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Big Data分析是新興的商業應用趨勢，其目標是從大量的資料中快速獲得有價值的資訊，其面臨的挑戰主要在於大量與多樣化資料的蒐集、儲存、智慧型分析。本演講將介紹如何以Python程式語言搭配開放原始碼工具及新興技術來解決Big Data分析面臨的問題，包含NoSQL database, Storage, MapReduce, Information Retrieval, Natural Language Processing, Machine Learning, Visualization. 演講除介紹概念外，並以程式碼實例示範Big Data分析。

RUOK with online shopping?It's not just big tech monitoring your data, its Record companies too! Alan Cross, Canadian music writer, tells us how the evolving internet is giving labels unprecedented access to our musical tastes and how the Metaverse could make music more virtual than ever.How can you save your memories from 35mm film? Handy Andy Baryer has tips on how to do just that, what to do with your old Amazon Alexa, and he tells us why sports streaming is stirring up controversy. HEY, DO YOU LIKE PODCASTS? Why not subscribe to ours?find it on Apple, Google, Spotify & Tune In

We use the data of Wisconsin H$\alpha$ Mapper (WHAM) to test the hypothesis of whether the amplitudes and spectrum of density fluctuations measured by WHAM can be matched to the data obtained for interstellar scintillations and scattering. To do this, first of all, we adjusted the mean level of signal in the adjacent patches of the data. Then, assuming that the spectrum is Kolmogorov, we successfully matched the amplitudes of turbulence obtained from the WHAM data and the interstellar density fluctuations reported in the existing literature. As a result, we conclude that the existing data is consistent with the Kolmogorov cascade which spans from $10^6$ to $10^{17}$ $m$.

Analyzing Big Data can help corporations to im-prove their efficiency. In this work we present a new vision to derive Value from Big Data using a Semantic Hierarchical Multi-label Classification called Semantic HMC based in a non-supervised Ontology learning process. We also proposea Semantic HMC process, using scalable Machine-Learning techniques and Rule-based reasoning.

The 3He({\alpha},{\gamma})7Be reaction affects not only the production of 7Li in Big Bang nucleosynthesis, but also the fluxes of 7Be and 8B neutrinos from the Sun. This double role is exploited here to constrain the former by the latter. A number of recent experiments on 3He({\alpha},{\gamma})7Be provide precise cross section data at E = 0.5-1.0 MeV center-of-mass energy. However, there is a scarcity of precise data at Big Bang energies, 0.1-0.5 MeV, and below. This problem can be alleviated, based on precisely calibrated 7Be and 8B neutrino fluxes from the Sun that are now available, assuming the neutrino flavour oscillation framework to be correct. These fluxes and the standard solar model are used here to determine the 3He(alpha,gamma)7Be astrophysical S-factor at the solar Gamow peak, S(23+6-5 keV) = 0.548+/-0.054 keVb. This new data point is then included in a re-evaluation of the 3He({\alpha},{\gamma})7Be S-factor at Big Bang energies, following an approach recently developed for this reaction in the context of solar fusion studies. The re-evaluated S-factor curve is then used to re-determine the 3He({\alpha},{\gamma})7Be thermonuclear reaction rate at Big Bang energies. The predicted primordial lithium abundance is 7Li/H = 5.0e-10, far higher than the Spite plateau.

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¿Por qué un Podcast?, sobre Transformación Digital e Industria 4.0 Nuestro objetivo, es Educar a las nuevas generaciones de profesionales que deben de afrontar la transformación Digital en su industria o negocio, por medio de experiencias, entrevistas y charlas amenas. Mas que un podcast es una hoja de ruta para tu desarrollo profesional. Twitter: @Tdi40podcast Linkedin: linkedin.com/in/tdi-podcast-b45628200 Youtube: TDI4.0 Podcast web: https://www.tdi40.com Mail: [email protected]

There are between 150 and 200 parameters for measuring the performance of ship maintenance processes in the U.S. Navy. Despite this level of detail, budgets and timelines for performing maintenance on the Navy's fleet appear to be problematic. Making sense of what these parameters mean in terms of the overall performance of ship maintenance processes is clearly a big data problem. The current process for presenting data on the more than 150 parameters measuring ship maintenance performance costs and processes, containing billions of data points, is still done by static, cumbersome spreadsheets. The central goal of this thesis is to provide a means to aggregate voluminous maintenance data in such a way that the causal factors contributing to cost and schedule overruns can be better understood by ship maintenance leadership. Big data visualization software was examined to determine if visualization tools could improve the understanding of U.S. Navy ship maintenance by its leaders. This thesis concludes that the visualization of big data supports decision making by enabling leaders to quickly identify trends, develop a better understanding of the problem space, establish defensible baselines for monitoring activities, perform forecasting, and evaluate metrics for use.

There are between 150 and 200 parameters for measuring the performance of ship maintenance processes in the U.S. Navy. Despite this level of detail, budgets and timelines for performing maintenance on the Navy's fleet appear to be problematic. Making sense of what these parameters mean in terms of the overall performance of ship maintenance processes is clearly a big data problem. The current process for presenting data on the more than 150 parameters measuring ship maintenance performance costs and processes, containing billions of data points, is still done by static, cumbersome spreadsheets. The central goal of this thesis is to provide a means to aggregate voluminous maintenance data in such a way that the causal factors contributing to cost and schedule overruns can be better understood by ship maintenance leadership. Big data visualization software was examined to determine if visualization tools could improve the understanding of U.S. Navy ship maintenance by its leaders. This thesis concludes that the visualization of big data supports decision making by enabling leaders to quickly identify trends, develop a better understanding of the problem space, establish defensible baselines for monitoring activities, perform forecasting, and evaluate metrics for use.

Due to such disadvantages of current traffic light control methods as waste of time, waste of fuel and resources, increased air pollution, providing an intelligent traffic light control system that leads to the shortest waiting time for vehicles and pedestrians becomes so significant. Given the high priority of this issue, this paper presents an intelligent urban traffic system method based on IoT data and fog processing. Fog processing is a platform that is at the edge of the network and provides powerful services and applications for users. Compared to cloud computing, cloud computing is closer to users and therefore collects information faster and disseminates it over a network of sensors. It also helps cloud computing to perform tasks such as preprocessing and data collection. Cloud computing is a new type of distributed processing structure used for the Internet of Things. This paper proposed a method called GW-KNN. According to this method, we first collect data through the Internet of Things. Then, the preprocessing operation and extraction of effective fields in the cloud processing section are performed using the k-nearest neighbor improved machine learning algorithm. Traffic on each road is predicted in the next time slot and this information is sent for use in the fog processing layer to make traffic control decisions. The concept of Euclidean distance network with Gaussian weight was used to predict the future traffic situation and KNN model was included in the algorithm output to increase the forecasting accuracy and finally solve the problem of traffic light control. This idea was implemented and simulated using MATLAB. To get the results, the implementation was done on a computer with an i7-10750 processor and 16 GB of main memory and 1 TB of external memory. The results of the evaluations show that the proposed method has a much better performance than the previous two methods in terms of absolute mean error percentage, absolute mean error percentage of traffic forecast, and average waiting time of each vehicle.

Big Data is in the air. O'Reilly's Strata conference this week was a good focal point, with executives everywhere perking up to the potential gold they might uncover. Big Data has big upsides and downsides. The positives range from early warning of epidemics to highly accurate translation and spell-checking. One of the major downsides is that the temptation to stalk customers is addictive like crack. With David Weinberger, Doc Searls and Rick Smolan, let's discuss: What's new about big databases? What (and who) are the new superpowers? How is Big Data changing industries? analysis? people's lives? privacy? Where is this trend headed?

Penalized regression models such as the lasso have been extensively applied to analyzing high-dimensional data sets. However, due to memory limitations, existing R packages like glmnet and ncvreg are not capable of fitting lasso-type models for ultrahigh-dimensional, multi-gigabyte data sets that are increasingly seen in many areas such as genetics, genomics, biomedical imaging, and high-frequency finance. In this research, we implement an R package called biglasso that tackles this challenge. biglasso utilizes memory-mapped files to store the massive data on the disk, only reading data into memory when necessary during model fitting, and is thus able to handle out-of-core computation seamlessly. Moreover, it's equipped with newly proposed, more efficient feature screening rules, which substantially accelerate the computation. Benchmarking experiments show that our biglasso package, as compared to existing popular ones like glmnet, is much more memory- and computation-efficient. We further analyze a 31 GB real data set on a laptop with only 16 GB RAM to demonstrate the out-of-core computation capability of biglasso in analyzing massive data sets that cannot be accommodated by existing R packages.

A radio conversation on the anthropology of Big Data, featuring Christian Rudder, Astra Taylor, and Rev. Shelley Brown. 

As the term indicates the Big data it means we are work for the something big or can say something large in the Amount, Data the high volume is known as the big data.Now a Day for storing the data file in the Computer Science Engineering we are used the Hard disk, some of the storage place , these storage device may store the data in a Giga byte capability and Terabyte capability or many more, Now a day we are used the some new technology call the cloud environment. So in this work I am study the big data storing process in the Cloud environment, big data fetching from the cloud in a securer manner. So in this dissertation work I am also include the some information about the existing work and some of the algorithm that war already proposed by the researcher in the various paper. My work is based on the Attribute-Based Encryption(ABE) is promising technique to ensure the end-to-end security of big data in the cloud However, the policy updating has always been a challenging issue when ABE is used to construct access control schemes

Fred Axelgard joins us to discuss the succession in Saudi Arabia, we learn how large urban centers today, resemble those in ancient times.

With the prevalence of e-commence websites and the ease of online shopping, consumers are embracing huge amounts of various options in products. Undeniably, shopping is one of the most essential activities in our society and studying consumer's shopping behavior is important for the industry as well as sociology and psychology. Indisputable, one of the most popular e-commerce categories is clothing business. There arises the needs for analysis of popular and attractive clothing features which could further boost many emerging applications, such as clothing recommendation and advertising. In this work, we design a novel system that consists of three major components: 1) exploring and organizing a large-scale clothing dataset from a online shopping website, 2) pruning and extracting images of best-selling products in clothing item data and user transaction history, and 3) utilizing a machine learning based approach to discovering fine-grained clothing attributes as the representative and discriminative characteristics of popular clothing style elements. Through the experiments over a large-scale online clothing shopping dataset, we demonstrate the effectiveness of our proposed system, and obtain useful insights on clothing consumption trends and profitable clothing features.

Conventional sampling techniques fall short of drawing descriptive sketches of the data when the data is grossly corrupted as such corruptions break the low rank structure required for them to perform satisfactorily. In this paper, we present new sampling algorithms which can locate the informative columns in presence of severe data corruptions. In addition, we develop new scalable randomized designs of the proposed algorithms. The proposed approach is simultaneously robust to sparse corruption and outliers and substantially outperforms the state-of-the-art robust sampling algorithms as demonstrated by experiments conducted using both real and synthetic data.

Conventional sampling techniques fall short of drawing descriptive sketches of the data when the data is grossly corrupted as such corruptions break the low rank structure required for them to perform satisfactorily. In this paper, we present new sampling algorithms which can locate the informative columns in presence of severe data corruptions. In addition, we develop new scalable randomized designs of the proposed algorithms. The proposed approach is simultaneously robust to sparse corruption and outliers and substantially outperforms the state-of-the-art robust sampling algorithms as demonstrated by experiments conducted using both real and synthetic data.

Conventional sampling techniques fall short of drawing descriptive sketches of the data when the data is grossly corrupted as such corruptions break the low rank structure required for them to perform satisfactorily. In this paper, we present new sampling algorithms which can locate the informative columns in presence of severe data corruptions. In addition, we develop new scalable randomized designs of the proposed algorithms. The proposed approach is simultaneously robust to sparse corruption and outliers and substantially outperforms the state-of-the-art robust sampling algorithms as demonstrated by experiments conducted using both real and synthetic data.

We provide the first streaming algorithm for computing a provable approximation to the $k$-means of sparse Big data. Here, sparse Big Data is a set of $n$ vectors in $\mathbb{R}^d$, where each vector has $O(1)$ non-zeroes entries, and $d\geq n$. E.g., adjacency matrix of a graph, web-links, social network, document-terms, or image-features matrices. Our streaming algorithm stores at most $\log n\cdot k^{O(1)}$ input points in memory. If the stream is distributed among $M$ machines, the running time reduces by a factor of $M$, while communicating a total of $M\cdot k^{O(1)}$ (sparse) input points between the machines. % Our main technical result is a deterministic algorithm for computing a sparse $(k,\epsilon)$-coreset, which is a weighted subset of $k^{O(1)}$ input points that approximates the sum of squared distances from the $n$ input points to every $k$ centers, up to $(1\pm\epsilon)$ factor, for any given constant $\epsilon>0$. This is the first such coreset of size independent of both $d$ and $n$. Existing algorithms use coresets of size at least polynomial in $d$, or project the input points on a subspace which diminishes their sparsity, thus require memory and communication $\Omega(d)=\Omega(n)$ even for $k=2$. Experimental results real public datasets shows that our algorithm boost the performance of such given heuristics even in the off-line setting. Open code is provided for reproducibility.

This article assumes acquired the skills and expertise of a statistician in unsupervised (NMF, k-means, SVD) and supervised learning (regression, CART, random forest). What skills and knowledge do a statistician must acquire to reach the "Volume" scale of big data? After a quick overview of the different strategies available and especially of those imposed by Hadoop, the algorithms of some available learning methods are outlined in order to understand how they are adapted to the strong stresses of the Map-Reduce functionalities

This article assumes acquired the skills and expertise of a statistician in unsupervised (NMF, k-means, SVD) and supervised learning (regression, CART, random forest). What skills and knowledge do a statistician must acquire to reach the "Volume" scale of big data? After a quick overview of the different strategies available and especially of those imposed by Hadoop, the algorithms of some available learning methods are outlined in order to understand how they are adapted to the strong stresses of the Map-Reduce functionalities

From the Aspen Ideas Festival 2012 session, CEO Series: The Next Seismic Shift: Understanding Big Data. Palantir Technologies was founded in 2004 by a handful of PayPal alumni and Stanford computer scientists. Today its analytics products are widely used by the intelligence, defense, and law enforcement communities as well as some of the world's leading hedge funds and financial institutions. Join Palantir's CEO to explore the concept of Big Data and why it matters to you; the privacy, civil liberties, and security concerns raised by the next wave of Internet technologies; and what's behind the uniquely creative culture of Silicon Valley. Speakers: Elliot Gerson, Michael Lopp

Theresa Payton and Theodore Claypoole discuss their book ``Privacy in the Age of Big Data.''

Monopolios del Conocimiento, Big Data y Conocimiento Abierto, de Jesús Octavio Elizondo Martínez

One of the complexities of cyber threats that comes with digitalization is the opportunity to go farther than just the traditional methods in securitization. Big Data Analytics (BDA) means that it is to be the leading tool in improving proactive, data-driven Cybersecurity. We highlight the problems and solutions when we talk about BDA in the modern era of information security. The combination of advanced algorithms and machine learning on big data allows BDA to be able to identify minor abnormalities, such as those of malicious activity, and, consequently, helps in threat hunting, behavioral analytics, and incident response. Like in the case of anomaly detection, Artificial Intelligence (AI), predictive modeling, and attack reconstruction are among the applications. The main issues that hinder it are too much data storage, inadequate skills, and privacy protection. Prospects for the future are AI, the exchange of threat intelligence, and the analysis of the cloud. This research claims that BDA should be adopted as the main feature of a flexible and effective cybersecurity system.

Cloud storage service allows data owner to host their data in the cloud and through which provide the data access to the users. Due to the high volume and velocity of big data, it is an effective option to store big data in the cloud

Big data is an enormous data in size, collection of data that are enormous in size and can grow exponentially with time. It can be in three forms such as structured, unstructured and can be semi structured form. Big data have few characteristics which is v4 such as volume, variety, velocity and variability. Big data is use for better decision making, better viable capability and revised customer service. Big data deals with industries, to deal with the enormous data or amount of information. This paper has a detailed study of big data analytics based on challenges in IoT. Padmavati Chauhan ""Big Data Analytics Issues Based on Challenges in IoT"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-2 , February 2020, URL: https://www.ijtsrd.com/papers/ijtsrd30206.pdf Paper Url : https://www.ijtsrd.com/computer-science/other/30206/big-data-analytics-issues-based-on-challenges-in-iot/padmavati-chauhan

L. Todd Wood, founder of CD Media, former Special Operations Pilot, author, and great patriot, returns to the show today to help us get a handle on the speed of movement happening all over the world. CD Media has been running their own data polls you need to hear about! Todd also gives insight into our true enemies foreign and domestic.https://creativedestructionmedia.com/https://ltoddwood.com/https://tsarizm.com/If you want to support the show, you can donate here: http://bit.ly/cd-donateBecome a Conservative Daily member right now for massive savings on Faxblasts, discounts at Joe's Depot, and more perks like backstage time with the hosts of Conservative Daily! Use the link and sign up today! https://conservative-daily.com/forms/Step1bMake sure you Like, Comment, and Share!Text FREEDOM to 89517 to get added to our text list to receive notifications when we go Live!Please make sure you join our newsletter to receive our action alerts: https://bit.ly/joinconservativedailyGet you and your family prepared at the Brighteon Store right now and stock up on high quality storeable food, survival gear, and the cleanest supplements on the planet!https://bit.ly/3PezXDdLiberty Cigars is a Patriot owned business with an extensive line of historically themed individual cigars and cigar collections including the Commander Series, Founders Series, and the Presidents Series. All packaging is proudly made in the USA by American workers. A truly unique gift for both cigar and history lovers. All orders over $76 will receive a free Patrick Henry cigar, the perfect gift for anyone who says, \"Give Me Liberty or Give Me Death\" this holiday season. Use code REPUBLIC at www.libertycigars.comGo to IPVANISH.com/daily and use promotional code DAILY - and claim your 70% savings. That's I-P-V-A-N-I-S-H.com/daily.If you want to support Mike Lindell and our show, use promo code CD21 to get up to 66% off at https://www.mypillow.com/radiospecials or by placing your order over the phone at 800-872-0627. When you use promo code CD21, a Queen Sized MyPillow is just $29, the cheapest it has ever been!Conservative Daily is on Rumble! https://rumble.com/user/ConservativeDailyWe are now also going to be streaming on dlive! Check us out here: https://dlive.tv/ConservativeDailyClick here to donate: http://bit.ly/cd-donateSubscribe to our daily podcast at Apple Podcasts: http://bit.ly/ConservativeDailyPodcaston Google Podcasts (for Android users): https://bit.ly/CDPodcastGoogleWe are also available on Spotify! https://open.spotify.com/show/2wD8YleiBM8bu0l3ahBLDNAnd on Pandora: https://www.pandora.com/podcast/conservative-daily-podcast/PC:37034And on iHeart Radio: https://www.iheart.com/podcast/256-conservative-daily-podcast-53710765/on TuneIn: https://tunein.com/radio/Conservative-Daily-Podcast-p1350272/And on Podbean: https://conservative.podbean.com/And now also on Audible! https://www.audible.com/pd/Conservative-Daily-Podcast-Podcast/B08JJQQ4MSupport Joe Oltmann in his legal battle against Eric Coomer: https://givesendgo.com/defendjoeoltmann

Current life involves physical enjoyment, social activities and content, profile and cyber resources. Now it is easy to merge computing, networking and society with physical systems to create new revolutionary science, technical capabilities and better quality of life. That all possible through Cyber Physical Social Content and Profile Based System (CPSCPs).In this propose system, a group-centric intelligent recommender system named as GroRec, which integrates social, mobile and big data technologies to provide effective, objective and accurate recommendation services. This provides group recommendation in CPSCPs domain. In which activity oriented cluster discovery, the revision of rating information for improved accuracy and cluster preferences modelling that supports descent context mining from multiple sources. Group recommendation is based on profile and content based approach. Our main goal is make several interactions with group members by using specific technique and methods. The recommender system is economical, objective and correct. Ms. Nikita S. Mohite | Mr. H. P. Khandagale"Social Group Recommendation based on Big Data" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-3 , April 2018, URL: http://www.ijtsrd.com/papers/ijtsrd7097.pdf http://www.ijtsrd.com/computer-science/data-miining/7097/social-group-recommendation-based-on-big-data/ms-nikita-s-mohite

The Internet of things (IoT) and big data describe the movement of many industries toward a focus on data collection, communication and analysis. Tools such as radio frequency identification (RFID) are leveraged in order to maximize the potential benefits gained from the IoT and big data, connecting devices to one another and providing end users meaningful ways to interact with technology. The healthcare industry has revolutionized its approach to the IoT, with hopes of realizing cost savings, increasing efficiencies and improving quality of care. Navy medicine has a history of big data initiatives, and in the future could include even more intersection between technology and healthcare. In the adoption of new technologies, resistance to technology change is a possibility. The type and strength of resistance and its mediation is largely dependent on how leaders approach resistance and acceptance among end users. The introduction of new RFID technologies has the potential to positively impact processes such as surgical tool sterilization and patient and employee tracking. Moving into the future, the integration between healthcare and technology continues to develop at a rapid pace, and the IoT, big data and RFID technology should play an important role in the healthcare industry.

The Internet of things (IoT) and big data describe the movement of many industries toward a focus on data collection, communication and analysis. Tools such as radio frequency identification (RFID) are leveraged in order to maximize the potential benefits gained from the IoT and big data, connecting devices to one another and providing end users meaningful ways to interact with technology. The healthcare industry has revolutionized its approach to the IoT, with hopes of realizing cost savings, increasing efficiencies and improving quality of care. Navy medicine has a history of big data initiatives, and in the future could include even more intersection between technology and healthcare. In the adoption of new technologies, resistance to technology change is a possibility. The type and strength of resistance and its mediation is largely dependent on how leaders approach resistance and acceptance among end users. The introduction of new RFID technologies has the potential to positively impact processes such as surgical tool sterilization and patient and employee tracking. Moving into the future, the integration between healthcare and technology continues to develop at a rapid pace, and the IoT, big data and RFID technology should play an important role in the healthcare industry.

Recognizing emotions and human speech has always been an exciting challenge for scientists. In our work the parameterization of the vector is obtained and realized from the sentence divided into the containing emotionalinformational part and the informational part is effectively applied. The expressiveness of human speech is improved by the emotion it conveys. There are several characteristics and features of speech that differentiate it among utterances, i.e. various prosodic features like pitch, timbre, loudness and vocal tone which categorize speech into several emotions. They were supplemented by us with a new classification feature of speech, which consists in dividing a sentence into an emotionally loaded part of the sentence and a part that carries only informational load. Therefore, the sample speech is changed when it is subjected to various emotional environments. As the identification of the speaker’s emotional states can be done based on the Mel scale, MFCC is one such variant to study the emotional aspects of a speaker’s utterances. In this work, we implement a model to identify several emotional states from MFCC for two datasets, classify emotions for them on the basis of MFCC features and give the correspondent comparison of them. Overall, this work implements the classification model based on dataset minimization that is done by taking the mean of features for the improvement of the classification accuracy rate in different machine learning algorithms. In addition to the static analysis of the author's tonal portrait, which is used in particular in MFFC, we propose a new method for the dynamic analysis of the phrase in processing and studying as a new linguistic-emotional entity pronounced by the same author. Due to the ranking by the importance of the MEL scale features, we are able to parameterize the vectors coordinates be processed by the parametrized KNN method. Language recognition is a multi-level task of pattern recognition. Here acoustic signals are analyzed and structured in a hierarchy of structural elements, words, phrases and sentences. Each level of such a hierarchy may provide some temporal constants: possible word sequences or known types of pronunciation that reduce the number of recognition errors at a lower level. An analysis of voice and speech dynamics is appropriate for improving the quality of human perception and the formation of human speech by a machine and is within the capabilities of artificial intelligence. Emotion results can be widely applied in e-learning platforms, vehicle on-board systems, medicine, etc

workers in Kenya

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Traditionally, anthropologists have worked within relatively small groups of individuals; at least relative to the scope of modern big-data analytics. Traditionally, we have known our informants and participants and likely have had some personal relationship or connection with them at some level. Such research has carried with it a tradition of protection whereby anthropologists are keenly aware that we are often working in fragile parts of human societies and asking personal questions; therefore we have strived to protect the identities of our informants. However, the modern digital environment is one whereby we have access to individual’s data, sometimes deeply personal data, at the touch of a button. Given this massive amount of unique individual data, one can reverse-engineer the data in order to obtain the specific identity of the person, even if their name is changed or erased from that data. In addition, it is often the case that, when a researcher obtains social network data—even when assuming complete consent and legal transfer of the information—information concerning real individuals who have not consented to participate in the research is also transmitted. Generally, this paper argues that we have not given enough thought to such problems as online data becomes of increasing interest by anthropology. The presentation concludes that anthropologists must keep in mind a combination of “traditional” research values as well as the fact that we are in a new frontier of information as we enter the world of “big-data”. It finishes with some suggestions for participant protection. Torrent of academic paper https://anthro.web.ox.ac.uk/sites/default/files/anthro/documents/media/jaso8_1_2016_74_88.pdf

On a daily basis, we refer to the present era as the 'modern age', which can be named as the era of big data in the field of information technology field. In today's world, our daily life is moving fast and never thirsting. The fields of science, science, engineering and technology produce data at a fatal rate, leading to data from Exhibit (s) every day. Big data helps us explore and rediscover many areas where education, health and law are not limited. The main purpose of this paper is to provide in-depth analysis of healthcare in the field of healthcare using big data and analytics. The main purpose is to emphasize the use of big data that helps look back in history, but this is the time to focus on analytics to improve medicine and services. Although many big data implementations are intended for home development, this proposed implementation is intended to propose a wider range using Hadoop, which is intended only as the tip of the iceberg. The focus of this paper is not just limited to the correction and analysis of data; It also focuses on strengths and drawbacks compared to traditional techniques available

The development of information technology has greatly impacted various industries in society, and the practice of accounting, which provides financial information systems, has also been greatly impacted by big data. In order to help students better cope with the challenges of the big data era, the training mode of accounting professionals in colleges and universities needs to be changed accordingly. Based on the background of big data, this project, on the one hand, makes a detailed investigation and research on the current undergraduate accounting talent cultivation mode in Beijing universities, on the other hand, conducts a survey on the changes of enterprises employment demand, summarizes the talent markets expectation cognition on the ability elements and teaching mode of accounting students, and accordingly makes suggestions on the transformation of undergraduate accounting talent cultivation mode in universities under the background of big data. Yangyanzhen | Zhangle | Wang Mei Ying "A Study on the Difference between Undergraduate Accounting Talent Training and Social Needs in the Context of Big Data" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-1 , December 2021, URL: https://www.ijtsrd.com/papers/ijtsrd47846.pdf Paper URL: https://www.ijtsrd.com/management/other/47846/a-study-on-the-difference-between-undergraduate-accounting-talent-training-and-social-needs-in-the-context-of-big-data/yangyanzhen

Millie Weaver presents " Big Data Is Watching ", an eye opening documentary about the growing dangers and vulnerabilities in the modern age of technological advancements in artificial intelligence and data surveillance. You will not be the same after watching this documentary!

International Journal of Emerging Technologies and Advanced Applications

The use of digital technologies and computational approaches play a decisive role in smart, sustainable and inclusive development in health process. Data protection and information security are of the utmost importance to maintain public trust in digital health services. On the other hand for patients, digitization results in higher-level services, easily accessible to actively manage their health and well-being. Simultaneously, through the utilization of big data analytics, early diagnosis and prevention is supported for their benefit. Neurodegenerative diseases are a broad range of pathological conditions in most brain-related disorders that lead to progressive degeneration and/or death of neuronal cells. Computational approaches in neurodegeneration through machine learning techniques can be used for identifying features of importance in high dimensional data and provide constructive models of potential biomarkers, defining a new field of translation research in the new digital era. Based on the epigenetic history for each patient separately as well as the existence of underlying diseases, the presence of digital solutions can form an individualized strategy in terms of the personalized healthcare. 

Introduction:  Big data is a set of data that, with its special features, cannot be stored, managed and processed by conventional software systems. Cloud computing is a collection of virtualized resources with easy access and usability. Cloud-based storage technology is able to effectively manage big data. The purpose of this study is to determine the current status of libraries̓ big data management based on cloud computing and its Component ranking in the libraries of first level universities in Iran Methodology:  The present study is an applied and in terms of nature, method and control of variables is a descriptive research. The statistical population includes all librarians working in the libraries of first level universities in Iran, 520 people. The questionnaire was provided to the respondents electronically and a total of 393 questionnaires were received and analyzed. Data collection tool is a researcher-made questionnaire that examines previous studies of six main components affecting the libraries̓ big data management based on cloud computing in the libraries of first level universities in Iran that including manpower, organization, infrastructure, economic issues, culture and data management was identified, which was confirmed through confirmatory factor analysis. To analyze data from descriptive and inferential statistics with software Spss 22 and Amos 22 were used. Findings:  The current situation of library data management based on cloud computing in the libraries of Iran's top-level universities was considered unfavorable from the librarians' point of view, with an average of 23.582 and a standard deviation of 7.655 equal to -0.41730. Ranking the components of the current situation from strength to weakness, respectively 1. Manpower With a factor loading rate of 0.64, 2. Economic issues with a factor loading rate of0.60, 3. Infrastructure With a factor loading rate of0.59, 4. Data Management With a factor loading rate of0.58, 5. Culture With a factor loading rate of 0.57 6. Organizations With a factor loading rate of 0.52 are located. Conclusion:  Librarians are practically dealing with big data because of their day-to-day dealings with very large volumes of data. Matching the average budget of these libraries with the current needs and providing the necessary funds for the digitization of resources and the existence of appropriate legal hardware and software infrastructure, it can pave the way for the necessary steps to take advantage of cloud-based storage services. Most librarians, while understanding the culture of free access to information, understand the importance of providing analyzed data to clients, but clients' perception of receiving such data is moderate. Low awareness and understanding of officials and unwillingness to interact and cooperate with organizations active in this field, does not provide a stable environment to work with big data and provide services based on them.

More and more, the decisions that rule our lives are made by algorithms. From the news we see in our feeds, to whether or not we qualify for a mortgage, to the rates we pay for health insurance. And while there are demonstrable biases against marginalized communities caused by algorithms, some say the machines are innocent — they’re just doing math. But as more systems rely on big data and artificial intelligence, bias and discrimination is rampant. How do we protect ourselves when we’re at the mercy of the few who create the algorithms? Should there be a universal set of ethical standards? Is regulation needed? How can we be more savvy about the models that increasingly, and imperceptibly, rule our lives? Speakers: Jason Pontin, Surya Mattu, Virginia Eubanks

Economists and social scientists are increasingly making use of web data to address socio-economic issues and to integrate existing sources of information. The data produced by online platforms and websites could produce a lot of useful and multidimensional information with a variety of potential applications in socio-economic analysis. In this respect, with the internet growth and knowledge, many aspects of job search have been transformed due to the availability of online tools for job searching, candidate searching and job matching. In European countries there is growing interest in designing and implementing real labour market information system applications for internet labour market data in order to support policy design and evaluation through evidence-based decision-making. The analysis of labour market web data could provide useful information for policy-makers to define labour market strategies as big data, jointly with official statistics, support policy makers in a pressing policy question namely “How to tackle the mismatch between jobs and skills?”. In this regard, the topic of skills gap, how to measure it and how to bridge it with education and continuous training have been tackled by using the big data collection, such as the Cedefop (European Center for the Development of Vocational Training) initiative and the Wollybi Project (made by Burning Glass). In this framework, this contribution focuses on the issues arising from the use (and the usefulness) of on-line job vacancy data to analyse the Italian labour market by using the Wollybi data available for the years 2019 and 2020. Furthermore, the availability of data for the year 2020, will allow us to evaluate whether there has been an impact of COVID19 in terms of needed skills and required occupations in the online job vacancies.

Algorithms, data, advanced technology: In the last few years, the data stream flowing through the internet has turned into a tsunami. Ninety percent of the information sitting on the world’s servers was created in the last two years. This total digitization opens up completely new possibilities. Suddenly, our entire lives can be modelled mathematically – and become predictable. We show how predictive analytics is being used today and ask how our lives will change if our future becomes computable. --- SUBSCRIBE for more amazing stories, including free FULL documentaries. At Java Films we have an incredible library of award-winning documentaries: from world-leading investigations to true crime and history, we have something for everyone! Click the SUBSCRIBE button and make sure to set NOTIFICATIONS to stay updated with all new content! Sign up to the Java Films Clubs for exclusive deals and discounts for amazing documentaries - find out first about FREE FULL docs: http://eepurl.com/hhNC69 Head to https://www.watchjavafilms.tv/ to check out our catalogue of documentaries available on Demand. You can also find our docs on : Amazon Prime Video: https://www.amazon.com/v/javafilms Vimeo On Demand: https://vimeo.com/javafilms/vod_pages Like us on Facebook: https://www.facebook.com/JavaFilms/ Follow us on Twitter: https://twitter.com/java_films Follow us on Instagram: https://www.instagram.com/javafilms.tv/

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

For more information, including links to the scientific evidence base for the statements in this video, as well as further resources: http://medicatingnormal.com/ Robert Whitaker on Wikipedia: https://bit.ly/33tgN4n Robert Whitaker’s books provide much more detail about this subject, including citations to the scientific research. His book “Anatomy of an Epidemic: Magic Bullets, Psychiatric Drugs, and the Astonishing Rise of Mental Illness in America” is available here: https://amzn.to/2OO45tI A link to summaries and the original scientific articles of the Xanax study discussed here: https://medicatingnormal.com/benzodiazepines-anti-anxiety-meds/ Xanax is the brand name of Alprazolam. Medicating Normal on Facebook: https://www.facebook.com/pg/medicatingnormalfilm/ Medicating Normal on Twitter: https://twitter.com/medicatingnorm1?lang=en Donate: https://medicatingnormal.com/donate/ Note: This video does not constitute medical advice. Stopping psychiatric drugs, especially abruptly, can be dangerous, as withdrawal effects may be severe, disabling or even life-threatening. Music: http://www.bensound.com/royalty-free-music Video edited by Daniel Mackler

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This article is from Biology Direct , volume 7 . Abstract Abstract: As advances in life sciences and information technology bring profound influences on bioinformatics due to its interdisciplinary nature, bioinformatics is experiencing a new leap-forward from in-house computing infrastructure into utility-supplied cloud computing delivered over the Internet, in order to handle the vast quantities of biological data generated by high-throughput experimental technologies. Albeit relatively new, cloud computing promises to address big data storage and analysis issues in the bioinformatics field. Here we review extant cloud-based services in bioinformatics, classify them into Data as a Service (DaaS), Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS), and present our perspectives on the adoption of cloud computing in bioinformatics. Reviewers: This article was reviewed by Frank Eisenhaber, Igor Zhulin, and Sandor Pongor.

The integration of big data into healthcare has transformed the approach to analyzing patient outcomes, enabling unprecedented insights into medical treatments and health service efficacy. This paper examines the application of big data technologies in healthcare, focusing on their ability to collect, process, and analyze vast datasets generated from electronic health records (EHRs), clinical trials, and patient generated data. Analyzing patient outcomes offers actionable intelligence for improving care delivery, driving value-based healthcare initiatives, and informing policy changes. We delve into the methodologies and tools used for data collection and analysis, illustrate practical applications through case studies, and discuss challenges such as data privacy, integration, and the need for multidisciplinary collaboration. The paper concludes with future directions, emphasizing the transformative potential of advanced analytics and machine learning in creating a patient-centered healthcare system.