Between 2006 and 2011, humanity accumulated 1,600 EB of data. As a result of this growth, there is now more data produced than available storage. This article explores the problem of “Big Data,” arguing for an epistemological approach as a possible solution to this ever-increasing challenge.

Recently, it has been argued that the use of Big Data transforms the sciences, making data-driven research possible and studying causality redundant. In this paper, I focus on the claim on causal knowledge by examining the Big Data project EXPOsOMICS, whose research is funded by the European Commission and considered capable of improving our understanding of the relation between exposure and disease. While EXPOsOMICS may seem the perfect exemplification of the data-driven view, I show how causal (...) knowledge is necessary for the project, both as a source for handling complexity and as an output for meeting the project’s goals. Consequently, I argue that data-driven claims about causality are fundamentally flawed and causal knowledge should be considered a necessary aspect of Big Data science. In addition, I present the consequences of this result on other data-driven claims, concerning the role of theoretical considerations. I argue that the importance of causal knowledge and other kinds of theoretical engagement in EXPOsOMICS undermine theory-free accounts and suggest alternative ways of framing science based on Big Data. (shrink)

Big Data must be processed with advanced collection and analysis tools, based on predetermined algorithms, in order to obtain relevant information. Algorithms must also take into account invisible aspects for direct perceptions. Big Data issues is multi-layered. A distributed parallel architecture distributes data on multiple servers (parallel execution environments) thus dramatically improving data processing speeds. Big Data provides an infrastructure that allows for highlighting uncertainties, performance, and availability of components. DOI: 10.13140/RG.2.2.12784.00004 .

Big Data has the potential to enable unprecedentedly rigorous quantitative modeling of complex human social relationships and social structures. When such models are extended to nonhuman domains, they can undermine anthropocentric assumptions about the extent to which these relationships and structures are specifically human. Discoveries of relevant commonalities with nonhumans may not make us less human, but they promise to challenge fundamental views of what it is to be human.

Healthcare professionals decide wisely about personalized medicine, treatment plans, and resource allocation by utilizing big data analytics and machine learning. To guarantee that algorithmic recommendations are impartial and fair, however, ethical issues relating to prejudice and data privacy must be taken into account. Big data analytics and machine learning have a great potential to disrupt healthcare, and as these technologies continue to evolve, new opportunities to reform healthcare and enhance patient outcomes may arise. In order to investigate (...) the patient’s outcomes with empirical evidence, this research was conducted using an online survey to incorporate healthcare professionals, patient’s reviews, and clinical staff. The data were analyzed using SmartPLS 4.0 to predict the structural model. The findings revealed a direct impact as positive influence of using machine learning on healthcare performance and patient outcomes through big data analytics. Moreover, it is evident that this can lead to personalized treatment plans, early interventions, and improved patient outcomes. Additionally, big data analytics can help healthcare providers optimize resource allocation, improve operational efficiency, and reduce costs. The impact of big data analytics on patient outcome and healthcare performance is expected to continue to grow, making it an important area for investment and research. (shrink)

This review delves into the influence of big data analytics on project management effectiveness and project success rates. By examining applications, accomplishments, hindrances, and emerging developments in the context of big data analytics and project management, this review provides insights into its transformative potential. Results indicate that big data analytics fosters improved project performance, more robust risk management, and heightened adaptability. However, challenges related to data quality, privacy, and project manager training remain to be addressed. This (...) review underscores the value of data-driven decision-making for both practitioners and researchers in the project management field. (shrink)

Big data ethics involves adherence to the concepts of right and wrong behavior regarding data, especially personal data. Big Data ethics focuses on structured or unstructured data collectors and disseminators. Big data ethics is supported, at EU level, by extensive documentation, which seeks to find concrete solutions to maximize the value of big data without sacrificing fundamental human rights. The European Data Protection Supervisor (EDPS) supports the right to privacy and the right (...) to the protection of personal data in the respect of human dignity. (shrink)

In this article, we show how it is possible to lawfully buy an election. The method we describe for buying an election is novel. The key things that make it possible to buy an election are the existence of public voter registration lists where one can see whether a given elector has voted in a particular election, and the existence of Big Data Analytics that with a high degree of accuracy can predict what a given elector will vote in (...) an upcoming election. Someone interested in buying an election can enter an employment contract with all, or some of, the opponent electors where these electors are paid to do a job that prevents them from voting. By purchasing access to the public voter registration lists, it is possible to verify ex-post whether the opponent electors that one has signed a contract with have abstained. In the last two sections of the article, we discuss several barriers that can undermine an attempt to buy an election in the manner we identify. (shrink)

What is the epistemological status of Big Data? Is there really place for them in a scientific search for new empirical laws?

Principalele probleme cu care se confruntă oamenii de știință în lucrul cu seturile mari de date (Big Data), evidențiind principale aspecte etice, luând în considerare inclusiv legislația din Uniunea Europeană. După o scurtă Introducere despre Big Data, secțiunea Tehnologia prezintă aplicațiile specifice în cercetare. Urmează o abordare a principalelor probleme filosofice specifice în Aspecte filosofice, și Aspecte legale cu evidențierea problemelor etice specifice din Regulamentul UE privind protecția datelor 2016/679 (General Data Protection Regulation, "GDPR"). Secțiunea Probleme etice (...) detaliază aspectele specifice generate de Big Data. După o scurtă secțiune Cercetarea Big Data, finalizez lucrarea cu prezentarea Concluziilor pentru etica cercetării în lucrul cu Big CUPRINS: Abstract 1. Introducere - 1.1 Definiții - 1.2 Dimensiunile Big Data 2. Tehnologia - 2.1 Aplicații - - 2.1.1 În cercetare 3. Aspecte filosofice 4 Aspecte legale - 4.1 GDPR - - Etapele procesării datelor personale - - Principiile procesării datelor - - Politica de confidențialitate și transparența - - Scopurile procesării datelor - - Confidențialitate prin design și implicită - - Paradoxul (legal) al Big Data 5. Probleme etice - Etica în cercetare - Conștientizarea - Consimțământul - Controlul - Transparența - Încrederea - Proprietatea - Supravegherea și securitatea - Identitatea digitală - Realitatea ajustată - De-anonimizarea - Inegalitatea digitală - Confidențialitatea 6. Cercetarea Big Data Concluzii Bibliografie DOI: 10.13140/RG.2.2.27629.33761 . (shrink)

Termenul Big Data se referă la extragerea, manipularea și analiza unor seturi de date care sunt prea mari pentru a fi tratate în mod obișnuit. Din această cauză se utilizează software special și, în multe cazuri, și calculatoare și echipamente hardware special dedicate. În general la aceste date analiza se face statistic. Pe baza analizei datelor respective se fac de obicei predicții ale unor grupuri de persoane sau alte entități, pe baza comportamentului acestora în diverse situații și folosind tehnici (...) analitice avansate. Se pot identifica astfel tendințe, necesități și evoluții comportamentale ale acestor entități. (shrink)

DNA sequencers, Twitter, MRIs, Facebook, particle accelerators, Google Books, radio telescopes, Tumblr: what do these things have in common? According to the evangelists of “data science,” all of these are instruments for observing reality at unprecedentedly large scales and fine granularities. This perspective ignores the social reality of these very different technological systems, ignoring how they are made, how they work, and what they mean in favor of an exclusive focus on what they generate: Big Data. But no (...) data, big or small, can be interpreted without an understanding of the process that generated them. Statistical data science is applicable to systems that have been designed as scientific instruments, but is likely to lead to confusion when applied to systems that have not. In those cases, a historical inquiry is preferable. (shrink)

Big Data ethics involves adherence to the concepts of right and wrong behavior regarding data, especially personal data. Big Data ethics focuses on structured or unstructured data collectors and disseminators. Big Data ethics is supported, at EU level, by extensive documentation, which seeks to find concrete solutions to maximize the value of Big Data without sacrificing fundamental human rights. The European Data Protection Supervisor (EDPS) supports the right to privacy and the right (...) to the protection of personal data in the respect of human dignity. DOI: 10.13140/RG.2.2.30867.43041. (shrink)

Le big data peut générer, par inférences, de nouvelles connaissances et perspectives. Le paradigme qui résulte de l'utilisation du big data crée de nouvelles opportunités. L'une des principales préoccupations dans le cas du big data est que les scientifiques des données ont tendance à travailler avec des données sur des sujets qu'ils ne connaissent pas et n'ont jamais été en contact, étant éloignés du produit final de leur activité (l'application des analyses). Une étude récente (Tanner 2014) indique (...) que cela peut être la raison d'un phénomène connu sous le nom d'aliénation numérique. DOI: 10.13140/RG.2.2.34700.54409. (shrink)

Big Data poate genera, prin inferențe, noi cunoașteri și perspective. Paradigma care rezultă din utilizarea Big Data generează noi oportunități. Un motiv de îngrijorare majoră în cazul Big Data se datorează faptului că oamenii de știință de date tind să lucreze cu date despre subiectele pe care nu le cunosc și cu care nu au fost niciodată în contact, fiind înstrăinați de produsul final al activității lor (aplicarea analizelor). Un studiu recent afirmă că ceasta poate fi motivul (...) unui fenomen cunoscut ca alienarea digitală. Big Data are influențe mari la nivel guvernamental, afectând pozitiv societatea. DOI: 10.13140/RG.2.2.27117.84961. (shrink)

In this paper we investigate if sentences presented as the result of the application of statistical models and artificial intelligence to large volumes of data – the so-called ‘Big Data’ – can be characterized as semantically true, or as quasi-true, or even if such sentences can only be characterized as probably quasi-false and, in a certain way, post-true; that is, if, in the context of Big Data, the representation of a data domain can be configured as (...) a total structure, or as a partial structure provided with a set of sentences assumed to be true, or if such representation cannot be configured as a partial structure provided with a set of sentences assumed to be true. (shrink)

Datele trebuie procesate cu instrumente avansate de colectare și analiză, pe baza unor algoritmi prestabiliți, pentru a putea obține informații relevante. Algoritmii trebuie să ia în considerare și aspecte invizibile pentru percepțiile directe. Big Data în procesele guvernamentale cresc eficiența costurilor, productivitatea și inovația. Registrele civile sunt o sursă pentru Big Data. Datele prelucrate ajută în domenii critice de dezvoltare, cum ar fi îngrijirea sănătății, ocuparea forței de muncă, productivitatea economică, criminalitatea, securitatea și gestionarea dezastrelor naturale și a (...) resurselor. DOI: 10.13140/RG.2.2.19525.50405. (shrink)

Odată cu creșterea volumului de date pe Internet, în media socială, cloud computing, dispozitive mobile și date guvernamentale, Big Data devine în același timp o amenințare și o oportunitate în ceea ce privește gestionarea și utilizarea acestor date, menținând în același timp drepturile persoanelor implicate. În fiecare zi, folosim și generăm tone de date, alimentând bazele de date ale agențiilor guvernamentale, companiilor private și chiar cetățenilor privați. Beneficiem în multe feluri de existența și utilizarea Big Data, dar trebuie (...) să ne amintim de asemenea că „nu există nimic gratuit”. Există riscuri în utilizarea Big Data, un fel de parte întunecată. Schimbările de piață pot fi obținute prin utilizarea Big Data: în timp ce acestea pot fi pozitive pentru cineva, ele pot dăuna altcuiva. Riscurile potențiale provin și de la agențiile publice. Este clar că datele mari pot aduce beneficii cetățenilor și companiilor, dar trebuie să fie echilibrate de o conștientizare sporită a „părții întunecate” a Big Data în exploatarea noilor capacități și oportunități pe care le oferă tuturor . -/- CUPRINS -/- 1. Big Data - Definiție - Caracteristici - Clasificarea datelor mari - Arhitectura - Tehnologii - Oportunități - Provocări - Critici - - Criticile paradigmei Big Data - - Criticile modelului "V" - - Criticile de noutate - - Criticile execuției Big Data - 1.1 Viața datelor mari - - Nașterea - - Copilăria - - - Educație - - - Divertisment - - Adolescența - - - Servicii online - - - Comunicații mobile - - - Servicii de streaming - - Adulți tineri - - - Căutarea de locuri de muncă - - - Servicii bancare și asigurări - - - Utilități - - - Dating online și pornografie - - Vârsta medie - - - Servicii de sănătate - - - Aplicații pentru îngrijire și aplicații pentru sănătate și fitness - - - Căutarea de locuri de muncă - - - Dating online - - Bătrânețea - - - Îngrijirea sănătății - - Moartea - 1.2 Aplicații - - Guvern - - Dezvoltare internațională - - Fabrici - - Sănătate - - Educaţie - - Mass-media - - Asigurare - - Internetul obiectelor (IoT) - - Tehnologia informației - 1.3 Studii de caz - - Guvern - - - China - - - India - - - Israel - - - Regatul Unit - - - Statele Unite ale Americii - - Cu amănuntul - - Ştiinţă - - Sport - - Tehnologie - - Activități de cercetare - - - Prelevarea de date mari - 1.4 Agenda europeană privind societatea digitală - - Cadrul juridic - - Peisajul opiniei părților interesate - 1.5 GDPR - - Etapele procesării datelor personale - - Principiile procesării datelor - - Politica de confidențialitate și transparența - - Scopurile procesării datelor - - Confidențialitate prin design și implicită - - - Paradoxul (legal) al Big Data - - Domeniul teritorial al GDPR - - Definirea datelor mari, a datelor personale și a datelor anonime - - Etapele procesării datelor cu caracter personal - - Principii de prelucrare a datelor - - Politica de confidențialitate și Transparența - - Scopuri de prelucrare a datelor - - Confidențialitate prin design și confidențialitate în mod implicit - - Paradoxul (legal) al datelor mari 2. Analiza Big Data - 2.1 Analitica - - Analitica vs. analiză - - Aplicații ale analiticii - - - Optimizarea marketingului - - - Analiza oamenilor - - - Analiza portofoliului - - - Analitica riscurilor - - - Analitica digitală - - - Analitica securității - - - Analitica software-ului - - Provocări - - Riscuri - 2.2 Analitica predictivă - - Definiție - - Procesul de analitica predictivă - - Tipuri - - - Modele predictive - - - Modele descriptive - - - Modele de decizie - - Aplicații - - - Managementul relațiilor cu clienții analitic (CRM) - - - Protecția copilului - - - Sisteme de suport decizional clinic - - - Analiticile colecțiilor - - - Vânzări încrucișate - - - Retenția clienților - - - Marketing direct - - - Detectarea fraudelor - - - Prezicerea rezultatelor deciziilor legale - - - Previziuni privind portofoliul, produsele sau economia - - - Managementul riscului de proiect - - - Acceptanța (Underwriting) - - Tehnologia și influențele big data - - Tehnici analitice - - - Tehnici de regresie - - - - Model de regresie liniară - - - - Modele de alegere discretă - - - - Regresie logistică - - - - Regresie logistică multinomială - - - - Regresie probit - - - - Logit versus probit - - - - Modele pentru serii de timp - - - - Analiza supraviețuirii sau a duratei - - - - Arborele de clasificare și regresie (CART) - - - - Funcții spline adaptive de regresie multivariate - - - Tehnici de învățare automată - - - - Rețele neuronale - - - - Perceptron multistrat (MLP) - - - - Funcții radiale de bază - - - - Mașini cu vectori de suport - - - - Bayesiene naive - - - - Vecinul k-cel mai apropiat - - - - Modelarea predictivă geospațială - - Instrumente - - - PMML - - Critici - 2.3 Analitica prescriptivă - - Istorie - 2.4 Studiul critic al datelor - - Perspective critice - - Abordare: Pragmatismul și etica discursului 3. Etica Big Data - Datele personale ale copiilor - Rolul instituțiilor - - State naționale - - Bănci - Articole relevante de știri despre etica datelor - Legislație relevantă privind etica datelor - 3.1 Principii ale eticii Big Data - - Etica în cercetare - - Conștientizarea - - Consimţământul - - Controlul - - Transparența - - Încrederea - - Proprietatea - - Supravegherea și securitatea - - Identitatea digitală - - Realitatea ajustată - - De-anonimizarea - - Inegalitatea digitală - - Confidențialitatea - - Financiar - - - Cât de mult merită datele? - - Deschidere - 3.2 Probleme etice în cercetare - - Etica cercetării pentru cercetarea socială - - Principalele aspecte etice ale cercetării sociale - - - Intimitate - - - Consimțământ informat - - - De-identificarea - - - Inegalitate – Diviziune digitală - - Integritatea cercetării - - Probleme emergente - - Aviz legal - 3.3 Aspecte filosofice - 3.4 10 reguli de bază în lucrul cu Big Data - - Concluzie Referințe Despre autor - Nicolae Sfetcu - - De același autor - - Contact Editura - MultiMedia Publishing . 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The main problems faced by scientists in working with Big Data sets, highlighting the main ethical issues, taking into account the legislation of the European Union. After a brief Introduction to Big Data, the Technology section presents specific research applications. There is an approach to the main philosophical issues in Philosophical Aspects, and Legal Aspects with specific ethical issues in the EU Regulation on the protection of natural persons with regard to the processing of personal data and (...) on the free movement of such data, and repealing Directive 95/46/EC (Data Protection Directive - General Data Protection Regulation, "GDPR"). The Ethics Issues section details the specific aspects of Big Data. After a brief section of Big Data Research, I finalize my work with the presentation of Conclusions on research ethics in working with Big Data. -/- ISBN 978-606-033-307-4 , DOI: 10.58679/MM14975 . (shrink)

Ethical decision-making frameworks assist in identifying the issues at stake in a particular setting and thinking through, in a methodical manner, the ethical issues that require consideration as well as the values that need to be considered and promoted. Decisions made about the use, sharing, and re-use of big data are complex and laden with values. This paper sets out an Ethics Framework for Big Data in Health and Research developed by a working group convened by the Science, (...) Health and Policy-relevant Ethics in Singapore Initiative. It presents the aim and rationale for this framework supported by the underlying ethical concerns that relate to all health and research contexts. It also describes a set of substantive and procedural values that can be weighed up in addressing these concerns, and a step-by-step process for identifying, considering, and resolving the ethical issues arising from big data uses in health and research. This Framework is subsequently applied in the papers published in this Special Issue. These papers each address one of six domains where big data is currently employed: openness in big data and data repositories, precision medicine and big data, real-world data to generate evidence about healthcare interventions, AI-assisted decision-making in healthcare, public-private partnerships in healthcare and research, and cross-sectoral big data. (shrink)

Digital data help data scientists and epidemiologists track and predict outbreaks of disease. Mobile phone GPS data, social media data, or other forms of information updates such as the progress of epidemics are used by epidemiologists to recognize disease spread among specific groups of people. Targeting groups as potential carriers of a disease, rather than addressing individuals as patients, risks causing harm to groups. While there are rules and obligations at the level of the individual, we (...) have to reach a stage in the development of data analytics where groups are protected as entities. This chapter offers a deeper examination of harms to the groups. (shrink)

Criticism of big data has focused on showing that more is not necessarily better, in the sense that data may lose their value when taken out of context and aggregated together. The next step is to incorporate an awareness of pitfalls for aggregation into the design of data infrastructure and institutions. A common strategy minimizes aggregation errors by increasing the precision of our conventions for identifying and classifying data. As a counterpoint, we argue that there are (...) pragmatic trade-offs between precision and ambiguity that are key to designing effective solutions for generating big data about biodiversity. We focus on the importance of theory-dependence as a source of ambiguity in taxonomic nomenclature and hence a persistent challenge for implementing a single, long-term solution to storing and accessing meaningful sets of biological specimens. We argue that ambiguity does have a positive role to play in scientific progress as a tool for efficiently symbolizing multiple aspects of taxa and mediating between conflicting hypotheses about their nature. Pursuing a deeper understanding of the trade-offs and synthesis of precision and ambiguity as virtues of scientific language and communication systems then offers a productive next step for realizing sound, big biodiversity data services. (shrink)

Les principaux problèmes rencontrés par les scientifiques qui travaillent avec des ensembles de données massives (mégadonnées, Big Data), en soulignant les principaux problèmes éthiques, tout en tenant compte de la législation de l'Union européenne. Après une brève Introduction au Big Data, la section Technologie présente les applications spécifiques de la recherche. Il suit une approche des principales questions philosophiques spécifiques dans Aspects philosophiques, et Aspects juridiques en soulignant les problèmes éthiques spécifiques du règlement de l'UE sur la protection (...) des données 2016/679 (General Data Protection Regulation, « GDPR »). La section Problèmes éthiques détaille les problèmes spécifiques générés par le big data. Après une brève section de Recherche de big data, sont présentées les Conclusions sur l’éthique de la recherche dans l’utilisation du big data. SOMMAIRE: Abstract 1. Introduction - 1.1 Définitions - 1.2 Les dimensions du big data 2. La technologie - 2.1 Applications - - 2.1.1 En recherche 3. Aspects philosophiques 4 Aspects juridiques - 4.1 RGPD (GDPR) - - Étapes du traitement des données personnelles - - Principes du traitement des données - - Politique de confidentialité et transparence - - Finalités du traitement des données - - Confidentialité par conception et confidentialité implicite - - Le paradoxe (juridique) des mégadonnées 5. Problèmes éthiques - L'éthique dans la recherche - Prise de conscience - Consentement - Contrôle - Transparence - Confiance - Propriété - Surveillance et sécurité - Identité numérique - Réalité ajustée - De-anonymisation - Inégalité numérique - Confidentialité 6. Recherche des mégadonnées Conclusions Bibliographie DOI: 10.13140/RG.2.2.10128.56328 . (shrink)

Utilizarea Big Data prezintă probleme juridice semnificative, în special din punctul de vedere al protecției datelor. Cadrul juridic existent al Uniunii Europene, bazat în special pe Directiva nr. 46/95/CE și Regulamentul general privind protecția datelor cu caracter personal, oferă o protecție corespunzătoare. Dar, pentru Big Data este necesară o strategie cuprinzătoare și globală. Evoluția în timp a fost de la dreptul de a exclude pe alții la dreptul la controlul propriilor date și, în prezent, la regândirea dreptului la (...) identitate (digitală). DOI: 10.13140/RG.2.2.22989.38889. (shrink)

Detecting quality in large unstructured datasets requires capacities far beyond the limits of human perception and communicability and, as a result, there is an emerging trend towards increasingly complex analytic solutions in data science to cope with this problem. This new trend towards analytic complexity represents a severe challenge for the principle of parsimony (Occam’s razor) in science. This review article combines insight from various domains such as physics, computational science, data engineering, and cognitive science to review the (...) specific properties of big data. Problems for detecting data quality without losing the principle of parsimony are then highlighted on the basis of specific examples. Computational building block approaches for data clustering can help to deal with large unstructured datasets in minimized computation time, and meaning can be extracted rapidly from large sets of unstructured image or video data parsimoniously through relatively simple unsupervised machine learning algorithms. Why we still massively lack in expertise for exploiting big data wisely to extract relevant information for specific tasks, recognize patterns and generate new information, or simply store and further process large amounts of sensor data is then reviewed, and examples illustrating why we need subjective views and pragmatic methods to analyze big data contents are brought forward. The review concludes on how cultural differences between East and West are likely to affect the course of big data analytics, and the development of increasingly autonomous artificial intelligence (AI) aimed at coping with the big data deluge in the near future. Keywords: big data; non-dimensionality; applied data science; paradigm shift; artificial intelligence; principle of parsimony (Occam’s razor) . (shrink)

In this paper, we will argue that, while Big Data enthusiasts imply that the analysis of massive data sets can produce serendipitous (that is, unexpected and fortunate) discoveries, the way those models are currently designed not only does not create serendipity so easily but also frequently generates zemblanitous (that is, expected and unfortunate) findings.

Big Data is a large-sized and complex dataset, which cannot be managed using traditional data processing tools. Mining process of big data is the ability to extract valuable information from these large datasets. Association rule mining is a type of data mining process, which is indented to determine interesting associations between items and to establish a set of association rules whose support is greater than a specific threshold. The classical association rules can only be extracted from (...) binary data where an item exists in a transaction, but it fails to deal effectively with quantitative attributes, through decreasing the quality of generated association rules due to sharp boundary problems. In order to overcome the drawbacks of classical association rule mining, we propose in this research a new neutrosophic association rule algorithm. The algorithm uses a new approach for generating association rules by dealing with membership, indeterminacy, and non-membership functions of items, conducting to an efficient decision-making system by considering all vague association rules. To prove the validity of the method, we compare the fuzzy mining and the neutrosophic mining. The results show that the proposed approach increases the number of generated association rules. (shrink)

Most people are completely oblivious to the danger that their medical data undergoes as soon as it goes out into the burgeoning world of big data. Medical data is financially valuable, and your sensitive data may be shared or sold by doctors, hospitals, clinical laboratories, and pharmacies—without your knowledge or consent. Medical data can also be found in your browsing history, the smartphone applications you use, data from wearables, your shopping list, and more. At (...) best, data about your health might end up in the hands of researchers on whose good will we depend to avoid abuses of power.2 Most likely, it will end up with data brokers who might sell it to a future employer, or an insurance company, or the government. At worst, your medical data may end up in the hands of criminals eager to commit extortion or identity theft. In addition to data harms related to exposure and discrimination, the collection of sensitive data by powerful corporations risks the creation of data monopolies that can dominate and condition access to health care. -/- This chapter aims to explore the challenge that big data brings to medical privacy. Section I offers a brief overview of the role of privacy in medical settings. I define privacy as having one’s personal information and one’s personal sensorial space (what I call autotopos) unaccessed. Section II discusses how the challenge of big data differs from other risks to medical privacy. Section III is about what can be done to minimise those risks. I argue that the most effective way of protecting people from suffering unfair medical consequences is by having a public universal healthcare system in which coverage is not influenced by personal data (e.g., genetic predisposition, exercise habits, eating habits, etc.). (shrink)

As opposed to a ‘one size fits all’ approach, precision medicine uses relevant biological, medical, behavioural and environmental information about a person to further personalize their healthcare. This could mean better prediction of someone’s disease risk and more effective diagnosis and treatment if they have a condition. Big data allows for far more precision and tailoring than was ever before possible by linking together diverse datasets to reveal hitherto-unknown correlations and causal pathways. But it also raises ethical issues relating (...) to the balancing of interests, viability of anonymization, familial and group implications, as well as genetic discrimination. This article analyses these issues in light of the values of public benefit, justice, harm minimization, transparency, engagement and reflexivity and applies the deliberative balancing approach found in theEthical Framework for Big Data in Health and Research to a case study on clinical genomic data sharing. Please refer to that article for an explanation of how this framework is to be used, including a full explanation of the key values involved and the balancing approach used in the case study at the end. Our discussion is meant to be of use to those involved in the practice as well as governance and oversight of precision medicine to address ethical concerns that arise in a coherent and systematic manner. (shrink)

Big Data can generate, through inferences, new knowledge and perspectives. The paradigm that results from using Big Data creates new opportunities. Big Data has great influence at the governmental level, positively affecting society. These systems can be made more efficient by applying transparency and open governance policies, such as Open Data. After developing predictive models for target audience behavior, Big Data can be used to generate early warnings for various situations. There is thus a positive (...) feedback between research and practice, with rapid discoveries taken from practice. DOI: 10.13140/RG.2.2.14677.17120 . (shrink)

The oil and gas industry is a complex data-driven industry with compute-intensive, data-intensive and business-intensive features. Cloud computing and big data have a broad application prospect in the oil and gas industry. This research aims to highlight the cloud computing and big data issues and challenges from the informatization in oil and gas industry. In this paper, the distributed cloud storage architecture and its applications for seismic data of oil and gas industry are focused on (...) first. Then,cloud desktop for oil and gas industry applications are also introduced in terms of efficiency, security and usability. Finally, big data architecture and security issues of oil and gas industry are analyzed. Cloud computing and big data architectures have advantages in many aspects, such as system scalability, reliability, and serviceability. This paper also provides a brief description for the future development of Cloud computing and big data in oil and gas industry. Cloud computing and big data can provide convenient information sharing and high quality service for oil and gas industry. (shrink)

The use of Big Data presents significant legal problems, especially in terms of data protection. The existing legal framework of the European Union based in particular on the Directive no. 46/95/EC and the General Regulation on the Protection of Personal Data provide adequate protection. But for Big Data, a comprehensive and global strategy is needed. The evolution over time was from the right to exclude others to the right to control their own data and, at (...) present, to the rethinking of the right to (digital) identity. The General Data Protection Regulation, "GDPR" (Regulation EU 2016/679) deals with data protection and privacy of persons in the European Union and the European Economic Area. DOI: 10.13140/RG.2.2.29614.46400. (shrink)

The exponential growth of social data both in volume and complexity has increasingly exposed many of the shortcomings of the conventional frequentist approach to statistics. The scientific community has called for careful usage of the approach and its inference. Meanwhile, the alternative method, Bayesian statistics, still faces considerable barriers toward a more widespread application. The bayesvl R package is an open program, designed for implementing Bayesian modeling and analysis using the Stan language’s no-U-turn (NUTS) sampler. The package combines the (...) ability to construct Bayesian network models using directed acyclic graphs (DAGs), the Markov chain Monte Carlo (MCMC) simulation technique, and the graphic capability of the ggplot2 package. As a result, it can improve the user experience and intuitive understanding when constructing and analyzing Bayesian network models. A case example is offered to illustrate the usefulness of the package for Big Data analytics and cognitive computing. (shrink)

In this paper, I critically evaluate several related, provocative claims made by proponents of data-intensive science and “Big Data” which bear on scientific methodology, especially the claim that scientists will soon no longer have any use for familiar concepts like causation and explanation. After introducing the issue, in Section 2, I elaborate on the alleged changes to scientific method that feature prominently in discussions of Big Data. In Section 3, I argue that these methodological claims are in (...) tension with a prominent account of scientific method, often called “Inference to the Best Explanation”. Later on, in Section 3, I consider an argument against IBE that will be congenial to proponents of Big Data, namely, the argument due to Roche and Sober Analysis, 73:659–668, that “explanatoriness is evidentially irrelevant.” This argument is based on Bayesianism, one of the most prominent general accounts of theory-confirmation. In Section 4, I consider some extant responses to this argument, especially that of Climenhaga Philosophy of Science, 84:359–368,. In Section 5, I argue that Roche and Sober’s argument does not show that explanatory reasoning is dispensable. In Section 6, I argue that there is good reason to think explanatory reasoning will continue to prove indispensable in scientific practice. Drawing on Cicero’s oft-neglected De Divinatione, I formulate what I call the “Ciceronian Causal-nomological Requirement”, which states, roughly, that causal-nomological knowledge is essential for relying on correlations in predictive inference. I defend a version of the CCR by appealing to the challenge of “spurious correlations,” chance correlations which we should not rely upon for predictive inference. In Section 7, I offer some concluding remarks. (shrink)

The public constitutes a major stakeholder in the debate about, and resolution of privacy and ethical The public constitutes a major stakeholder in the debate about, and resolution of privacy and ethical about Big Data research seriously and how to communicate messages designed to build trust in specific big data projects and the institution of science in general. This chapter explores the implications of various examples of engaging the public in online activities such as Wikipedia that contrast with (...) “Notice and Consent” forms and offers models for scientists to consider when approaching their potential subjects in research. Drawing from Lessig, Code and Other Laws of Cyberspace, the chapter suggests that four main regulators drive the shape of online activity: Code (or Architecture), Laws, Markets, and Norms. Specifically, scientists should adopt best practices in protecting computerized Big Data (Code), remain completely transparent about their data management practices (Law), make smart choices when deploying digital solutions that place a premium on information protection (Market), and, critically, portray themselves to the public as seriously concerned with protecting the privacy of persons and security of data (Norms). The community of Big Data users and collectors should remember that such data are not just “out there” somewhere but are the intimate details of the lives of real persons who have just as deep an interest in protecting their privacy as they do in the good work that is conducted with such data. (shrink)

In this chapter I defend the construction of inclusive, tightly governed DNA databases, as long as police can access them only for the prosecution of the most serious crimes or less serious but very high-volume offences. I deny that that the ethics of collecting and using these data sets the pattern for other kinds of policing by big data, notably predictive policing. DNA databases are primarily used for matching newly gathered biometric data with stored data. After (...) considering and disputing a number of objections to this practice, I conclude that DNA databases used in this way are ethically acceptable, if not valuable, contributions to legitimate policing. (shrink)

Le terme big data désigne l'extraction, la manipulation et l'analyse des ensembles de données trop volumineux pour être traités de manière routinière. Pour cette raison, des logiciels spéciaux sont utilisés et, dans de nombreux cas, des ordinateurs et du matériel informatiques dédiés. Généralement, ces données sont analysées de manière statistique. Les données doivent être traitées avec des outils de collecte et d'analyse avancés, basés sur des algorithmes prédéterminés, afin d'obtenir des informations pertinentes. Les algorithmes doivent également prendre en compte (...) les aspects invisibles pour les perceptions directes. DOI: 10.13140/RG.2.2.19453.51684 . (shrink)

In the last half century we have gone from storing data on 5-1/4 inch floppy diskettes to cloud and now fog computing. But one should ask why so much data is being collected. Part of the answer is simple in light of scientific projects but why is there so much data on us? Then, we ask about its “interface” through fog computing. Such questions prompt this chapter on the philosophy of big data and fog computing. After (...) some background on definitions, origins and contemporary applications, the main discussion begins with thinking about modern data collection, management, and applications from a complexity standpoint. Big data is turned into knowledge but knowledge is extrapolated from the past and used to manage the future. Yet it is questionable whether humans have the capacity to manage contemporary technological and social complexity evidenced by our world in crisis and possibly on the brink of extinction. Such calls for a new way of studying societies from a scientific point of view. We are at the center of the observation from which big data emerge and are manipulated, the overall human project being not only to create an artificial brain with an attendant mind but a society that might be able to survive what “natural” humans cannot. (shrink)

In this paper, we examine how the availability of massive quantities of data i.e., the “Big Data” phenomenon, contributes to the creation, spread, and harms of online misinformation. Specifically, we argue that a factor in the problem of online misinformation is the evolved human instinct to recognize patterns. While the pattern-recognition instinct is a crucial evolutionary adaptation, we argue that in the age of Big Data, these capacities have, unfortunately, rendered us vulnerable. Given the ways in which (...) online media outlets profit from the spread of misinformation by preying on this pattern-finding instinct, we conceptualize the problem that we identify as a morally objectionable form of “epistemic exploitation.” As we argue, the consumer of digital misinformation is often exploited by having her pattern-recognition instinct used against her. This exploitation is morally objectionable because it deprives her of an epistemic good to which she has a right. This epistemic good is the integrity of the pattern-recognition instinct itself which, we argue, is a capacity that allows us to participate in uniquely human goods. While our primary goal is to bring attention to this form of epistemic exploitation, we conclude by briefly evaluating some general solutions to the growing problem of online misinformation. (shrink)

In 1990, Gilles Deleuze publishedPostscript on the Societies of Control, an introduction to the potentially suffocating reality of the nascent control society. This thirty-year update details how Deleuze’s conception has developed from a broad speculative vision into specific economic mechanisms clustering around personal information, big data, predictive analytics, and marketing. The central claim is that today’s advancing control society coerces without prohibitions, and through incentives that are not grim but enjoyable, even euphoric because they compel individuals to obey their (...) own personal information. The article concludes by delineating two strategies for living that are as unexplored as control society itself because they are revealed and then enabled by the particular method of oppression that is control. (shrink)

Compartmentalizing our distinct personal identities is increasingly difficult in big data reality. Pictures of the person we were on past vacations resurface in employers’ Google searches; LinkedIn which exhibits our income level is increasingly used as a dating web site. Whether on vacation, at work, or seeking romance, our digital selves stream together. One result is that a perennial ethical question about personal identity has spilled out of philosophy departments and into the real world. Ought we possess one, unified (...) identity that coherently integrates the various aspects of our lives, or, incarnate deeply distinct selves suited to different occasions and contexts? At bottom, are we one, or many? The question is not only palpable today, but also urgent because if a decision is not made by us, the forces of big data and surveillance capitalism will make it for us by compelling unity. Speaking in favor of the big data tendency, Facebook’s Mark Zuckerberg promotes the ethics of an integrated identity, a single version of selfhood maintained across diverse contexts and human relationships. This essay goes in the other direction by sketching two ethical frameworks arranged to defend our compartmentalized identities, which amounts to promoting the dis-integration of our selves. One framework connects with natural law, the other with language, and both aim to create a sense of selfhood that breaks away from its own past, and from the unifying powers of big data technology. (shrink)

Etica Big Data presupune aderarea la conceptele de comportament corect și greșit în ceea ce privește datele, în special datele cu caracter personal. Etica Big Data pune accentul pe colectorii și diseminatorii de date structurate sau nestructurate. Etica Big Data este susținută, la nivelul UE, de o amplă documentație, prin care se încearcă să se găsească soluții concrete pentru maximizarea valorii Big Data fără a sacrifica drepturile fundamentale ale omului. Autoritatea Europeană pentru Protecția Datelor (AEPD) sprijină (...) dreptul la viață privată și dreptul la protecția datelor cu caracter personal în respectul demnității umane. DOI: 10.13140/RG.2.2.34749.82405. (shrink)

Abstract: Reports suggests that total amount of data generated everyday reaches 2.5 quintillion bytes [9], annual global IP traffic run rate in 2016 was 1.2 zettabytes and will reach 3.3 zettabytes by 2021 [12]. According to Gartner [25], Internet of Things excluding personal computers, tablets and smartphones will grow to 26 billion units of installed devices in year 2020. This results from penetration of digital applications which highly motivated by smart societies which can be defined as to when a (...) society deploys light and advanced computer technologies to aid provision and or supply chain value of social, cultural, governance and economic utilities for efficiency. Smart society is equipped with mobile, ubiquitous computing facilities, sensors and cyber-physical systems aims at exploring economies of scale; and to large extent it has been made possible with Internet of Things (IoT). This survey paper discusses status of big data in Internet of Things; how IoT generates big data, nature of data generated and dynamics in IoT as influenced by big data. (shrink)

Science and engineering rely on the accumulation and dissemination of knowledge to make discoveries and create new designs. Discovery-driven genome research rests on knowledge passed on via gene annotations. In response to the deluge of sequencing big data, standard annotation practice employs automated procedures that rely on majority rules. We argue this hinders progress through the generation and propagation of errors, leading investigators into blind alleys. More subtly, this inductive process discourages the discovery of novelty, which remains essential in (...) biological research and reflects the nature of biology itself. Annotation systems, rather than being repositories of facts, should be tools that support multiple modes of inference. By combining deduction, induction and abduction, investigators can generate hypotheses when accurate knowledge is extracted from model databases. A key stance is to depart from ‘the sequence tells the structure tells the function’ fallacy, placing function first. We illustrate our approach with examples of critical or unexpected pathways, using MicroScope to demonstrate how tools can be implemented following the principles we advocate. We end with a challenge to the reader. (shrink)

In Husserl’s Missing Technologies, Don Ihde urges us to think deeply and critically about the ways in which the technologies utilized in contemporary science structure the way we perceive and understand the natural world. In this paper, I argue that we ought to extend Ihde’s analysis to consider how such technologies are changing the way we perceive and understand ourselves too. For it is not only the natural or “hard” sciences which are turning to advanced technologies for help in carrying (...) out their work, but also the social and “human” sciences. One set of tools in particular is rapidly being adopted—the family of information technologies that fall under the umbrella of “big data.” As in the natural sciences, big data is giving researchers in the human sciences access to phenomena which they would otherwise be unable to experience and investigate. And like the former, the latter thereby shape the ways those scientists perceive and understand who and what we are. Looking at two case studies of big data-driven research in the human sciences, I begin in this paper to suggest how we might understand these phenomenological and hermeneutic changes. (shrink)

A huge amount of data is being generated everyday through different transactions in industries, social networking, communication systems etc. Big data is a term that represents vast volumes of high speed, complex and variable data that require advanced procedures and technologies to enable the capture, storage, management, and analysis of the data. Big data analysis is the capacity of representing useful information from these large datasets. Due to characteristics like volume, veracity, and velocity, big (...) class='Hi'>data analysis is becoming one of the most challenging research problems. Semantic analysis is method to better understand the implied or practical meaning of the input dataset. It is mostly applied with ontology to analyze content mainly in web resources. This field of research combines text analysis and Semantic Web technologies. The use semantic knowledge is to aid sentiment analysis of queries like emotion mining, popularity analysis, recommendation systems, user profiling, etc. A new method has been proposed to extract semantic relationships between different data attributes of big data which can be applied to a decision system. (shrink)

"Scientific Research in the Era of Big Data" - this book was also published in French (Mimesis) in 2019 and in Portuguese in 2022 (FIOCRUZ editors).

In the end of the course the student will be able to: • Understand the functioning of data mining tools and their contributions to managerial professions • Master the use of dynamic search tools on the open web and on the dark web. • Use the proper tools according to the objectives sought • Master the latest trends and innovations in Business Analytics • Analyze the opportunities offered in terms of data mining by artificial intelligence and IoT.