Big Data Analytics and Visualization for Food Health Status Determination Using Bigmart Data

Being premature, the traditional data visualization techniques suffer from several challenges and lack the ability to handle a huge amount of data, particularly in gigabytes and terabytes. In this research, we propose an R-tool and data analytics framework for handling a huge amount of commercial market stored data and discover knowledge patterns from the dataset for conveying the derived conclusion. In this chapter, we elaborate on pre-processing a commercial market dataset using the R tool and its packages for information and visual analytics. We suggest a recommendation system based on the data which identifies if the food entry inserted into the database is hygienic or non-hygienic based on the quality preserved attributes. For a precise recommendation system with strong predictive accuracy, we will put emphasis on Algorithms such as J48 or Naive Bayes and utilize the one who outclasses the comparison based on accuracy. Such a system, when combined with R language, can be potentially used for enhanced decision making.

Big Data Analytics in the Healthcare Industry

Big Data has changed the way we manage, analyze and impact the data information in any industry. A champion among the most promising zones where it will, in general, be associated with takeoff progress is therapeutic medicinal administrations. Administration examinations can diminish costs of treatment, foresee flare-ups of pestilences, keep up a key separation from preventable diseases and improve individual fulfillment overall. The chapter depicts the beginning field of a huge information investigation in human services, talks about the advantages, diagrams a design structure and approach, portrays models revealed in the writing, quickly examines the difficulties, and offers ends. A continuous examination which targets the utilization of tremendous volumes of remedial data information while combining multimodal data information from various sources is discussed. Potential locales of research inside this field which can give noteworthy impact on medicinal administrations movement are in like manner dissected.

Big Data-Based Spectrum Sensing for Cognitive Radio Networks Using Artificial Intelligence

Cognitive radio has emerged as a promising candidate solution to improve spectrum utilization in next generation wireless networks. Spectrum sensing is one of the main challenges encountered by cognitive radio and the application of big data is a powerful way to solve various problems. However, for the increasingly tense spectrum resources, the prediction of cognitive radio based on big data is an inevitable trend. The signal data from various sources is analyzed using the big data cognitive radio framework and efficient data analytics can be performed using different types of machine learning techniques. This chapter analyses the process of spectrum sensing in cognitive radio, the challenges to process spectrum data and need for dynamic machine learning algorithms in decision making process.

Social Network Analysis

Each day, 2.5 quintillion bytes of data are generated due to our daily activity. It is due to the vast amount of use of the smart mobiles, Cloud data storage, and the Internet of Things. In earlier days, these technologies were utilized by large IT companies and the private sector, but now each person has a high-end smartphone along with the cloud and IoT for the easy storage of data and backup. The analysis of the data generated by social media is a tedious process and involves a lot of techniques. Some tools for social network analysis are: Gephi, Networkx, IGraph, Pajek, Node XL, and cytoscope. Apart from these tools there are various efficient social data analysis algorithms that are far more helpful in doing analytics. The need for and use of social network analysis is very helpful in our current problem of huge data generation. In this chapter, the need for the analysis of social data along with the tools that are needed for the analysis and the techniques that are to be implemented in the field of social data analysis are covered.

Big Data and Analytics

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.

A Detailed Study on Classification Algorithms in Big Data

After an era of managing data collection difficulties, these days the issue has turned into the problem of how to process these vast amounts of information. Scientists, as well as researchers, think that today, probably the most essential topic in computing science is Big Data. Big Data is used to clarify the huge volume of data that could exist in any structure. This makes it difficult for standard controlling approaches for mining the best possible data through such large data sets. Classification in Big Data is a procedure of summing up data sets dependent on various examples. There are distinctive classification frameworks which help us to classify data collections. A few methods that discussed in the chapter are Multi-Layer Perception Linear Regression, C4.5, CART, J48, SVM, ID3, Random Forest, and KNN. The target of this chapter is to provide a comprehensive evaluation of classification methods that are in effect commonly utilized.

“Saksham Model” Performance Improvisation Using Node Capability Evaluation in Apache Hadoop

Big Data processing and analysis requires tremendous processing capability. Distributed computing brings many commodity systems under the common platform to answer the need for Big Data processing and analysis. Apache Hadoop is the most suitable set of tools for Big Data storage, processing, and analysis. But Hadoop found to be inefficient when it comes to heterogeneous set computers which have different processing capabilities. In this research, we propose the Saksham model which optimizes the processing time by efficient use of node processing capability and file management. The proposed model shows the performance improvement for Big Data processing. To achieve better performance, Saksham model uses two vital aspects of heterogeneous distributed computing: Effective block rearrangement policy and use of node processing capability. The results demonstrate that the proposed model successfully achieves better job execution time and improves data locality.

Decoding Big Data Analytics for Emerging Business Through Data-Intensive Applications and Business Intelligence

In recent years, big data have gained massive popularity among researchers, decision analysts, and data architects in any enterprise. Big data had been just another way of saying analytics. In today's world, the company's capital lies with big data. Think of worlds huge companies. The value they offer comes from their data, which they analyze for their proactive benefits. This chapter showcases the insight of big data and its tools and techniques the companies have adopted to deal with data problems. The authors also focus on framework and methodologies to handle the massive data in order to make more accurate and precise decisions. The chapter begins with the current organizational scenario and what is meant by big data. Next, it draws out various challenges faced by organizations. The authors also observe big data business models and different frameworks available and how it has been categorized and finally the conclusion discusses the challenges and what is the future perspective of this research area.

Understanding Big Data

In today's world, it is necessary to use data or information available in a wise manner to make effective business decisions and define better objectives. If the information available is not utilized to its full extent, organizations might lose their reputation and position in this competitive world. However, data needs to be processed appropriately to gain constructive insights from it, and the heterogeneous nature of this data makes this increasingly more complex and time-consuming. The ever-increasing growth of data generated is far more than human processing capabilities and thus computing methods need to be automated to scale effectively. This chapter defines Big Data basically and provides an overview of Big Data in terms of current status, organizational effects (technology, health care, education, etc.), implementation challenges and Big Data projects. This research adopted literature review as methodology and refined valuable information through current journals, books, magazines and blogs.

Feature Selection Algorithm Using Relative Odds for Data Mining Classification

A filter feature selection algorithm is developed and its performance tested. In the initial step, the algorithm dichotomizes the dataset then separately computes the association between each predictor and the class variable using relative odds (odds ratios). The value of the odds ratios becomes the importance ranking of the corresponding explanatory variable in determining the output. Logistic regression classification is deployed to test the performance of the new algorithm in comparison with three existing feature selection algorithms: the Fisher index, Pearson's correlation, and the varImp function. A number of experimental datasets are employed, and in most cases, the subsets selected by the new algorithm produced models with higher classification accuracy than the subsets suggested by the existing feature selection algorithms. Therefore, the proposed algorithm is a reliable alternative in filter feature selection for binary classification problems.