Big Data Analytics

Decision-making is a cognitive process stemming from the analysis of available courses of action related to a given problem. Rationally, potential courses of action are derived from deliberate analysis of available data related to a problem and its potential outcomes. Decisions are generally characterized as normative, descriptive or naturalistic. Normative decisions are based on evidence, logic and analysis; descriptive decisions are based on intuition and experience. Naturalistic decisions are formed as a hybrid of normative and descriptive processes.

For decision-makers the revolution in information technology and the amount and scope of the data they provide has become overwhelming, leading to cognitive overload, confusion and an inability to function effectively. Decision Support Systems (DSS) facilitate data analysis and selection of a viable course of action rapidly and effectively. However, like the human decision- makers they serve, DSS capabilities may also suffer from cognitive overload, inaccuracy, and complexity, especially in time-sensitive environments.

This study explores DSS employment for time-sensitive decision processes using Digitally Aided Close Air Support (DACAS) as a model. The study explores decision theory, models, DSS development and time-sensitive environments. Using DACAS as a model the paper reveals the obstacles and opportunities associated with the employment of DSS for time- sensitive processes. Finally, the study makes recommendations for improved employment of DSS in time-sensitive environments.

Breast cancer is one of the largest causes of women's death in the world today. Advance engineering of natural image classification techniques and Artificial Intelligence methods has largely been used for the breast-image classification task. The involvement of digital image classification allows the doctor and the physicians a second opinion, and it saves the doctors' and physicians' time. Despite the various publications on breast image classification, very few review papers are available which provide a detailed description of breast cancer image classification techniques, feature extraction and selection procedures, classification measuring parameterizations, and image classification findings. We have put a special emphasis on the Convolutional Neural Network (CNN) method for breast image classification. Along with the CNN method we have also described the involvement of the conventional Neural Network (NN), Logic Based classifiers such as the Random Forest (RF) algorithm, Support Vector Machines (SVM), Bayesian methods, and a few of the semisupervised and unsupervised methods which have been used for breast image classification.

Blockchain has numerous benefits such as decentralisation, persistency, anonymity and auditability. There is a wide spectrum of blockchain applications ranging from cryptocurrency, financial services, risk management, internet of things (IoT) to public and social services. Although a number of studies focus on using the blockchain technology in various application aspects, there is no comprehensive survey on the blockchain technology in both technological and application perspectives. To fill this gap, we conduct a comprehensive survey on the blockchain technology. In particular, this paper gives the blockchain taxonomy, introduces typical blockchain consensus algorithms, reviews blockchain applications and discusses technical challenges as well as recent advances in tackling the challenges. Moreover, this paper also points out the future directions in the blockchain technology.

Recent studies advocate that digital technologies are key enabling factors for the introduction of servitized business models. At the same time, these technologies support the implementation of the circular economy (CE) paradigm into businesses. Despite this general agreement, the literature still overlooks how digital technologies enable such a CE transition. To fill the gap, this paper develops a conceptual framework, based on the literature and a case study of a company implementing a usage-focused servitized business model in the household appliance industry. This study focuses on the Internet of Things (IoT), Big Data, and analytics, and identifies eight specific functionalities enabled by such technologies (improving product design, attracting target customers, monitoring and tracking product activity, providing technical support, providing preventive and predictive maintenance, optimizing the product usage, upgrading the product, enhancing renovation and end-of-life activities). By investigating how these functionalities affect three CE value drivers (increasing resource efficiency, extending lifespan, and closing the loop), the conceptual framework developed in this paper advances knowledge about the role of digital technologies as an enabler of the CE within usage-focused business models. Finally, this study shows how digital technologies help overcome the drawback of usage-focused business models for the adoption of CE pointed out by previous literature.

When users consult a trip planner, map or navigation system for directions, they are presented with several route options which often are evaluated based on shortest path or shortest duration. However, in some cases, users may require the choices of route to be evaluated on other criteria, such as safest route, particularly if users are new visitors to the city. With recent introductions of different platforms to crowdsource public experience and perceptions of safety, this data can be used to calculate safe route selection. However, such user generated contents are often inconsistent, sparse, or may not have a complete spatial coverage. This paper proposes Vector-based Diffusion and Interpolation Matrix (VDIM), a novel method to diffuse and interpolate information on spatial grid cells to calculate safety ratings of regions, which could be used to calculate the route safety.

This monograph considers how a classical challenge that commanders face in war—namely, making critical decisions on the basis of limited and often unreliable information—has been exacerbated in the era of big data. Data overload complicates the intelligence community’s efforts to identify and exclude disinformation, misinformation, and deception, and thus hampers its ability to deliver reliable intelligence to inform decision-makers in a timely manner. The military commander remains responsible for making a final decision, yet the great wealth of data now available through the intelligence cycle amplifies the risk of decision paralysis. With this in mind, technological solutions tend to be considered the most appropriate response for managing data overload and disinformation. While these remain relevant, they alone may be insufficient to equip the military commander with the necessary insight to guide decisions through the uncertainty of the big data environment. Rather, the militar...

Big Data can unify all patient related data to get a 360-degree view of the patient to analyze and predict outcomes. It can improve clinical practices, new drug development and health care financing process. It offers a lot of benefits such as early disease detection, fraud detection and better healthcare quality and efficiency. This paper introduces the Big Data concept and characteristics, health care data and some major issues of Big Data. These issues include Big Data benefits, its applications and opportunities in medical areas and health care. Methods and technology progress about Big Data are presented in this study. Big Data challenges in medical applications and health care are also discussed.

Vaccination is the most effective preventive measure against COVID-19 spread. While the WHO and other stakeholders fear vaccine nationalism, vaccine-hesitancy has become a topical issue among experts. Based on the evidence of vaccine hesitancy among Blacks, we explore the interrelatedness of psycho-social factors (personal health engagement, fear of COVID-19, perceived susceptibility, and vaccine-related attitude) likely to thwart vaccine acceptance in Africa. We sampled 1768 Ghanaian adults over 2 weeks from December 14, 2020, the first day a successful COVID-19 vaccine was administered in the US using an online survey. A higher level of personal health engagement was found to promote vaccine-related attitudes while reducing COVID-19 related fears, susceptibility, and vaccine hesitancy. Fear of COVID-19 and perceived vulnerability are significant contributors to the willingness to accept vaccination. This is an indication that health engagement alone will not promote vaccination willingness, but the fear and higher level of perceived susceptibility out of personal evaluation are essential factors in vaccination willingness. We recommend promoting health educational messages on COVID-19 vaccination ahead of any vaccination rollout in Africa, and such messages should contain some element of fear appeal.

Introduction The maternal mortality rate in Indonesia is still quite high. It requires good knowledge for early prevention. The study aimed to analyze the determinants of knowledge of the pregnancy danger signs in Indonesia. Methods The samples used were 85,832 women of childbearing age (15-49 years old). The variables included understanding of danger signs of pregnancy, types of residence, age, education, employment, marital status, wealth, parity, the autonomy of health, current pregnancy status, and media exposure. The determinant was pointed out by using binary logistic regression. Results Urban women were 1.124 times more likely to understand the pregnancy danger signs of than rural women. Older women could identify pregnancy danger signs better than those aged 15-19 years. The more educated a woman is, the higher knowledge of the pregnancy danger signs she has. Married women or those who live with their partner were at 1.914 times likely to identify the pregnancy danger signs than unmarried ones or those who have never been in a relationship. If the wealth status gets higher, knowledge of the pregnancy danger signs will be better too. Grande multiparous women were at 0.815 times more likely to understand the pregnancy danger signs than primiparous. Women with the autonomy of health had 1.053 times chances to identify the pregnancy danger signs than those without autonomy. Women who were currently pregnant had 1.229 times better understanding of the pregnancy danger signs than women who were not currently pregnant. Media exposure had a good effect on women's understanding of the pregnancy danger signs.

Companies are beginning to recognise the value of having vast volumes of data available in order to make the best choices to help their policies. The generation of digital evidence is increasingly the as emerging technology, the Internet, Security and social networks emerge. The word "Big Data" refers to the heterogeneous mass of digital data generated by businesses and individuals, which, due to its characteristics (large scale, various formats, and processing speed), necessitates the use of specialised and increasingly advanced machine storage and analysis methods. The aim of this essay is to describe Big Data & Hadoop, its principles, problems, and implementations, as well as the significance of Big Data Analytics.

Presently, great accomplishment on speechrecognition, computer-vision and natural-language processing has been achieved by deep-neural networks. To tackle the major trouble in synergetic or collaborative-filtering on the idea of hidden feedback; in this task we concentrated intensively on the techniques based on neural networks. Although a few latest researches have employed deep learning, they mostly used it to sculpt auxiliary facts, along with textual metaphors of objects and acoustic capabilities of music's. When it involves the major aspect in synergetic filtering; the communication between customer and object capabilities, still resorted to matrix factorization and implemented a core product on the hidden capabilities of customers and objects. We present a popular framework named Artificial Neural Synergetic Filtering (ANSF) to substitute the core makeup with a neural design which could be very efficient to analyze a data with a random feature. ANSF is ordinary and might specific; popularize matrix-factorization beneath its frame work. To improvise ANSF modeling with non-linearity's we propose to leverage a multi-layer perceptron to investigate customer-object communication function. In-depth experiments on actual-global databases display big improvisation of our proposed ANSF over the latest techniques. Investigational results manifest that the application of core layers of artificial neural networks gives improvised overall performance.

La plateforme opensource MyWebIntelligence (ou MyWI,) est un projet de recherche produit par le laboratoire MICA dans le cadre de l’Institut des Humanités Digitales qui vise à fournir un outil stratégique dans l’analyse et l’intelligence de la communication sur Internet.

Il s’agit d’un « crawler » d’une nouvelle génération qui constitue, à partir d’un dictionnaire de mots clés, une base de données des pages web qualifiées au service de l’intelligence stratégique. Il intégrera non seulement de nombreuses sources de données externes et Open Data mais aussi les derniers algorithmes de qualification de données (SNA, TextAnalysis, etc.)

Le CHU de Bordeaux a souhaité participer aux bêta tests de cette plateforme autour d’un projet pilote qui vise à mettre en place une étude débouchant sur une veille du discours thérapeutique et affilié. En effet le web a bouleversé l’ordre du discours médical. Première source d’information du patient et des accompagnants, il est un espace non structuré voire anarchique de prise de parole où la légitimité des savoirs et la hiérarchisation des connaissances s’avèrent difficiles. Pourtant ces « savoirs » sont aujourd’hui de plus en plus prégnant dans l’univers du malade.

MyWebIntelligence peut fournir les moyens d’appréhender, de qualifier et de hiérarchiser une masse de discours considérable afin de cartographier l’univers discursif de l’information médicale pédiatrique. L’ensemble du corps médical pourra ainsi non seulement avoir des études en temps réel sur le discours thérapeutique en ligne mais aussi mieux appréhender les acteurs hétérogènes du savoir et leurs stratégies argumentaires.

Dans la société digitale, si le médecin n’est plus seul dans la prescription médicale, le médecin 2.0 reprend une place prépondérante dans la diffusion de la prescription scientifique.

Big Data ist keine Technologie. Big Data verwendet Technologien, um auf die richtigen Fragen Antworten in Echtzeit zu finden und diese im Geschäftsalltag gewinnbringend umzusetzen. Die richtige Kombination von Big-Data-Methoden, Tools und Technologien wie Hadoop, In-Memory-Computing, NoSQL-Datenbanken, Social Media und traditionellem Data Mining ermöglichen Unternehmen jeder Grösse, Antworten auf ihre Fragen zu finden. Die Schwierigkeit besteht darin, die richtigen Fragen zu stellen. Big Data setzt voraus, dass ein Unternehmen kundenorientiert arbeitet. Ist dies der Fall, steigert Big Data die Wettbewerbsfähigkeit insbesondere auch von KMU.

Visual analytics (VA) combines the strengths of human and machine intelligence to enable the discovery of interesting patterns in challenging datasets.  Historically, most attention
has been given to developing the machine component—for example, machine learning or the human-computer interface. However, it is also essential to develop the abilities of the analysts themselves, especially at the beginning of their careers.

Until recently, knowledge-intensive work activities have predominantly taken place in office buildings as a specialized form of economic infrastructure. New digital technologies together with an economic and organizational transition from closed firms to open platforms has changed the pattern of work within the modern metropolis. The office building is no longer the sole workplace typology and work activity has intensified in other urban locations. The questions then are: "How might smart cities reinterpret workplace culture at the urban scale outside the framework of office buildings typology?" and "Which tools and methodologies can be used to make digital workplace culture visible at the urban scale?" In order to answer these questions, workplaces are observed not as private architectural spaces but as compositions of "subjective urban experiences". A Twitter data analysis provides evidence of workplace spatial culture within the innovative global cities of Amsterdam, London and Paris, interpreted as behavior settings. This analysis shows that office pattern locations are generally distributed independently to knowledge intensive business services and workplace demand, as expressed through social media analyses. In addition to office buildings, transit hubs, urban amenities and new digital services play a key role in reframing workplace location. Moving beyond generic visions for digital work in outer spaces, big data therefore provides specific insights and incentives for considering workplace design at the urban scale.

Information Technology has grown rapidly leading to challenges with communicating information with nodes at remote locations. There are many communication media, and certain varieties of wired links such as coaxial cables, where multiple nodes can all be linked to hear each other's transmissions either properly, correctly or with some non-zero probability. Generally, there are two rudimentary ways of sharing such stations or media, which can be the time-sharing, and frequency sharing. This paper tackles the rudimentary question of how such one common communication channel also called a shared medium can be distributed among the distinct nodes with maximum productivity. It analyzes wireless sensor networks and its accompanying technologies sighting their pros and cons. Focusing on time-sharing, we examine methodically two prevalent approaches used in obtaining this which are either by time division (split) multiple access (TDMA) or contention protocols of which both approaches are widely being used in today's systems. It also shows that with proper time of selecting the likelihood exigency of Tame Division Multiple Access's (TDMAs), utilization tends to 1/e (37%) but also tends to increase collision. To adapt the transmitted message likelihood, a format for calculation was adopted. The idea was to seek to gather to the favorable point of value. Slotted Aloha has twice the utilization of Unslotted-Aloha when the numbers of accumulated nodes grow. The Study also shows that each node will stake transmission within sprinkling fixed number of slots and this is guaranteed when using a related distribution in a finite window, but this is not the case with geometric distribution.

While advanced analysis of large dataset is in high demand, data sizes have surpassed capabilities of conventional software and hardware. Hadoop framework distributes large datasets over multiple commodity servers and performs parallel computations. We discuss the I/O bottlenecks of Hadoop framework and propose methods for enhancing I/O performance. A proven approach is to cache data to maximize memory-locality of all map tasks. We introduce an approach to optimize I/O, the in-node combining design which extends the traditional combiner to a node level. The in-node combiner reduces the total number of intermediate results and curtail network traffic between mappers and reducers.

Recently, big data (BD) has attracted researchers and practitioners due to its potential usefulness in decision-making processes. Big data analytics (BDA) is becoming increasingly popular among manufacturing companies as it helps gain insights and make decisions based on BD. However, there many barriers to the adoption of BDA in manufacturing supply chains. It is therefore necessary for manufacturing companies to identify and examine the nature of each barrier. Previous studies have mostly built conceptual frameworks for BDA in a given situation and have ignored examining the nature of the barriers to BDA. Due to the significance of both BD and BDA, this research aims to identify and examine the critical barriers to the adoption of BDA in manufacturing supply chains in the context of Bangladesh. This research explores the existing body of knowledge by examining these barriers using a Delphi-based analytic hierarchy process (AHP). Data were obtained from five Bangladeshi manufacturing companies. The findings of this research are as follows: (i) data-related barriers are most important , (ii) technology-related barriers are second, and (iii) the five most important components of these barriers are (a) lack of infrastructure, (b) complexity of data integration, (c) data privacy, (d) lack of availability of BDA tools and (e) high cost of investment. The findings can assist industrial managers to understand the actual nature of the barriers and potential benefits of using BDA and to make policy regarding BDA adoption in manufacturing supply chains. A sensitivity analysis was carried out to justify the robustness of the barrier rankings.

Companies desires for making productive discoveries from big data have motivated academic institutions offering variety of different data science (DS) programs, in order to increases their graduates' ability to be data scientists who are capable to face the challenges of the new age. These data science programs represent a combination of subject areas from several disciplines. There are few studies have examined data science programs within a particular discipline, such as Business (e.g. Chen et al.). However, there are very few empirical studies that investigate DS programs and explore its curriculum structure across disciplines. Therefore, this study examines data science programs offered by American universities. The study aims to depict the current state of data science education in the U.S. to explore what discipline DS programs covers at the graduate level. The current study conducted an exploratory content analysis of 30 DS programs in the United States from a variety of disciplines. The analysis was conducted on course titles and course descriptions level. The study results indicate that DS programs required varying numbers of credit hours, including practicum and capstone. Management schools seem to take the lead and the initiative in lunching and hosting DS programs. In addition, all DS programs requires the basic knowledge of database design, representation, extraction and management. Furthermore, DS programs delivered information skills through their core courses. Moreover, the study results show that almost 40 percent of required courses in DS programs is involved information representations, retrieval and programming. Additionally, DS programs required courses also addressed communication visualization and mathematics skills.

In the time of enormous information, information examination, business knowledge database administration assumes an imperative part from specialized business administration and exploration perspective. Over numerous decades, database administration has been a subject of dynamic examination. There are distinctive kind of database administration framework have been proposed over a time frame yet Relational Database Management System (RDBMS) is the one which has been most prevalently utilized as a part of scholastic examination and technical setup. As of late, Graphs databases recovered enthusiasm among the analysts for certain conspicuous reasons. A standout amongst the most critical explanations behind such an enthusiasm for a graph database is a result of the inalienable property of charts as a Graphs structure. Charts are available all around in the information structure, which speaks to the solid availability inside the information. The vast majority of the Graph database models are characterized in which information structure for graph and occasions are displayed as graph or speculation of a graph. In such graph database models, information controls are communicated by chart arranged operations and sort constructors. Presently days, the vast majority of this present reality applications can be demonstrated as a graph and one of the best genuine illustrations is social or organic system. This paper gives an outline of the diverse sort of graph databases, applications, and correlation between their models in view of a few properties.

This paper presents a case study of a Smart City initiative in Lyngby-Taarbæk municipality, which has successfully applied the triple helix model to create an informal collaboration between academia, government and private industry. The study recounts how a group of university students, participating in a big data hackathon, managed to create a smart city solution prototype based on open data in only 48 hours. The solution offers to make the municipality more cost efficient and improve citizen services, while simultaneously contributing to reduced CO2 emissions, thus addressing a difficult societal challenge. A special attention is paid to how the smart city vision based on the triple helix model is used to align interests and enable an informal collaboration between heterogeneous stakeholders. This collaboration represents an underlying value network, where value generation is moving beyond the simple profit-driven mechanisms of the markets. The paper identifies three main roles in the triple helix based value network: The Influencer, the Facilitator and The Implementer

Analyse électronique des données et logiciels spécialisés : les gros volumes ne font plus peur.
Il est aujourd’hui inconcevable pour les auditeurs et contrôleurs d’utiliser les méthodes de travail d’il y a seulement vingt ans. Si les contrôles portent toujours sur l’examen des faits, les volumes de données ne permettent plus de se contenter d’un examen superficiel des livres et des pièces comptables. Les contrôleurs et les auditeurs incluent donc l’analyse électronique des données qui peut se faire avec des outils logiciels courants.

During the recent years, a number of efficient and scalable frequent itemset mining algorithms for big data analytics have been proposed by many researchers. Initially, MapReduce-based frequent itemset mining algorithms on Hadoop cluster were proposed. Although, Hadoop has been developed as a cluster computing system for handling and processing big data, but the performance of Hadoop does not meet the expectation for the iterative algorithms of data mining, due to its high I/O, and writing and then reading intermediate results in the disk. Consequently, Spark has been developed as another cluster computing infrastructure which is much faster than Hadoop due to its in-memory computation. It is highly suitable for iterative algorithms and supports batch, interactive, iterative, and stream processing of data. Many frequent itemset mining algorithms have been redesigned on the Spark, and most of them are Aprioribased. All these Spark-based Apriori algorithms use Hash Tree as the underlying data structure. This paper investigates the efficiency of various data structures for the Spark-based Apriori. Although, the data structure perspective has been investigated previously, but for MapReduce-based Apriori, and it must be re-investigated in the distributed computing environment of Spark. The considered underlying data structures are Hash Tree, Trie, and Hash Table Trie. The experimental results on the benchmark datasets show that the performance of Spark-based Apriori with Trie and Hash Table Trie are almost similar but both perform many times better than Hash Tree in the distributed computing environment of Spark.

The digitalisation of the economy has increased tax administrations' traditional tax risks and introduced new tax non-compliance risks, such as the use of income suppression software and tax fraud associated with the use of alternative payment methods, such as cryptocurrencies. This study focuses on the global reform that took place among tax authorities from a tax risk management and assurance perspective. The study was executed in two phases, including a cross-national literature review to synthesise international reform regarding tax risk management and assurance in response to the digitalisation of the economy. This process was followed by interviews with risk, technology and data experts of 30 global tax authorities in order to evaluate the level of implementation of the global reform measures identified in the first research phase. The research results suggest an imbalance in reform among participants from developed and developing economies. An inability to optimise tax ri...

A flexible, efficient and secure networking architecture is required in order to process big data. However, existing network architectures are mostly unable to handle big data. As big data pushes network resources to the limits it results in network congestion, poor performance, and detrimental user experiences. This paper presents the current state-of-the-art research challenges and possible solutions on big data networking theory. More specifically, we present the state of networking issues of big data related to capacity, management and data processing. We also present the architectures of MapReduce and Hadoop paradigm with research challenges, fabric networks and software defined networks (SDN) that are used to handle today's idly growing digital world and compare and contrast them to identify relevant problems and solutions.

This project examines selected big data technologies from an enterprise usage viewpoint.
The addressed use cases include data warehousing, visualization, reporting and integration.
Industry trending big data technologies are examined in terms of their ability to address the needs of organizations.
The emphasis of this project is on the practical usage of big data technologies in analysing business data and thus achieving results that enhance business value for organizations. Said results could be a factor of business insight gained or competitive advantage gained from using newer and better technologies.

Therefore, this project will focus on the steps involved in setting up realistic use cases and the resultant outputs.
Firstly an overview of big data technologies is given with historical references followed by steps involved in setting up development and runtime environments.
Next the use cases are developed one by one and the results are used to justify the need for big data technologies in organizations.
Finally a use case demonstrating how business data stored in big data repositories could be shared within and outside of an organization using industry standards like REST, SOAP etc.



The end results are as follows :
(1) An appreciation of how big data technologies could be efficiently used in storing and analysing data for the purpose of enhancing business value for organizations,
(2) A demonstration of possible use cases showing how big data technologies could be employed in analysing business data,
(3) Enabling data scientists in organizations to take advantage of the latest technologies in analysing data efficiently.

As an occurrence that jeopardises vital national interests or the basic needs of the populace, a crisis necessitates rapid decision-making and coordination between various departments and agencies in order to resolve it effectively. As a result, crisis and disaster management systems are necessary and critical. Crisis and disaster response systems are intricate, requiring numerous phases, techniques, and resources. These systems require useful and necessary data that can be used to make future decisions more effectively, such as historical and current data on crises. The use of machine learning and big data technologies to process data from crises and disasters has the potential to yield significant results in this area. The first section of this document discusses crisis management systems and available tools, such as big data and machine learning. Additionally, a machine learning and big data approach to crisis management systems were developed, which included a description and experiments, as well as a discussion of the findings and the field's future directions.

International development and humanitarian organizations are  increasingly calling for digital data to be treated as a public good because of its value in supplementing scarce national statistics and informing interventions, including in emergencies. In response to this claim, a ‘responsible data’ movement has evolved to discuss guidelines and frameworks that will establish ethical principles for data sharing. However, this movement is not gaining traction with those who hold the highest-value data, particularly mobile network operators who are proving reluctant to make data collected in low- and middle-income
countries accessible through intermediaries. This paper evaluates how the argument for ‘data as a public good’ fits with the corporate reality of big data, exploring existing models for data sharing. I draw on the idea of corporate data as an ecosystem involving often conflicting rights, duties and claims, in comparison to the utilitarian claim that data’s humanitarian value makes it imperative to share them. I assess the power dynamics implied by the idea of data as a public good, and how differing incentives lead actors to adopt particular ethical positions with regard to the use of data.

This book brings together an impressive range of academic and intelligence professional perspectives to interrogate the social, ethical and security upheavals in a world increasingly driven by data. Written in a clear and accessible style, it offers fresh insights to the deep reaching implications of Big Data for communication, privacy and organisational decision-making. It seeks to demystify developments around Big Data before evaluating their current and likely future implications for areas as diverse as corporate innovation, law enforcement, data science, journalism, and food security. The contributors call for a rethinking of the legal, ethical and philosophical frameworks that inform the responsibilities and behaviours of state, corporate, institutional and individual actors in a more networked, data-centric society. In doing so, the book addresses the real world risks, opportunities and potentialities of Big Data.

Customers are increasingly attracted towards different e-commerce websites and applications for the purchase of products significantly. This is the reason the sellers are moving to different internet based services to sell their products online. The growth of customers in this sector has resulted in the use of big data analytics to understand customers' behavior in predicting the demand of items. It uses a complex process of examining large amount of data to uncover hidden patterns in the information. It is established on the basis of finding correlation between various parameters that are recorded, understanding purchase patterns and applying statistical measures on collected data. This paper is a document of the bottom-up strategy used to manage the selling price of a first-time product for maximizing profit while selling it online. It summarizes how existing customers' expectations can be used to increase the sale of product and attract the attention of the new customer for buying the new product.