensor Technology ◽

Structural Health

This manuscript explores the application of big data analytics in online structural health monitoring. As smart sensor technology is making progress and low cost online monitoring is increasingly possible, large quantities of highly heterogeneous data can be acquired during the monitoring, thus exceeding the capacity of traditional data analytics techniques. This paper investigates big data techniques to handle the highvolume data obtained in structural health monitoring. In particular, we investigate the analysis of infrared thermal images for structural damage diagnosis. We explore the MapReduce technique to parallelize the data analytics and efficiently handle the high volume, high velocity and high variety of information. In our study, MapReduce is implemented with the Spark platform, and image processing functions such as uniform filter and Sobel filter are wrapped in the mappers. The methodology is illustrated with concrete slabs, using actual experimental data with induced damage

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A Long Short Term Memory with Peephole Connections and Generative Adversarial Network Based Collaborative Methodology to Identify Outliers in ECG Dataset

Journal of Computational and Theoretical Nanoscience ◽

10.1166/jctn.2020.9273 ◽

2020 ◽

Vol 17 (8) ◽

pp. 3798-3803

Author(s):

M. D. Anto Praveena ◽

B. Bharathi

Keyword(s):

Time Series ◽

Big Data ◽

Data Analytics ◽

Time Series Data ◽

Short Term Memory ◽

Big Data Analytics ◽

Data Preprocessing ◽

Heterogeneous Data ◽

Series Data ◽

Outlier Identification

Big Data analytics has become an upward field, and it plays a pivotal role in Healthcare and research practices. Big data analytics in healthcare cover vast numbers of dynamic heterogeneous data integration and analysis. Medical records of patients include several data including medical conditions, medications and test findings. One of the major challenges of analytics and prediction in healthcare is data preprocessing. In data preprocessing the outlier identification and correction is the important challenge. Outliers are exciting values that deviates from other values of the attribute; they may simply experimental errors or novelty. Outlier identification is the method of identifying data objects with somewhat different behaviors than expectations. Detecting outliers in time series data is different from normal data. Time series data are the data that are in a series of certain time periods. This kind of data are identified and cleared to bring the quality dataset. In this proposed work a hybrid outlier detection algorithm extended LSTM-GAN is helped to recognize the outliers in time series data. The outcome of the proposed extended algorithm attained better enactment in the time series analysis on ECG dataset processing compared with traditional methodologies.

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Big Data Analytics in Healthcare

International Journal of Big Data and Analytics in Healthcare ◽

10.4018/ijbdah.2020010102 ◽

2020 ◽

Vol 5 (1) ◽

pp. 19-27

Author(s):

Jaimin Navinchandra Undavia ◽

Atul Manubhai Patel

Keyword(s):

Big Data ◽

Data Analytics ◽

Big Data Analytics ◽

Heterogeneous Data ◽

Simple Type ◽

Healthcare Industry ◽

Technological Advancement ◽

Huge Amount ◽

High Level ◽

Almost All

The technological advancement has also opened up various ways to collect data through automatic mechanisms. One such mechanism collects a huge amount of data without any further maintenance or human interventions. The health industry sector has been confronted by the need to manage the big data being produced by various sources, which are well known for producing high volumes of heterogeneous data. High level of sophistication has been incorporated in almost all the industry, and healthcare is one of them. The article shows that the existence of huge amount of data in healthcare industry and the data generated in healthcare industry is neither homogeneous nor a simple type of data. Then the various sources and objectives of data are also highlighted and discussed. As data come from various sources, they must be versatile in nature in all aspects. So, rightly and meaningfully, big data analytics has penetrated the healthcare industry and its impact is also highlighted.

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Analyzing Indian healthcare data with big data

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i3.29.18467 ◽

2018 ◽

Vol 7 (3.29) ◽

pp. 88

Author(s):

R Venkateswara Reddy ◽

Dr D. Murali

Keyword(s):

Big Data ◽

Data Analysis ◽

Big Data Analytics ◽

Active Role ◽

Heterogeneous Data ◽

Retail Banking ◽

Healthcare Data ◽

Emergency Situations ◽

Competitive Edge ◽

The Right

Big Data is the enormous amounts of data, being generated at present times. Organizations are using this Big Data to analyze and predict the future to make profits and gain competitive edge in the market. Big Data analytics has been adopted into almost every field, retail, banking, governance and healthcare. Big Data can be used for analyzing healthcare data for better planning and better decision making which lead to improved healthcare standards. In this paper, Indian health data from 1950 to 2015 are analyzed using various queries. This healthcare generates the considerable amount of heterogeneous data. But without the right methods for data analysis, these data have become useless. The Big Data analysis with Hadoop plays an active role in performing significant real-time analyzes of the enormous amount of data and able to predict emergency situations before this happens.

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