New Age Analytics

Transforming the Internet through Machine Learning, IoT, and Trust Modeling

Gulshan Shrivastava, Sheng-Lung Peng, Himani Bansal, Kavita Sharma, Meenakshi Sharma, Gulshan Shrivastava, Sheng-Lung Peng, Himani Bansal, Kavita Sharma, Meenakshi Sharma

• Publisher: Apple Academic Press
• Year: 2020
• ISBN:
  9781000762600
• Edition: 1
• Topic: Computer Science
• Subtopic: Computer Science General
• Index: Computer Science

This comprehensive and timely book, New Age Analytics: Transforming the Internet through Machine Learning, IoT, and Trust Modeling, explores the importance of tools and techniques used in machine learning, big data mining, and more. The book explains how advancements in the world of the web have been achieved and how the experiences of users can be analyzed. It looks at data gathering by the various electronic means and explores techniques for analysis and management, how to manage voluminous data, user responses, and more.

This volume provides an abundance of valuable information for professionals and researchers working in the field of business analytics, big data, social network data, computer science, analytical engineering, and forensic analysis. Moreover, the book provides insights and support from both practitioners and academia in order to highlight the most debated aspects in the field.

CHAPTER 1
Digital Marketing and Analysis Techniques: Transforming Internet Usage

MEENAKSHI SHARMA,¹ NIDHIKA CHAUHAN,² HIMANI BANSAL,³ and LOREDANA STANCIU⁴

¹Galgotias University, India, E-mail: [email protected] (M. Sharma)

²Chandigarh University, Chandigarh, India

³Jaypee Institute of Information Technology, Noida, India

⁴Politehnica University, Timisoara, Romania

ABSTRACT

In the era of immense digitalization, changes have occurred in the field of analysis. Data analysis has boost up opportunities not only in business but in another field as well. With the introduction of the internet, the world has undergone various transformations. This chapter mainly focuses on how new-age transformation is changing the Internet. It also reflects the opportunities for analysis and showcases the changes brought to our everyday life by the internet. As it is known the internet is a massive source of data gathering, this gathered data is given some form by analysis. Data analysis has become a popular term these days and has opened new career opportunities. Various tools and techniques of data gathering and analysis will be discussed in this chapter.

1.1 INTRODUCTION

Internet and data analysis are two interlinked terms but this does not mean that data analysis is only restricted to data gathering and works only on existing data, but it is also about prediction and forecasting. The best example is the stock exchange forecast or weather forecast. In order to make the prediction both fields use analysis. The analysis is not only restricted to these fields, but there are many more areas where analysis is used, like in business it plays a considerable role and helps in boosting the business.

Now the word analysis may sound very easy. People might think that all required to do is pick the data from the internet, feed it to the system and them glance at the results and make the prediction. Though the steps are the same, there are many steps in between which involve a collection of data, cleaning of the data, slicing, and dicing of data and much more which makes this process tedious job but thanks to new-age tools and software that have given some relaxation. A details description will be given later in the chapter that will be discussing the challenges with data collected over the internet. Enormous challenges were faced in the case of big data (BD), which will be explained in detail later.

In order to understand the concept, there needs to be clarity of basic terms like analysis, how the internet is linked to an analysis, BD, structured, and unstructured data, various analysis tools, etc. With the exploration of Hadoop, the information system has taken a different turn and information architecture has undergone a considerable transformation (Robert, 2015). For ages, business analysis and intelligence were dependent on the organization’s data mart and data warehouse. Some best data analysis was also defined for them; however, there has been a noticeable change in this system. Now the area of analysis and data storing is transforming. With the immense growth of data, the techniques to handle the data and to analysis are also transforming. The internet has been an enormous source of data for us, but the problem lies in structuring this data as it is not homogeneous in nature. Now, the question is why to require the homogeneous data. So in order to perform the analysis, the data is to be clustered in groups which means the data has to be arranged in homogeneous sets. Similar kinds of data when putting together results in easy analysis.

The semantic web is an environment which helps human and machine to communicate semantically. It mainly focuses on making the content suitable for a machine to understand it, so as to extract query (Sharma et al., 2012)

Although there are numerous advantages to this digital transformation, there are many obstacles to this transformation that cannot be ignored. To understand these obstacles there needs to understand the fundamental transformation, how the system was evolved and now where have humans reached and what are the other challenges faced today. A diagrammatic representation of the initiating digital transformation journey gives a clear picture of how an organization responds to such changes (Figure 1.1).

FIGURE 1.1 Initiating digital transformation: The journal.

The digital transformation was not an overnight process; a lot of planning and structuring was done to move from manual phase to the digital transformation phase. As Figure 1.1 represents transformation involved active participation from the beginning till the end. In the beginning stage, some parameters of digital innovation and changes were set (Figure 1.2).

FIGURE 1.2 Digital transformation.

As it is evident from Figure 1.2 that over the past years the organizations have undergone a considerable transformation at different levels. Apart from traditional methods, organizations have moved to the Internet, CRM, and ERP system which has further led to three different types of experiences. These experiences are basically how the members linked to the organization respond to the transformation (Hinchcliffe, 2018). Internet is a wide source of information that has increased the use of web usage mining techniques in academics and commercial areas. Studies show that intrigued web users give profitable inputs to web designers for efficient organizing of website (Sharma et al., 2011) (Figure 1.3).

FIGURE 1.3 An adaptable digital transformation framework.

Now moving on to the next phase of transformation framework. Figure 1.3 clearly shows the generative factors which means the source that provides the input. Now having such a system is itself a critical task and getting people to function across this framework is another tedious job. The most challenging task is to get organization collaboration across various parameters and without this collaboration, digital transformation is difficult to achieve. It is believed that people will and cannot adapt to digital transformation overnight, so in order to change the mindset of these people, specific activities are proposed like MOOCs, reverse mentoring, certification, etc. thus leading to the building of required digital skills. Once the organization agrees to adopt the digital system, a framework is designed to identify the starting point and then the ongoing steps. Now the question is how the internet is playing a role in this? So in that case, the internet has been a considerable source for data collection and data gathering. Cloud, Hadoop, etc. are used to handle this immense data. The digital transformation model discussed above in Figure 1.3 mainly focuses on day to day functionality and timely alterations are also made to it so as to stay updated and get proper outcomes (Hinchcliffe, 2018).

1.2 BUSINESS APPLICATIONS IN SOCIAL MEDIA AND ITS ANALYSIS

Social media is the way forward for Businesses. One of the most prominent uses of social media in businesses is Digital Marketing.

Digital Marketing is a new way for corporations to connect with their audience. It is the sphere that provides equal footing to each business, regardless of its size or resources.

When digital marketing is discussed, then there are many factors that influence it on the ground level to top-level working. They can include factors such as brand communication which can further be used to indicate the traces of digital marketing. However, for the complete evaluation of digital marketing, the explanation should revolve around the working of digital marketing and also its scope in various industries (Fan et al., 2014).

1.2.1 CURRENT DIGITAL MARKETING LANDSCAPE

As the era is emerging, so is the scenario of digital marketing getting its priority. Organizations must observe that the most obvious way to deal with marketing, i.e., via advertising is still ruling over the market with TV and Ads. The available online platforms still need more importance to be given as the industries have realized that online sources have been commonly utilized by the consumers and can be easily targeted for marketing. This automatically will generate more revenue.

There have been five forms of digital marketing.

• Static images take less time to create and are easy to share. The most common content of static images is in the form of infographics or memes.
• Videos or animations have a greater tendency to attract more consumers as people are more attracted to motion. Even after its high efficiency, brands avoid using video content due to its high cost of production.
• The consumers want an easy text to read which ...

Table of contents

• Cover
• Half Title
• Title Page
• Copyright Page
• About the Editors
• Contents
• Contributors
• Abbreviations
• Preface
• 1. Digital Marketing and Analysis Techniques: Transforming Internet Usage
• 2. Machine Learning Techniques and Tools: Merits and Demerits
• 3. Machine Learning and Its Applicability in Networking
• 4. Machine Learning to Gain Novel Insight on Web Network Analysis
• 5. Online Trust Evaluation
• 6. Trust-Based Sentimental Analysis and Online Trust Evaluation
• 7. Denaturing the Internet Through a Trust Appraisement Paradigm by QoS Accretion
• 8. Collaborative Filtering in Recommender Systems: Technicalities, Challenges, Applications, and Research Trends
• 9. Business Application Analytics Through the Internet of Things
• 10. Business Application Analytics and the Internet of Things: The Connecting Link
• 11. An Enablement Platform for an Internet of Things Application: A Business Model
• 12. Social Media Analytics Using R Programming
• Index