Big Data

There is no doubt that air pollution harms human health. Municipal areas are the most affected by the degradation of the air quality by discharging anthropogenic gases from transport and industrial activities. This research collected remote sensing data from numerous satellite sensors to efficiently monitor the air quality in near-real-time. This paper deliberates the developed software based on the complex event processing calculating in streaming the air quality level in Morocco and Spain. Therefore, this computer program extracts only useful information rapidly from remote sensing big data helping decision-makers. This investigation takes up also a validation between the air quality measured by the ground station data of Andalucía and Madrid regions and the used satellite sensors data.

The growing attention and prominence afforded to analytics presents a genuine challenge for the operational research community. Many in the community have recognised this growth and sought to align themselves with analytics. For instance, the US operational research society INFORMS now offers analytics related conferences, certification and a magazine. However, as shown in this research, the volume of analytics-orientated studies in journals associated with operational research is comparatively low. This paper seeks to address this paradox by seeking to better understand what analytics is, and how operational research is related to it. To do so literature from a range of academic disciplines is analysed, in what is conceived as concurrent histories in the shared tradition of a management paradigm spread over the last 100 years. The findings of this analysis reveal new insights as to how operational research exists within an ecosystem shared with several other disciplines, and how interactions and ripple effects diffuse knowledge and ideas between each. Whilst this ecosystem is developed and evolved through interdisciplinary collaborations, individual disciplines are cast into competition for the attention of the same business users. These findings are further explored by discussing the implication this has for operational research, as well as considering what directions future research may take to maximise the potential value of these relationships.

Estratto da F. Chrisam, Le licenze sugli user-generated content. Click-wrap agreement, big data e circolazione online del diritto d’autore, Aracne, 2016, 125-153. La ricerca ha ad oggetto i rapporti contrattuali tra provider e user nel contesto del cosiddetto «web 2.0»: tale espressione viene comunemente utilizzata per descrivere il passaggio dall’originaria dimensione statica del web ad una dinamico-partecipativa in cui gli utenti delle reti internet non sono più meri fruitori passivi di contenuti digitali bensì autori; spesso infatti sono oggetto di upload dati che soddisfano il requisito costitutivo del carattere creativo e risultano pertanto protetti dal diritto d’autore. E’ per tale ragione che i contratti di erogazione dei servizi online prevedono tipicamente clausole con cui l’utente concede al provider una licenza sugli user-generated content: i servizi in esame implicano infatti operazioni che si sostanziano in atti di riproduzione, modificazione e comunicazione al pubblico dei contenuti immessi dagli utenti e che costituirebbero pertanto contraffazione in assenza di autorizzazione da parte del titolare dei diritti. Meno evidente è la ragione che sottende l’adozione di licenze talmente ampie da costituire in taluni casi vere e proprie previsioni in bianco: la giustificazione che viene comunemente proposta ruota attorno alla complessità dei servizi erogati online e alla necessità di un livello sufficiente di flessibilità in ragione dell’avanzamento tecnologico continuo che caratterizza l’ambiente digitale; tuttavia questa tesi non convince pienamente poiché il medesimo risultato potrebbe essere agevolmente ottenuto anche in presenza di previsioni maggiormente determinate nel loro contenuto e vincolate espressamente alla fornitura di specifici servizi o categorie di servizi in luogo del mero richiamo nominale della piattaforma. La tendenza sembra piuttosto quella di attribuire al provider una licenza in bianco sui contenuti immessi dall’utente tale da consentirne il più ampio riutilizzo per finalità che vengono definite di volta in volta unilateralmente dall’intermediario medesimo attraverso la modifica dell’architettura della piattaforma online.
Occorre allora chiedersi quale sia il ruolo effettivamente giocato da tali licenze nell’economia dei contratti di erogazione dei servizi online e se sia possibile individuarne dei limiti al di là del mero dato testuale.
Il settimo capitolo tenta di sciogliere i nodi relativi ai limiti delle licenze ricostruendo il contratto alla luce della funzione economica unitariamente perseguita dalle parti; ma il ricorso ai soli criteri ermeneutici generali non consente di individuare una soluzione univoca lasciando il dubbio tra due schemi possibili: (i) quello per cui la licenza sugli user-generated content si inserirebbe nell’economia di uno scambio tra servizi e pubblicità mirata; (ii) quello per cui tale licenza costituirebbe di per sé la controprestazione dell’utente a fronte della fornitura solo apparentemente gratuita dei servizi.
Si procede allora alla lettura congiunta dei canoni generali di interpretazione del contratto e delle previsioni specificamente dettate dalla disciplina autorale: l’accento viene posto in particolare sul ruolo giocato dalla Zweckübertragungstheorie di cui si offre una rilettura critica alla luce del nuovo contesto della contrattazione di massa online.

This edition has been dedicated to Democracy, Corruption and Development in Africa. It addresses the following issues:
a)  Democracy: Are regular elections enough?  Do election observer missions matter?
b) How can Africa strengthen its democracy and achieve development?
c)  How are data used and abused to manipulate election results?
d) Does corruption reduce chances of a sitting president or party n power being reelected?
e) How have governance institutions failed or slowed down African development?
f) How can aid help Africa to develop?
g) What role should  mass media, community media, social media and formal education play to catalyze African democracy and development?

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.

IoT (Internet of Things)[1] is an advanced automation and analytics system which exploits networking, sensing, big data, and artificial intelligence technology to deliver complete systems for a product or service. These systems allow greater transparency, control, and performance when applied to any industry or system. Today internet of things is a new revolution of the internet. A world where the real, digital and the virtual are converging to create smart environments that make energy, transport, cities and many other areas more intelligent. Different types of application like water monitoring, water pollution, air pollution, forest fire detection, smart homes, smart cities where each things can connect from anywhere to anyplace to make our life easier. This paper provide a broad overview on what is IoTS, different applications of IOTS, role of IoT of Smart city and challenges.

Although artificial intelligence is the product of science-fiction literature, it currently represents a significant branch of computer science dealing with intelligent behavior, machine learning, and machine adaptation. It has become a discipline that attempts to answer real-world problems. Artificial intelligence systems are nowadays widely used in economics and medicine, design or military. The role of archives is changing worldwide. In this grandiose transformation, archives need to be at the forefront of their own future, so that they can steer, guide, and not lose out.
The vast masses of information in archives provide an excellent platform for the exploitation of artificial intelligence. The mass of data can be a great help not only for research but also for policy preparation and in some areas of public administration in the not too distant future.

The current trend of technology advancement with digital devices such as smart phones, tablets, and laptops has shifted the way of societal life including teaching and learning. This initiative might also contribute to the typical approach of ICT (information and communication technology) to increasingly enhance the achievement through utilizing it in the education setting in the higher education context. Adopting social media in network data massively within the big data trends, the extraction of the particular means in providing structured data from enterprise resources data and sources of unstructured data from audio and video can begin with providing the value from information space such as message, conversation, transaction, and other sources. This chapter attempts to explore the design for teaching and learning in big
data approach. Attempts to achieve particular advantage of big data with analytic process could provide the innovative environment of adaptive online technology to enhance pedagogy and technology skill to support the initiative way of teaching and learning process in the higher education (HE) context.

Big Data analytics is a differentiator within the field of commerce. The increasingly amount of data that is available can be used as the driving force behind business intelligence in order to create business value, and thus competitive advantage for companies. This research encompasses the quest of Indicia in Big Data services and product proposition development. It will look at the critical success factors of Big Data analysis for Business Intelligence. The research focuses on the technical and organizational aspects of Business Intelligence and Big Data Analysis projects. This encompasses data, IT infrastructure and Big Data analysis applications. In addition the development, implementation and adoption of such applications and processes is also discussed.

In this thesis, I have examined whether the current approach to competition policy is suitable for the distinct characteristics of the digital economy. It is particularly difficult to define the relevant market and rely on market share test as a proxy for market power in the digital economy mainly because of its dynamic nature. As the market boundaries shift so rapidly in dynamic markets, having a static perspective when defining the relevant market might fall short when assessing competitive constraints that an undertaking may face in the near future. In this regard, this thesis calls for a change in the traditional assessment of market power, and it proposes to replace the relevant market/market share paradigm with a more economic effects-based method, such as estimating the elasticity of residual demand. By doing so, competition authorities can assess competitive constraints from a broader perspective without necessarily being limited to a particular relevant market or any formalistic structure.

In order to identify the most suitable way to assess market power in the digital economy, this thesis is structured as follows. Chapter II explains what digital economy is, how it emerged, and what makes it different from the rest of the economy by identifying its distinguishing characteristics. After providing an overview of the digital economy, Chapter III gives more theoretical insight to the concept of market power. First, it explains the importance of market power within the social market economy. In the second part, it introduces various definitions of market power as provided by different institutions and scholars, describing the defining characteristics of these definitions afterwards. Having explained the concept of market power, Chapter IV focuses on how market power is assessed. By doing so, it explains not only the traditional indirect assessment of market power, but also the direct assessment of the latter. After clarifying what the digital economy is, as well as the concept of market power and how it is assessed, Chapter V evaluates the suitability of current understanding of market power in the digital economy. Then it explains what the challenges of assessing market power in the digital economy are when applying conventional techniques. In the light of these findings, it finally suggests a blueprint of how to assess market power in the digital economy. Lastly, Chapter VI provides the summary of findings and the final conclusion.

Cloud computing offers an innovative business model to enterprise for IT services consumption and delivery. Software as a Service (SaaS) is one of the cloud offerings that attract organisations as a potential solution in reducing their IT cost. However, the vast diversity among the available cloud SaaS services makes it difficult for customers to decide whose vendor services to use or even to determine a valid basis for their selections. Moreover, this variety of cloud SaaS services has led to proprietary architectures and technologies being used by cloud vendors, increasing the risk of vendor lock-in for customers. Therefore, when enterprises interact with SaaS providers within the purview of the current cloud marketplace, they often encounter significant lock-in challenges to migrating and interconnecting cloud. Hence, the complexity and variety of cloud SaaS service offerings makes it imperative for businesses to use a clear and well understood decision process to procure, migrate and/or discontinue cloud services. To date, the expertise and technological solutions to simplify such transition and facilitate good decision making to avoid lock-in risks in the cloud are limited. Besides, little investigation has been carried out to provide a comprehensive decision framework to support enterprises on how to avoid lock-in risks when selecting and implementing cloud-based SaaS solutions within existing environments. Such decision framework is important to reduce complexity and variations in implementation patterns on the cloud provider side, while at the same time minimising potential switching cost for enterprises by resolving integration issues with existing IT infrastructures. This paper proposes a holistic 6-step decision framework that enables an enterprise to assess its current IT landscape for potential SaaS replacement, and provides effective strategies to mitigate vendor lock-in risks in cloud (SaaS) migration. The framework follows research findings and addresses the core requirements for choosing vendor-neutral interoperable and portable cloud services without the fear of vendor lock-in, and architectural decisions for secure SaaS migration. Therefore, the results of this research can help IT managers have a safe and effective migration to cloud computing SaaS environment.

Conferência ao IV Congresso Interativo Virtual "Humanos Direitos Máquinas - amigos ou inimigos". Universidad Nacional de Lanús / E-Justicia Latinoamérica, Argentina. Dia 20 de novembro de 2019.
A mesma retoma a comunicação apresentada na "Nordic Conference on Legal Informatics - 2019", na Universidade da Lapónia, Rovaniemi, dia 14 de novembro de 2019.

Musicology is a small discipline within the wide spectrum of human knowledge, yet it is already divided into various branches, each with its own societies, conferences, journals, jargons, degrees, prejudices, …, and jobs. Although they share their object of investigation-"the art of music as a physical, psychological, aesthetic, and cultural phenomenon", these branches very often ignore one another. Research in musicology is mostly a solitary task, as investigations, papers, and publications are commonly signed by single authors, in contrast with STEM disciplines where teamwork is the rule. This is in part the result of tradition-the "Musicological Toolbox"-but also the aftermath of the job market and financing programs. Large funding schemes such as the European Research Council (ERC) grants are becoming a major disruptive factor in many disciplines in the humanities, including musicology. Scholars in all fields now have the opportunity to build research teams, and most of their members receive their salaries to exclusively work on the project. In other words, we are starting to build what could be called a Musicology Lab, learning along the way how teamwork is reshaping and transforming the Musicological Toolbox, the look and feel of our discipline, the way we work as well as the way we publish and disseminate our results. This paper presents some of the key features of the ERC Didone project, one of its principal tasks being to create a digitally encoded corpus of some 3,000 arias in MusicXML format from about 180 musical settings of a small number of opera librettos by Pietro Metastasio. It focuses on some of the project's research tasks, emphasizing how the skills of a team of eighteen scholars with very different expertise-historical musicology, music theory and analysis, cultural history, librettology, archival research, music performance, music engraving, MIR, computer science, and statistical modeling-combine to explore the potential answer(s) to the main research question of the project: How are emotions expressed through music?

It is nowadays a common place to say that the sharing economy is not really about sharing but about making profits and benefiting a few much more than others. A movement that takes the best of the technologies of sharing economy platforms but orients it to benefiting all, platform cooperativism, is on the rise. Nonetheless, it is far from being popular and nothing indicates that it will. This paper investigates the reasons why dominant platforms remain dominant and proposes a policy that aims at curtailing their dominance, fostering platform cooperativism and maximizing the beneficial societal effects that can be derived from exploiting the data generated in platforms. The paper is structured as follows. Section 1 reviews current definitions of the sharing economy, points out their contributions and limitations and offers a novel and more accurate definition. Section 2 briefly introduces platform cooperativism to show why it can be a tool to fix many of the problems of the sharing economy. Section 3 explains and discusses market power mechanisms specific to the sharing economy that help dominant platforms to remain dominant. Some already existing and proposed solutions to counter these market power mechanisms such as reputation passports, a market for personal data and antitrust remedies are evaluated. Section 4 presents a general policy proposal based on making data a common in the sharing economy using reciprocity licenses. Section 5 offers some clarifications regarding the proposal and sketches some of its shortcomings and open questions that arise from it.

Data analytics (DA) is the process of examining data sets in order to draw conclusions
about the information they contain, increasingly with the aid of specialized systems and
software. Data analytics technologies and techniques are widely used in commercial industries to
enable organizations to make more-informed business decisions and by scientists and researchers
to verify or disprove scientific models, theories and hypotheses. (Rouse and Stedman, 2016).
This project will seek to discover the applicability of DA within higher education and
specifically, the usability of DA to the University of the West Indies (UWI). By using DA, the
UWI can achieve many business outcomes and be more responsive to their target market.

Dünyanın sahip olduğu kaynaklar hızlı bir şekilde tükenirken bu durum karşısında şehirlerin mevcut altyapıları yetersiz kalmaktadır. Şehirlerdeki hızlı nüfus artışı, istihdam, barınma, kaliteli yaşam, eğitim, sağlık, ulaşım, su, elektrik, temiz hava ve yeşil alan gibi ihtiyaçların karşılanmasını güçleştirmektedir. Ortaya çıkan bu durum, mevcut altyapıların daha verimli ve akıllı kullanımını zorunlu hale getirmektedir. Sunulacak hizmetlerin daha az maliyetle ve daha verimli şekilde gerçekleşmesini sağlayacak olan akıllı teknolojiler, şehirlerde sürdürülebilir yaşam için kilit bir rolde olacaktır. Akıllı şehirler, doğal kaynakların, insan kaynağının ve finansal kaynakların etkin kullanımını sağlarken, maliyet tasarrufunu da teşvik edecektir. Bu yeni süreçte ulusal ve yerel bütün karar alıcılara en büyük destek ise Bilgi ve İletişim Teknolojilerinden (BİT) gelecektir. Bu kapsamda bilişim dünyasında büyük ses getiren teknolojilerden birisi olan Nesnelerin İnterneti (IoT) kavramı karşımıza çıkmaktadır. IoT, çeşitli haberleşme protokolleri sayesinde birbirleriyle haberleşen farklı nesnelerin bir araya gelerek bir ağ oluşturmasını ve bilgi paylaşmasını sağlayan anahtar bir teknolojidir. Ulaşım sistemlerinin gelişmesi, trafik sıkışıklığının azaltılması, atık yönetiminin sağlanması gibi insan yaşamının kalitesini arttırmayı amaçlayan hizmetlerin IoT ile gerçekleştirilmesi, şehirleri akıllı hale dönüştürür. Bu çalışmada, IoT’nin mevcut durumu, gelişimi, karşılaştığı zorluklar ve gelecek yönelimleri sunulmuştur. IoT tabanlı çeşitli akıllı şehir projelerinin, yaşam laboratuvarlarının ve sınama ortamlarının genel değerlendirmesi yapılmıştır.

Teaching the research paper has been considered a “present controversy” for over fifty years (Saalbach, 1963). Some scholars believe that it prepares students for “generalized academic writing” (Reiff and Bawarshi, 2011; Sutton, 1997; Schwegeler and Shamoon, 1982). However, others have referred to the research paper as a “fossilized,” “pseudo-academic,” “mutt genre” (Nowacek, 2011; Wardle, 2007; Carroll, 1999; Larsen, 1982). This chapter argues that radical openness and information overload--two major features of new media composing environments--compromise the relevance of the traditional research paper. First, the research paper has been praised and blamed for its (in)ability to help students learn the importance of writing from sources (Brent 2013, Rooney 2000). However, the parameters of most research assignments are incapable of matching the scale of data available at any given time. For instance, typical constraints like “use 5-7 sources” for a first-year college writing course seems ridiculous when it takes seconds to retrieve thousands, if not millions, of possible sources. Next, open-access publishing and collaborative authorship have transformed the way that research is conducted and recognized across disciplines. Although teaching students how to crowdsource research makes the most sense in a data deluge, the research paper privileges single authorship. Moreover, the problems of authorship and source attribution implicate the research paper in certain kinds of fraud. For instance, plagiarism and term paper mills are a “built-in” problem of research papers (Howell, 1977).  In sum, this chapter focuses on what the term "research" means in a context of too much information.  It concludes by offering a brief discussion of literature relevant to this topic.

The concept of sovereignty has come to frame a number of data governance proposals by the Indian government. To understand the scope, import and consequences of these reassertions of sovereignty, it is, however, important to unpack the nature of the claims that have been put forward. In particular, to what extent do these promote the exercise of autonomy and choice by the Indian people? In order to benefit the people of India, assertions of sovereignty in the face of data colonialism will need to take into account that data is not merely a resource “out there”, but increasingly functions as an extension of our bodies. As this analysis will show, current conceptualisations of data sovereignty fail to do so; for now, they therefore merely entail a transfer of power to domestic elites while doing little to return sovereignty to the people of India.

Sovereignty is seeing renewed relevance in the age of data in India as it has become the framework of choice in a number of data governance proposals by the Indian government. To understand the scope, import and consequences of these reassertions of sovereignty, however, it is important to unpack the nature of these claims as they have been put forward. In particular, to what extent does this type of sovereignty allow for the exercise of autonomy and choice of the Indian people? This paper will demonstrate that such assessments crucially depend on how we construct the nature of data. In most dominant discourses, data is described as a resource of some sort. However, in practice the line between our physical bodies and our virtual bodies is increasingly becoming irrelevant: data, then, emerges not so much as a resource that is simply out there, but as an extension of our bodies. In order to benefit the people of India, assertions of sovereignty in the face of data colonialism will need to take these shifting realities regarding the nature of data into account. Through an assessment of policy proposals relating to sovereignty in the realm of data and new technologies, we seek to examine to what extent policy in India does indeed recognise these new realities, and what the value of these new assertions of sovereignty for the people of India consequently is.

El estudio alerta de algunos peligros del big data y la inteligencia artificial: los datos masivos y los algoritmos no son objetivos, pueden tener importantes sesgos o “incrustados” valores contrarios a los constitucionales. Frente a la discriminación, brecha y sesgo algorítmicos, se plantean garantías  en el ámbito del análisis de impacto de privacidad o la no discriminación por defecto y en el diseño. Se hace referencia a la pérdida de la esfera pública y la postverdad, así como a la libertad de expresión de las máquinas. También se intenta fundamentar un derecho a la “transparencia algorítmica” o se plantean cuestiones por el uso del Big data en inteligencia artificial en la aplicación de la ley y la justicia. Se afirma la superación del consentimiento como garantía efectiva de la privacidad, las dificultades de proyectar los principios de la protección de datos o los derechos ARCO o la importancia de la anonimización de datos.

Summary
Some dangers of big data and artificial intelligence are warned: data and non-objective algorithms can have important biases or "embedded" values. Guarantees against discrimination and "algorithmic bias" are affirmed, in the scope of privacy impact assessment or non-discrimination by default and by design. Some reflections are made on the massive personalization of information, the loss of the public sphere and the post-truth, as well as the freedom of expression of machines. Also a right to "algorithmic transparency" is intended to affirm and base. Different questions are exposed about the use of Big Data and artificial intelligence in law enforcement and justice. Finally, about privacy and data protection, it is stated that the guarantee of consent is outdated and ineffective and the difficulties of projecting the principles of data protection, as well as the importance and problems of anonymization are also affirmed.

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.

Circular Economy  has been usually indicated as a promising approach to promote economic development and sustainability. Despite this general agreement, the Circular Economy paradigm is still little adopted by companies, as they need to face several challenges. In this context, Product-Service Systems (PSS) Business Models play a major role, as they have been proposed as an opportunity for promoting sustainability. Today, digital technologies are seen as a  key factor to enable PSS, allowing at the same time the introduction of Circular  Economy  into companies.  However,  little attention has been set on how  new  digital technologies can  overcome  in practice  the main  Circular  Economy challenges. To fill this gap, this paper explores such a role through a case study of a company who leverages Internet of Things, Big Data and Analytics in the provision of its PSS Business Model. Based on the findings of the empirical investigation, the role that the selected digital technologies play in overcoming the Circular Economy challenges is presented and discussed

An important text mining problem is to find, in a large collection of texts, documents related to specific topics and then discern further structure among the found texts. This problem is especially important for social sciences, where the purpose is to find the most representative documents for subsequent qualitative interpretation. To solve this problem, we propose an interval semi-supervised LDA approach, in which certain predefined sets of keywords (that define the topics researchers are interested in) are restricted to specific intervals of topic assignments. We present a case study on a Russian LiveJournal dataset aimed at ethnicity discourse analysis.

Leakage and misuse of sensitive data is a challenging problem to enterprises. It has become more serious problem with the advent of cloud and big data. The rationale behind this is the increase in outsourcing of data to public cloud and publishing data for wider visibility. Therefore Privacy Preserving Data Publishing (PPDP), Privacy Preserving Data Mining (PPDM) and Privacy Preserving Distributed Data Mining (PPDM) are crucial in the contemporary era. PPDP and PPDM can protect privacy at data and process levels respectively. Therefore, with big data privacy to data became indispensable due to the fact that data is stored and processed in semi-trusted environment. In this paper we proposed a comprehensive methodology for effective sanitization of data based on misusability measure for preserving privacy to get rid of data leakage and misuse. We followed a hybrid approach that caters to the needs of privacy preserving MapReduce programming. We proposed an algorithm known as Misusability Measure-Based Privacy Preserving Algorithm (MMPP) which considers level of misusability prior to choosing and application of appropriate sanitization on big data. Our empirical study with Amazon EC2 and EMR revealed that the proposed methodology is useful in realizing privacy preserving Map Reduce programming.

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...

A computer views all kinds of visual media as an array of numerical values. As a consequence of this approach, they require image processing algorithms to inspect contents of images. This project compares 3 major image processing algorithms: Single Shot Detection (SSD), Faster Region based Convolutional Neural Networks (Faster R-CNN), and You Only Look Once (YOLO) to find the fastest and most efficient of three. In this comparative analysis, using the Microsoft COCO (Common Object in Context) dataset, the performance of these three algorithms is evaluated and their strengths and limitations are analysed based on parameters such as accuracy, precision and F1 score. From the results of the analysis, it can be concluded that the suitability of any of the algorithms over the other two is dictated to a great extent by the use cases they are applied in. In an identical testing environment, YOLO-v3 outperforms SSD and Faster R-CNN, making it the best of the three algorithms.

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.

There has been an unprecedented growth and increase in the domain unstructured/semi-structured data is increasing over the years. While relational database systems remain popular and relevant, they are incapable of handling the growth level of unstructured data in the area of web applications. Conflating the benefits of a simple NoSQL storage engine with the relational databases and the unique ability of presenting query results from the duo to users at minimal costs have been a critical challenge in the research community. So far so good there has been a tremendous works such as using SQL with an extension of NQP on transformed NoSQL data which is store as SQL virtual relation, querying Apache Cassandra with SQL after altering the data structure, querying both world of relational and NoSQL and produced two results instead of single output. The TripleFetchQL system developed enables users to query relational and NoSQL databases and presents query results as if they were querying the familiar relational database alone. Furthermore, TripleFetchQL provide applications the ability of leveraging the benefits of relational and NoSQL databases at the small cost of learning the simple syntax of the TripleFetchQL system.

Introduction This paper presents a lifelong learning framework which constantly adapts with changing data patterns over time through incremental learning approach. In many big data systems, iterative re-training high dimensional data from scratch is computationally infeasible since constant data stream ingestion on top of a historical data pool increases the training time exponentially. Therefore, the need arises on how to retain past learning and fast update the model incrementally based on the new data. Also, the current machine learning approaches do the model prediction without providing a comprehensive root cause analysis. To resolve these limitations, our framework lays foundations on an ensemble process between stream data with historical batch data for an incremental lifelong learning (LML) model. Case description A cancer patient’s pathological tests like blood, DNA, urine or tissue analysis provide a unique signature based on the DNA combinations. Our analysis allows perso...

Crime remains to continue to be a serious threat to all groups and peoples throughout the world together with the complexity in technology and procedures that are being manipulated to allow extremely complex criminal acts. Data mining is now an essential tool for examining, reducing, and avoiding crime and is manipulated by both government and private institutions across the globe which is the method of revealing hidden information from Big Data. The data mining methods themselves are temporarily presented to the reader and this information includes the social network analysis, neural networks, naive Bayes rule, support vector machines, decision trees, association rule mining, clustering, entity extraction, and amongst others. The main objective of this article is to offer a concise analysis of the data mining applications in crime. Finally, the article evaluates applications of data mining in crime, including a considerable quantity of the study to date, displayed in chronological order with a summary table of numerous crucial information mining applications in the crime area as a directory of reference.

As informações que surgem da Internet das Coisas (IoT) estarão, com o passar dos anos, integradas ao dia a dia e disfarçadas nos objetos do cotidiano. Destarte, para coleta e processamento dessas informações tem-se o conceito de Fast Data, que são aplicações e softwares de análise de dados e informações em tempo real que permitem o conhecimento a partir de dados antes considerados de pouca ou nenhuma importância. O Fast Data tem como base a velocidade, por isso é importante na IoT, como na detecção de poluição e catástrofes naturais, controle da emissão de gases poluentes na atmosfera terrestre, emissão de alertas de terremotos e tsunamis, entre outros. Sendo assim, este trabalho estuda a utilização e importância da informação em todo esse contexto, de forma especial nos cenários que tangenciam a orquestração de criação de informações a partir de análises em tempo real.

We introduced a new computation model that reflects the assumptions of the map-reduce framework, but allows for networks of processes other than Map feeding Reduce. We illustrated the benefit of our model by showing how to improve the computation of the 3-way join and by developing algorithms for merging and sorting. The cost measures by which we evaluate the algorithms are the communication among processes and the processing time, both total over all processes and elapsed (i.e., exploiting parallelism). The input file is hold on as a group of files on persistent storage. The goal of a sorting system is to rework this input file into associate ordered set of output files, conjointly hold on persistent storage, such the concatenation of those output files so as constitutes the sorted version of the input file. Our goal is to style and implement a sorting system that may sort datasets of the targeted size whereas achieving a good exchange between speed, resource utilization, and cost.

Perpustakaan sebagai sarana sumber informasi dan ilmu pengetahuan untuk menyimpan bahan pustaka yang dipakai oleh pemakai untuk menggali ilmu sumber informasi. Penelitian dilakukan pada salah satu Perpustakaan yang ada di kota Batam. Perpustakaan ini memiliki beragam koleksi buku seperti buku umum, karya ilmiah, bahasa, sejarah, dan lain sebagainya. Permasalahan yang sering terjadi adalah buku yang dipinjam kadang tidak tersedia, selain itu pihak perpustakaan juga mengalami kesulitan karena tidak tau berapa jumlah buku yang dipinjam sehingga pihak Perpustakaan melihat kembali catatan transaksi meminjam buku pada buku tamu. Untuk itu dibuatlah suatu sistem dengan pengolahan jumlah data yang besar dengan teknik data mining metode k-means. Dari hasil yang diperoleh data peminjam buku yang telah diproses mendapatkan buku yang banyak dipinjam terdapat pada cluster 1 sebanyak 9 item, buku yang paling sedikit dipinjam terdapat pada cluster 2 sebanyak 15 item, buku yang cukup banyak dipinja...

One of the main features of the technology is saving the personal information and the many ways to access this information and build many reports to use it to help the organizations to take the right decisions. The personal information may contain sensitive information, and that may lead to abuse of using this information due to another purpose such protecting the others or due to losing to control for the people who have access to this information. McFarland, M. (2012). Al-Khouri, A. (2012). Explains that multiple owner can own the personal information as the below Figure and when the user registers their information they give them the authority to use this data and using this information depend on the ethical of the organizations and to the country laws. The organizations must consider the human rights of using the personal information and Erm.ecs.soton.ac.uk, (2015). Explains that the information must use it without harm the others maintain their privacy by taking the required pe...

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.

Modeling surface water quality using soft computing techniques is essential for the effective management of scarce water resources and environmental protection. The development of accurate predictive models with significant input parameters and inconsistent datasets is still a challenge. Therefore, further research is needed to improve the performance of the predictive models. This study presents a methodology for dataset pre-processing and input optimization for reducing the modeling complexity. The objective of this study was achieved by employing a two-sided detection approach for outlier removal and an exhaustive search method for selecting essential modeling inputs. Thereafter, the adaptive neuro-fuzzy inference system (ANFIS) was applied for modeling electrical conductivity (EC) and total dissolved solids (TDS) in the upper Indus River. A larger dataset of a 30-year historical period, measured monthly, was utilized in the modeling process. The prediction capacity of the develo...