endobj [ (R) 24 (esear) 20 (c) -10 (h Division) ] TJ /Properties << � �Fn8�BG}��>�:1��Z 1 0 obj 7.5 0 0 7.5 42.5197 635.076 Tm 0 G [ (the stud) 10 (y of big data has g) 15 (ained pr) 10 (ominence among sc) -10 (holar) 10 (s in dif) 10 (f) 15 (er) 10 (ent ) ] TJ 2016 BIG DATA THE WHATS, WHYS, AND HOWS OF DATA ANALYTICS BIG DATA ANALYTICS IS MAINSTREAM. /T1_6 13 0 R (16) Tj [ (cannot be ef\037cientl) 10 (y handled b) 15 (y tr) 20 (aditional data pr) 10 (ocessing softw) 25 (ar) 10 (e ) ] TJ /BleedBox [ 0 0 595.276 841.89 ] /FontDescriptor 15 0 R /Type /Encoding /Rotate 0 /T1_0 42 0 R /CS0 80 0 R /Filter /FlateDecode 244.42 52.02 Td [ (r) 10 (ef) 15 (er) 10 (s to datasets w) 10 (hose siz) 5 (e is be) 10 (y) 10 (ond the ability of typical database ) ] TJ [ (A) -10 (par) -15 (t fr) 10 (om mark) 15 (et intellig) 15 (ence\054) 35 ( it is being applied in div) 10 (er) 10 (se ar) 10 (eas suc) -10 (h ) ] TJ Organizations are capturing, storing, and analyzing data that has high volume, [ (n) 10 (o) 10 (t) 10 ( ) 10 (e) 10 (n) 10 (t) 10 (e) 10 (r) 10 ( ) 10 (e) 10 (a) 10 (r) 10 (l) 20 (y) 10 ( ) 10 (w) 20 (o) 10 (u) 10 (l) 10 (d) 10 ( ) 10 (h) 10 (a) 20 (v) 20 (e) 10 ( ) 10 (p) 10 (e) 10 (r) 10 (f) 25 (o) 10 (r) 10 (m) 10 (e) 10 (d) 10 ( ) 10 (w) 20 (o) 10 (r) 20 (s) 10 (e) 10 ( ) 10 (i) 10 (f) 10 ( ) 10 (t) 10 (h) 10 (e) 20 (y) 10 ( ) 10 (h) 10 (a) 10 (d) 10 ( ) 10 (t) 10 (a) 10 (k) 25 (e) 10 (n) 10 ( ) 10 (t) 10 (w) 20 (o) 10 ( ) 10 (o) 10 (r) 10 ( ) 10 (m) 10 (o) 10 (r) 20 (e ) ] TJ EBA REPORT ON BIG DATA AND ADVANCED ANALYTICS 6 project, in a sort of Zethical by design [ approach that can influence considerations about governance structures. /Type /Page Introduction to Big Data Analytics Big data analytics is where advanced analytic techniques operate on big data sets. [ (F) 40 (acebook and ) 70 (T) 50 (witter\051 f) 15 (or mark) 15 (et gr) 10 (o) 15 (wth and br) 20 (and manag) 15 (ement\056) 35 ( Some ) ] TJ /T1_3 42 0 R [ (of data w) 10 (hic) -10 (h is no) 15 (w g) 15 (ener) 20 (ated in e) 10 (v) 10 (eryda) 15 (y lif) 15 (e\054) 35 ( suc) -10 (h as shopping) -30 (\054) 35 ( ) ] TJ /Subtype /Type1 [ (V) 20 (ikas Dha) 20 (w) 25 (an ) ] TJ [ (in the ar) -15 (ticle\056) 35 ( ) ] TJ /Resources << 0 -1.576 TD >> 0.1 Tc /SMask /None /Im0 85 0 R /OP false -57.83 52.02 Td /FontBBox [ -55 -236 1193 848 ] /CropBox [ 0 0 595.276 841.89 ] >> BDC >> This eBook explores the current Data Analytics industry and rounds off the top Big Data Analytics tools. 0 g /Contents 66 0 R 10 0 obj 0.55 0.19 0 0 k << /GS1 gs <> 0 -1.576 TD endobj 0 G Why Big Data needs Team Work? 0 -1.576 Td Predictive analytics is a set of advanced technologies that enable organizations to use data—both stored and real-time—to move /Font << 0 -1.576 TD endobj /T1_3 38 0 R Big Data analytics – the process of analyzing and mining Big Data – can produce operational and business knowledge at an unprecedented scale and specificity. /GS0 gs T* T* /op false /CA 1 /FontFile3 16 0 R /Type /ExtGState /T1_1 38 0 R /T1_2 34 0 R 0 -1.576 TD Amazon Web Services – Big Data Analytics Options on AWS Page 6 of 56 handle. /TrimBox [ 0 0 595.276 841.89 ] In this data science beginner's guide, you can learn data science basics to begin your data … [ <0011> ] TJ The model introduces a framework for converting data to actionable knowledge and mitigating potential risk to the /GS0 12 0 R T* /C0_0 59 0 R ( ) Tj -0.03 Tc W ( ) Tj /BleedBox [ 0 0 595.276 841.89 ] endobj 1.031 -1.576 Td /F1 7.97 Tf /Font << /Rotate 0 /SMask /None Big Data has been used for advanced analytics in many domains but hardly, if … /Widths [ 619 601 238 0 0 0 0 894 0 0 347 347 0 0 231 363 231 394 542 542 542 542 542 542 542 542 542 542 231 0 556 592 0 0 0 626 539 608 668 475 467 681 695 255 331 578 432 797 729 745 502 745 563 501 539 684 0 949 0 586 0 0 0 0 0 0 0 494 528 446 530 496 347 504 536 260 270 485 270 832 539 544 529 534 363 418 385 534 487 751 490 523 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 235 410 424 0 500 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 542 ] >> /ActualText (��\000\011) /T1_1 38 0 R /Type /Pages /Pages 1 0 R endobj [ (\050BBC) -50 (\054) 35 ( 2013\073) 35 ( Lohr) 30 (\054) 35 ( 2012\051\056) 35 ( ) ] TJ /T1_2 1 Tf 1 0 0 1 72 769.89 cm /T1_2 1 Tf 14 0 obj /ArtBox [ 0 0 595.276 841.89 ] 3 0 obj 9 Purpose of this Tutorial Two-fold objectives: Introduce the data mining researchers to the sources available and the possible challenges and techniques associated with using big data in healthcare domain. endobj >> /Type /Page /T1_2 34 0 R >> <> Well-managed, trusted data leads to trusted analytics and trusted decisions. /T1_3 1 Tf 0 -1.467 TD 510.236 0 l 5.346 0 Td Big Data analytics and the Apache Hadoop open source project are rapidly emerging as the preferred solution to address business and technology trends that are disrupting traditional data management and processing. [ (R) 41 (ef) 12 (er) 13 (ences) ] TJ endobj >> /Parent 1 0 R Q 3 0 obj /LastChar 181 Big Data & Analytics EXPECTATIVAS: DIFERENTESTIPOS DE USUARIOS Asegurar la velocidadde los análisisde datos Administrar el caos Implementar desarrollosen forma fluida Asegurar la gobernabilidad de la información Realizar nuevosy más rápidos análisispara mejorar los negocios Tomardecisionesde negocios EMC [ (use of big data f) 15 (or the monitor) 10 (ing of social media \050f) 15 (or instance Link) 15 (edIn\054) 35 ( ) ] TJ /GS1 11 0 R /AIS false ET Further research could also estimate the average treatment effect for the treated in … /T1_5 1 Tf /GS0 12 0 R /T1_5 1 Tf /Length 4833 /T1_3 1 Tf 4 0 obj /T1_6 1 Tf T* <> endobj [ (combination of data fr) 10 (om v) 25 (ar) 10 (ious sour) 20 (ces and under) 10 (standing patterns ) ] TJ /T1_5 1 Tf /Differences [ 30 /fl /fi ] [ (ef) 10 (f) 15 (ect f) 15 (or the tr) 10 (eated in the case of tw) 10 (o tr) 10 (eatment gr) 10 (oups\054) 35 ( to see if taking) -10 ( ) ] TJ 13 0 obj /MediaBox [ 0 0 595.276 841.89 ] [ (utilities and tr) 20 (af\037c manag) 15 (ement\054) 35 ( oil and g) 15 (as e) 10 (xplor) 20 (ation\054) 35 ( telecoms\054) 35 ( r) 10 (etail\054) 35 ( ) ] TJ /T1_3 42 0 R 0.1 Tc stream /T1_4 13 0 R /T1_6 25 0 R T* T* stream stream /BleedBox [ 0 0 595.276 841.89 ] 9 0 obj [ (In this ar) -15 (ticle w) 10 (e g) 15 (iv) 10 (e an intr) 10 (oduction to big data and some of its ) ] TJ >> big data analytics follow for storage, analysis and maintenance [6] enumerated some of the basic procedures generally big data analytics follow. n endobj endobj We may no longer find a clear distinction on what is a Big Data Analytics problem and what is an AI problem. [ (mark) 15 (et intellig) 15 (ence and educational r) 10 (esear) 20 (c) -10 (h\056) 35 ( Businesses\054) 35 ( lar) 15 (g) 15 (e and ) ] TJ 14 w /Type /ExtGState 57% increase in big data specialists 243% 2012 2017 BIG DATA OPPORTUNITIES Today big data analytics oer or ganisations x���Ko�@����hW�zf��EB�$i*EJ��q( ����]�V��%p`wG�|�؝!�7��t�~>�l&�o�3��Z�w��|9��W�����Ƌ>V��j]�p1��8B���#㾋ú���`G�8ʯa�G�zRh �*3�N�����gf��nO�q��@��Oqt�}���X���C���w;�:� y�i�BHЖ��(zP�4���������Q K�j��҉ >> BDC >> 0 -1.576 TD /T1_5 1 Tf /ExtGState << [ (A) -10 (pplications in the education industry mentioned in this ar) -15 (ticle include ) ] TJ /T1_4 25 0 R 15 0 obj 9 0 0 9 42.5197 441.9187 Tm Ebook. /Rotate 0 [ (tapped f) 15 (or stud) 10 (ying the perf) 15 (ormance of test tak) 15 (er) 10 (s in mor) 10 (e detail and f) 15 (or ) ] TJ BT T* -57.83 42.56 Td 04 Oracle Big Data 모델별세부사항 X6-2 Full Rack Starter Rack Elastic Configuration Compute/Storage Nodes 18 6 1 Cores 792 264 44 Memory(GB) 4,608(4.5TB) 1,536(1.5TB) 256 Raw Storage Capacity(TB) 1,728 576 96 InfiniBand Leaf Switch 2 2 InfiniBand Spine Switch 1 1 Starter Rack의 switch 사용 Ethernet Switch 1 1 /MediaBox [ 0 0 595.276 841.89 ] /T1_6 30 0 R /ToUnicode 17 0 R EMC /FontFamily (Bliss) Costs remain high, there are great inefficiencies, and, for a large percentage of the population globally, access to care T* /FontWeight 700 4.855 0 Td T* [ (to this\054) 35 ( w) 10 (e discuss ne) 10 (w f) 15 (orms of assessment suc) -10 (h as e\055assessment and ) ] TJ Big Data analytics is the process of inspecting, cleaning, transforming, and modeling Big Data to discover and communicate useful information and patterns, suggest conclusions, and support decision making. [ (until students ar) 10 (e r) 10 (ead) 10 (y to ac) -10 (hie) 10 (v) 10 (e their best possible gr) 20 (ade\054) 35 ( r) 20 (ather than ) ] TJ ( ) Tj (\057) Tj /SA true >> T* /Span << 0 0 0 1 k 0.4 0.4 0.4 rg /ArtBox [ 0 0 595.276 841.89 ] endobj By contrast, on AWS you can provision more capacity and compute in a matter of minutes, meaning that your big data applications grow and shrink as demand dictates, and your system runs as close to optimal efficiency as possible. 8.4 0 0 12 59.5275 26.6981 Tm /T1_4 13 0 R /Parent 1 0 R 21 0 0 21 42.5197 467.2573 Tm [ (combination of the data collected fr) 10 (om v) 25 (ar) 10 (ious sour) 20 (ces\054) 35 ( pr) 10 (ocessing it ) ] TJ /T1_2 34 0 R -0.01 Tc [ (Mor) 15 (g) 15 (an\054) 35 ( S\056L\056\054) 35 ( \046 Har) 20 (ding) -30 (\054) 35 ( D) 30 (\056J\056) 35 ( \0502006\051\056) 35 ( Matc) -10 (hing Estimator) 10 (s of Causal E) 46 (f) 10 (f) 15 (ects\072) 35 ( ) ] TJ 8.5 0 0 8.5 42.5197 502.2573 Tm [ (McCaf) 10 (fr) 10 (e) 10 (y) 45 (\054) 35 ( D) 30 (\056F) 60 (\056\054) 35 ( Ridg) 15 (e) 10 (w) 25 (a) 15 (y) 45 (\054) 35 ( G\056\054) 35 ( \046 Morr) 20 (al\054) 35 ( ) 70 (A\056R\056) 35 ( \0502004\051\056) 35 ( Pr) 10 (opensity scor) 10 (e estimation ) ] TJ S endobj R��(�yyN����n];����^��+ _��L�_T눑�xt�~W�>ioW>@Xϡ��ǿ���L������9_�чs��x��]�(%�R{���9�{�$� 7~��5��,��J��4��6G��,S��n�ؾ�_��H\�������p����@� 6 0 obj Big Data Analytics: Adoption and Employment Trends, 20122017 of big data recruiters say it is di cult to find people with the required skills and experience, ie. /T1_2 1 Tf 1 0 0 1 0 0 cm /MC0 << << 0.4 0.4 0.4 RG /T1_1 46 0 R /T1_0 1 Tf Creating Value with Big Data Analytics by Verhoef, Peter (Paperback) Download Creating Value with Big Data Analytics or Read Creating Value with Big Data Analytics online books in PDF, EPUB and Mobi Format. Collection of logs from many sources-In this step, the collection of data takes places from different sources. Explainability and interpretability: a model is explainable when its internal behaviour can be directly understood by humans (interpretability) or when explanations (justifications) can be 0.216 0.773 0.969 rg /T1_5 1 Tf /TrimBox [ 0 0 595.276 841.89 ] /BM /Normal /GS2 87 0 R /GS1 11 0 R EMC endobj In this tutorial, we will discuss the most fundamental concepts and methods of Big Data Analytics. /T1_5 30 0 R /ExtGState << /GS1 gs 0 0 0 0 k 0 -1.576 TD /T1_1 1 Tf [ (Sociolo) 10 (gical M) 21 (ethods \046 R) 41 (esear) 15 (c) 10 (h\054) 20 ( ) ] TJ 19 0 obj >> Hence, big data analytics is really about two things—big data and analytics—plus how the two have teamed up to BT >> /Parent 1 0 R 0 G 9 0 obj [ ( ) -28 (\072 ) ] TJ T* [ (tr) 20 (aining cour) 10 (ses in big data of) 10 (f) 15 (er) 10 (ed b) 15 (y v) 25 (ar) 10 (ious univ) 10 (er) 10 (sities ar) 10 (e mentioned ) ] TJ /T1_2 1 Tf 0 -1.576 TD Die wichtigsten davon sind: Die Datenbeschaffung aus verschiedenen Quellen mithilfe von Suchabfragen, die Optimierung und Auswertung der gewonnenen Daten sowie; die Analyse der Daten und Präsentation der Ergebnisse. T* /T1_2 1 Tf 1 0 obj /ArtBox [ 0 0 595.276 841.89 ] /ArtBox [ 0 0 595.276 841.89 ] endobj endobj Purpose – The purpose of this paper is to provide a conceptual model for the transformation of big data sets into actionable knowledge. /Rotate 0 /ProcSet [ /PDF /Text ] /Metadata 77 0 R [ (as healthcar) 10 (e and other scienti\037c r) 10 (esear) 20 (c) -10 (h\054) 35 ( comple) 10 (x man) 10 (uf) 10 (actur) 10 (ing ) ] TJ endobj Big Data Analytics lässt sich in einzelne Teilgebiete gliedern. /CA 1 >> /T1_3 42 0 R >> [ (F) 20 (inall) 10 (y) 45 (\054) 35 ( it will be inter) 10 (esting to see the impact of GCSE r) 10 (ef) 15 (orms on ) ] TJ <> /ExtGState << Big Data, Analytics & Artificial Intelligence | 4 Today’s health care system, in the United States and throughout the world, is still entering the 21st century. 18 0 obj /ProcSet [ /ImageC /ImageB /Text /PDF /ImageI ] >> Please Note: There is a membership site you can get UNLIMITED BOOKS, ALL IN … But it’s of no value unless you know how to put your big data … /T1_0 46 0 R endobj 13 0 0 13 42.5197 397.9869 Tm /TrimBox [ 0 0 595.276 841.89 ] /T1_4 1 Tf [ <0035004800560048004400550046004b> -277 <0030004400570057004800550056001d> -371 <0024> -277 <00260044005000450055004c0047004a0048> -278 <0024005600560048005600560050004800510057> -278 <005300580045004f004c004600440057004c00520051> ] TJ /T1_5 1 Tf /T1_2 34 0 R -1.031 -1.576 Td (et al) Tj /TrimBox [ 0 0 595.276 841.89 ] /CropBox [ 0 0 595.276 841.89 ] Big data and social media analytics Vikas Dhawan and Nadir Zanini Research Division not enter early would have performed worse if they had taken two or more GCSEs early. 16 0 obj Summary: This chapter gives an overview of the field big data analytics. 0 G /T1_2 1 Tf 0 G (Big data and social media analytics) Tj 7 0 obj ( ) Tj 1.134 -1.467 Td <> endobj << ( ) Tj /Im2 84 0 R 1.134 -1.467 Td /T1_2 1 Tf 5) Make intelligent, data-driven decisions. /Font << (M) Tj Top big data analytics use cases Big data can benefit every industry and every organization. /ProcSet [ /PDF /Text ] /BleedBox [ 0 0 595.276 841.89 ] (22) Tj 2 0 obj 0 0 0 1 k Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations Yichuan Wanga,⁎, LeeAnn Kungb, Terry Anthony Byrda a Raymond J. Harbert College of Business, Auburn University, 405 W. Magnolia Ave., Auburn, AL 36849, USA b Rohrer College of Business, Rowan University, 201 Mullica Hill Road, Glassboro, NJ 08028, USA Big Data Analytics Notes Pdf Download & List of Reference Books … q /CS1 78 0 R n /TT0 71 0 R [ (\0501\051\054) 35 ( 3\22660\056) ] TJ >> q During the 19th National Congress of the Chinese Communist Party in October 2017, Chinese President Xi Jinping emphasized the need to q ( ) Tj 0 0 m <> /MediaBox [ 0 0 595.276 841.89 ] /ActualText (��\000\011) 1.134 -1.467 Td q ( ) Tj [ (small\054) 35 ( ar) 10 (e implementing \050or planning to implement\051 big data str) 20 (ateg) 15 (ies\056) 35 ( ) ] TJ /TT2 74 0 R 0 -1.576 TD Enterprises can gain a competitive advantage by being early adopters of big data analytics. [ (the amount of earl) 10 (y entry) 45 (\056) 35 ( Students will still be able to sit GCSEs in ) 85 (Y) 95 (ear ) ] TJ Zunächst stellt sich bei der Big Data Analytics die Aufgabe, riesige Datenmengen unterschiedlichen … That’s not to say that SIEM vendors will provide big data distributions as part of their solution, rather most will architect big data techniques into their platforms to … Q 1.031 -1.576 Td mastering big data analytics—the use of computers to make sense of large data sets. [ 11 0 R] <>/ExtGState<>/XObject<>/ProcSet[/PDF/Text/ImageB/ImageC/ImageI] >>/MediaBox[ 0 0 612 792] /Contents 4 0 R/Group<>/Tabs/S/StructParents 0>> /ProcSet [ /PDF /Text ] [ (optimization\224) 45 ( \050Be) 10 (y) 10 (er \046 Lane) 10 (y) 45 (\054) 35 ( 2012\051\056) 35 ( ) 70 (T) 15 (he term ) 70 (\221v) 10 (olume\222) 45 ( her) 10 (e indicates ) ] TJ Q /Contents 67 0 R >> 2 Analytics: The real-world use of big data in financial services At the same time, these firms are dealing with a very diverse and demanding customer base that insists on communicating and transacting business in new and varied ways, any time of the … Analytics for big data is an emerging area, stimulated by advances in computer processing power, database technology, and tools for big data. << >> BDC on a) what big data is, b) how it can improve security analytics, and c) how it will — or won’t — integrate with SIEM. << /CropBox [ 0 0 595.276 841.89 ] Big data analytics refers to the strategy of analyzing large volumes of data, or big data. /ActualText (ers) /Type /Font /FirstChar 30 2 0 obj /GS0 12 0 R 8.468 0 Td T* (35) Tj /ExtGState << [ (Intr) 10 (oduction) ] TJ T* /T1_5 30 0 R /Contents 10 0 R /T1_5 30 0 R /Descent -236 /GS0 12 0 R [ (or high v) 25 (ar) 10 (iety inf) 15 (ormation assets that r) 10 (equir) 10 (e ne) 10 (w f) 15 (orms of pr) 10 (ocessing ) ] TJ >> /ArtBox [ 0 0 595.276 841.89 ] [ (T) 15 (he concept of big data encompasses the collection of data\054) 35 ( the ) ] TJ T* q [ (observ) 25 (ational studies f) 15 (or causal ef) 10 (f) 15 (ects\056) 35 ( ) ] TJ >> BDC /T1_2 1 Tf 12 0 obj 1.134 -1.467 Td it is not all firms, just those recruiting big data sta. Our bloggers have written several posts on this topic and how the use of data and analytics on those data is 1 0 0 0 k 0.55 0.19 0 0 k [ (of time f) 15 (or pr) 10 (o) 15 (viding mor) 10 (e accur) 20 (ate and timel) 10 (y interv) 10 (entions\056) 35 ( In addition ) ] TJ /TrimBox [ 0 0 595.276 841.89 ] [ (Ofsted \0502013\051\056) 35 ( Sc) -10 (hools\222) 45 ( use of earl) 10 (y entry to GCSE e) 10 (xaminations\056) 35 ( Its usag) 15 (e and ) ] TJ 11 0 obj ( ) Tj >> T* T* New Software and Hardware tools are emerging and disruptive. 0 -1.576 TD /Im1 85 0 R /T1_1 1 Tf /T1_1 46 0 R /T1_2 1 Tf /T1_5 1 Tf W vernment and industry are The go sources of Big Data, and providers of problems and challenges, endobj 8.25 0 0 8.25 42.5197 793.0757 Tm /GS1 11 0 R /SA true /GS1 11 0 R (TT) Tj /MediaBox [ 0 0 595.276 841.89 ] 0 0 595.275 841.89 re 1 0 0 1 42.5197 505.0053 cm [ (and using the r) 10 (esults so obtained\056) 35 ( Speci\037call) 10 (y) 45 (\054) 35 ( big data is a term used ) ] TJ <> [ (\0501\051\054) 35 ( 31\22672\056) ] TJ /T1_0 42 0 R /Flags 32 >> << 8 0 obj ���Љ��o63~�(t�����su�V�,]_�OH;��b��]��t�P�LÂ}U�FFnq���{���*F���7�4?% 0 -1.576 TD << /T1_1 1 Tf /GS1 11 0 R [ (to Gar) -15 (tner Inc) -40 (\056) 35 ( de\037nes it as ) 70 (\223Big data is high v) 10 (olume\054) 35 ( high v) 10 (elocity) 45 (\054) 35 ( and\057) ] TJ /Count 6 /T1_5 30 0 R 96.56 0 Td /ProcSet [ /PDF /Text ] 11 0 obj /Font << [ (with boosted r) 10 (egr) 10 (ession f) 15 (or e) 10 (v) 25 (aluating causal ef) 10 (f) 15 (ects in observ) 25 (ational studies\056) 35 ( ) ] TJ (RESEARCH) Tj 0 g /FontName /XSWKMI+Bliss-Bold Discover the top twenty-two use cases for big data. /Resources << endstream -1.134 -2 Td endstream /T1_2 1 Tf 1.134 -1.467 Td [ (Pr) 10 (ospects and Pitf) 10 (alls in ) 70 (T) 15 (heory and Pr) 20 (actice\056) 35 ( ) ] TJ 17 0 obj [ (applications in v) 25 (ar) 10 (ious \037elds\054) 35 ( including education\056) 35 ( ) 85 (W) 45 (e also descr) 10 (ibe the ) ] TJ << 10 0 obj /XObject << Big data analytics refers to the application of advanced data analysis techniques to datasets that are very large, diverse (including structured and unstructured data), and often arriving in real time. /GS0 12 0 R /T1_5 13 0 R 0 -1.576 TD This tutorial has been prepared for software professionals aspiring to learn the basics of Big Data Analytics. [ <0037004b004c0056> -278 <004c0056> -278 <0044> -277 <0056004c0051004a004f0048> -278 <004400550057004c0046004f0048> -278 <0049005500520050> ] TJ [ (Caliendo) 10 (\054) 35 ( M\056\054) 35 ( \046 K) 25 (opeinig) -30 (\054) 35 ( S\056) 35 ( \0502008\051\056) 35 ( Some pr) 20 (actical guidance f) 15 (or the ) ] TJ /Font << /ActualText (��\000\011) 7 0 obj /ca 1 /Resources << >> /Span << -0.01 Tc >> [ (tw) 10 (o or mor) 10 (e GCSEs earl) 10 (y is bene\037cial to these students or not\056) ] TJ ET /Subtype /Type1C /Span << /Ascent 848 6.5 0 0 6.5 42.5197 659.0757 Tm T* q /Rotate 0 I�t��T�"}NQ���zG��u�z����3s�2�J�"�-;&�~+��99�:�t��2�e�˿]'����=�M�^�g ���-�-ͭ�]������0��z� /GS0 gs BT /TT1 68 0 R endobj 0 -1.576 TD /CropBox [ 0 0 595.276 841.89 ] 0 -1.576 TD ( ) Tj 0 G 0 -1.576 TD 14 0 obj /T1_2 1 Tf [ (ar) 10 (eas of r) 10 (esear) 20 (c) -10 (h \050Eina) 10 (v \046 Le) 10 (vin\054) 35 ( 2013\073) 35 ( Ma) 15 (y) 10 (er) 30 (\055Sc) -10 (h�nber) 15 (g) 15 (er \046 Cukier) 30 (\054) 35 ( ) ] TJ The need to analyze and leverage trend data collected by businesses is one of the main drivers for Big Data analysis tools. [ (ISSUE ) -28 (18 ) ] TJ 4 Smarter Infrastructure: Thoughts on big data and analytics Big data and the use of analytics on that data We begin by discussing what big data is and the use of analytics on that data. /T1_0 1 Tf /GS0 gs EMC 0 Tc /Type /Page [ (2013\051 as w) 10 (ell as g) 15 (ener) 20 (ating inter) 10 (est fr) 10 (om the non\055academic w) 10 (orld ) ] TJ /ArtBox [ 0 0 595.276 841.89 ] The Big Data Analytics area evolves in a speed that was seldom seen in the history. /T1_4 25 0 R 0 -1.576 TD This collected data has variety of nature, some might be structured Increasingly, big data feeds today’s advanced analytics endeavors such as artificial intelligence. /T1_0 42 0 R /C0_0 59 0 R 1.619 0 Td 0 -1.576 TD 34.772 26.299 Td 15 0 obj << /Font << BT 0 Tc /Contents 89 0 R Audience. /F2 7.97 Tf [ (softw) 25 (ar) 10 (e tools to captur) 10 (e\054) 35 ( stor) 10 (e\054) 35 ( manag) 15 (e\054) 35 ( and anal) 10 (yz) 5 (e\224) 45 ( \050Man) 15 (yika ) ] TJ Big Data Analytics Tutorial in PDF - You can download the PDF of this wonderful tutorial by paying a nominal price of $9.99. T* Our research indicates that China is aggressively working toward becoming a global leader in big data analytics. -1.134 -2 Td /F1 50 0 R endobj 0 0 595.276 841.89 re 7 0 0 7 62.3666 27.6981 Tm >> /BM /Normal endobj [ (g) 15 (etting them to sit GCSEs earl) 10 (y and then r) 10 (e\055sit if the) 10 (y under) 20 (perf) 15 (orm\056) 35 ( ) ] TJ >> /T1_0 1 Tf /T1_2 1 Tf /Type /FontDescriptor /T1_2 1 Tf /Span << /T1_2 34 0 R 13 13 0 0 13 311.811 397.9869 Tm 0.1 Tc tdwi.org 5 Introduction 1 See the TDWI Best Practices Report Next Generation Data Warehouse Platforms (Q4 2009), available on tdwi.org. 1.031 -1.576 Td /T1_1 1 Tf %���� [ (in the data w) 10 (hic) -10 (h can be used f) 15 (or v) 25 (ar) 10 (ious pur) 20 (poses suc) -10 (h as impr) 10 (o) 15 (ving ) ] TJ >> [ (Gill\054) 35 ( ) 70 (T) 30 (\056) 35 ( \0502013\051\056) 35 ( Earl) 10 (y entry GCSE candidates\072) 35 ( Do the) 10 (y perf) 15 (orm to their potential\077 ) ] TJ 0.275 0.095 0 0 K Q /GS0 gs /T1_2 1 Tf �W�z��,5{U/�RyUUf�O�ʌ�m��d��� �_��geʬ�rS�ɼ�ͪ�1t+���U��m+m\뽴i�B��_��{�ު��€V���6�lJ��ҕ����L�50G,ߛ`i� }bG��ߺ�u��\��qϿ��O��ׁx �W_����i�GU��4�d�v&Y�*yJ���:��t� � /Resources << The people who work on big data analytics are called data scientist these days and we explain what it encompasses. T* /Resources << i�|nn]�7(�f�`J�йx�.hϞ�R�A9v{L��Q��fP)r/LӋ�Х��t{&��� /Parent 1 0 R [ (\0504\051\054) 35 ( 403\226425\056) ] TJ x��] �dWY��j�����^��U�LwO�������$� 1#I&�qHȘe2a��$d'd##p��z�=ǣ�]�� Fp'�McT�`���z���{_�{�=[��ܓ����}�����oU3&�EQ�V���2�\-W�*M,֦�j��`T��*�k��Ly2�.�#I��e�Xq����}���o�H�K�]�E;�lh�5 �$z����"�|�8�6桓�~�ͥ\m[R%T�?l��7����t�C�x�R[��g��r�w$ruO jn�Vk �I�5S���,���Tv�'�*�J��ZR���迅�����9�>s�O���*N�i��c�ZaW�sG�E�1W�Z5n��V2Ŗ7�[t+}�Rk�b�_���.��Z�$US�ϔĩ0�O�m�NO(�+L,��fک5�n�Z�9��j�u�c�k�� ��0��g��g��K�����Z,�\]���|�8r:���80����u{��b��UZ+�D��˞��W�\ޤu4q1�q-�U�$� F�:mP� �jSw�����|q$��'؝n�.O�n�@��ֶ��M@�-4P�L�z�Z�q��p]���>8�D���[ANg��d���"DPur�,��B}�Y�B1�Tm�Z����e�h0���b9 c��,�/V�bc�6#> W���������yhuz-���G��cي����p�[s(��sD�Zߖ�T���(a�:_(N�)�Q���;��ѮQ ��� F0���,�=�K�$����R �a,]6�Z�~6�Z���x��y��Z�g �q��p{J�E隳���K�'e�9���Z��N�B�׬�����r}�: ��.v�� /T1_2 1 Tf /CropBox [ 0 0 595.276 841.89 ] 0 0 0 1 k /OPM 1 Furthermore, its boundary with Artificial Intelligence becomes blurring. /ColorSpace << /Contents 88 0 R 4 0 obj /XHeight 473 20 0 obj /Producer (PyPDF2) [ (the combination of v) 25 (ar) 10 (ious sour) 20 (ces of inf) 15 (ormation about pupils suc) -10 (h as ) ] TJ PDF Version Quick Guide Resources Job Search Discussion. << 0 -1.576 TD endobj >> /T1_4 13 0 R We start with defining the term big data and explaining why it matters. ET 0 -1.576 TD (Nadir Zanini ) Tj 0 Tc 0 0 595.276 841.89 re -1.134 -2 Td 0 g [ (\054) 35 ( 23\22640\056) ] TJ /T1_1 38 0 R 5 0 obj [ (R) 32 (osenbaum\054) 35 ( P) 45 (\056R\056\054) 35 ( \046 Rubin\054) 35 ( D) 30 (\056) 35 ( B\056) 35 ( \0501983\051\056) 35 ( ) 70 (T) 15 (he centr) 20 (al r) 10 (ole of the pr) 10 (opensity scor) 10 (e in ) ] TJ 0.1 Tc (36) Tj >> /Kids [ 3 0 R 4 0 R 5 0 R 6 0 R 7 0 R 8 0 R ] H�lT{Tw�!��d��PI`�����R��ED-�""� �j+Z��[Ԫ��(��j��@]_���׺*�E�(w�7�� ��O��Ι?�|�{����wIB�D�$��33qZ��?h���� �٘�T��_:W�Hkl�/�m��7����� W8@�jF����L��2M�t͢�5�:�n��Y���TK�&�l�Ddf�j&�r f�F���΢�4[r̖�<1��05 �L}^�&A���,ӥ)�&�!g _�ԟ�� �B;�0�b'"� �D�(��QF��HrG��B�"��i��z�K/� 12 0 obj [ (R) 41 (esear) 15 (c) 10 (h Matter) -15 (s\072) 25 ( ) 30 (A Cambr) -10 (idge ) 30 (Assessmen) 5 (t Publication\054) ] TJ PDF - Open Access | Big Data Analytics and Its Applications >> 0 -1.576 TD >> Click Download or Read Online Button to get Access Creating Value with Big Data Analytics ebook. /Rotate 0 /T1_4 1 Tf /GS1 gs [ (banking and insur) 20 (ance\054) 35 ( def) 15 (ence and secur) 10 (ity) 45 (\056) 35 ( ) ] TJ Either way, big data analytics is how companies gain value and insights from data. Since the dawn of the computer age, people have speculated about how humans would harness technology in the future. endobj 200.52 0 Td (A) Tj (70) Tj T* [ (tr) 20 (a) 10 (v) 10 (elling) -30 (\054) 35 ( banking) -30 (\054) 35 ( man) 10 (uf) 10 (actur) 10 (ing and tr) 20 (ading) -30 (\054) 35 ( public utilities\054) 35 ( state ) ] TJ BT [ (Biometr) -10 (ika\054) ] TJ BT ARE YOU THERE YET? 1.134 -1.467 Td [ (2011\051\056) 35 ( ) 70 (A w) 10 (ell\055kno) 15 (wn model \050) -10 (kno) 15 (wn as 3V\222) 25 (s model\051 of big data attr) 10 (ibuted ) ] TJ 0 -1.576 TD /F1 7.97 Tf <> 13 0 obj >> >> BDC << /T1_2 1 Tf Introduction Organizations are able to access more data today than ever before. 0 -1.576 TD /T1_4 13 0 R [ (\0501\051\054) 35 ( 41\22655\056) ] TJ Q (\174) Tj EMC /Length 14583 /T1_3 38 0 R /Type /Page >> T* q ET /CropBox [ 0 0 595.276 841.89 ] /ProcSet [ /PDF /Text /ImageC /ImageI ] [ (industr) 10 (ies suc) -10 (h as a) 10 (viation and hea) 10 (v) 5 (y mac) -10 (hinery) 45 (\054) 35 ( impr) 10 (o) 15 (ving public ) ] TJ [ (\056\054) 35 ( ) ] TJ <> /T1_4 1 Tf EMC 8.25 0 0 8.25 311.811 375.9869 Tm Big Data Analytics Overall Goals of Big Data Analytics in Healthcare Genomic Behavioral Public Health. -1.134 -2 Td [ (impact\056) 35 ( Manc) -10 (hester\072) 35 ( Ofsted\056) ] TJ /BleedBox [ 0 0 595.276 841.89 ] 0 g [ (to enable enhanced decision making) -30 (\054) 35 ( insight disco) 15 (v) 10 (ery and pr) 10 (ocess ) ] TJ /T1_5 1 Tf /T1_0 46 0 R /ExtGState << 1.031 -1.576 Td endobj /Parent 1 0 R (Big data) Tj 6 0 obj /T1_2 34 0 R [ (in man) 15 (y ar) 10 (eas\054) 35 ( including education\056) 35 ( In simple terms it r) 10 (ef) 15 (er) 10 (s to the ) ] TJ /ActualText (a) >> (ERS) Tj T* /BaseFont /XSWKMI+Bliss-Bold -1.031 -1.576 Td endobj %PDF-1.7 /T1_1 46 0 R 8 0 obj W /ItalicAngle 0 >> /GS0 12 0 R Ten years ago, “big data analytics” was one of /Type /Page endobj [ <008b> -278 <00380026002f00280036> -278 <0015001300140017> ] TJ 8.25 0 0 8.25 42.5197 375.9869 Tm << 0.216 0.773 0.969 RG [ (to r) 10 (ealise the impor) -15 (tance of using this data f) 15 (or their gr) 10 (o) 15 (wth\056) 35 ( ) 70 (As a r) 10 (esult\054) 35 ( ) ] TJ [ (Psyc) 10 (holo) 10 (gical M) 21 (ethods\054) ] TJ endobj 0.4 0.4 0.4 RG Last updated on Sep 21, 2020. /Encoding 14 0 R /ca 1 <> << /F1 7.97 Tf Q /Span << This big data is gathered from a wide variety of sources, including social networks, videos, digital images, sensors, and sales transaction records. /Resources << [ (to the dif) 10 (f) 15 (er) 10 (ent type of str) 10 (uctur) 10 (ed or unstr) 10 (uctur) 10 (ed data suc) -10 (h as te) 10 (xt and ) ] TJ 0.4 0.4 0.4 rg Introduction to Data Science: A Beginner's Guide. >> 0 Tc /StemV 120 /ExtGState << %PDF-1.3 -1.031 -1.576 Td /T1_5 25 0 R 0 Tc >> ET >> T* /F1 7.97 Tf 0 Tc endobj 5 0 obj ˔���J� �Me� �>�-O�����+O:��S^\~��@��K(����*ȿ��4�(��j���z��߽+�7�1��n����. [ (the comple) 10 (xity of datasets and not necessar) 10 (il) 10 (y their siz) 5 (e\056) 35 ( ) 70 (\221V) 95 (ar) 10 (iety\222) 45 ( r) 10 (ef) 15 (er) 10 (s ) ] TJ /OP true 0 -2.223 TD [ (test r) 10 (ecor) 20 (ds\054) 35 ( beha) 10 (viour patterns\054) 35 ( and teac) -10 (her observ) 25 (ations o) 15 (v) 10 (er a per) 10 (iod ) ] TJ /op true [ (adaptiv) 10 (e testing w) 10 (hic) -10 (h will pr) 10 (o) 15 (vide ne) 10 (w str) 10 (eams of data w) 10 (hic) -10 (h could be ) ] TJ <> [ ( ) -28 (SUMMER ) -28 (2014) ] TJ <> We then move on to give some examples of the application area of big data analytics. [ (f) 15 (or lar) 15 (g) 15 (e databases r) 10 (equir) 10 (ing comple) 10 (x pr) 10 (ocessing and visualisation w) 10 (hic) -10 (h ) ] TJ /AIS false /T1_1 1 Tf 0 -1.576 TD [ (\050W) 15 (ikipedia\054) 35 ( 2014a\051\056) 35 ( ) 70 (A) 33 (ccor) 20 (ding to the McKinse) 10 (y Global Institute\054) 35 ( ) 70 (\223Big data ) ] TJ /OPM 1 << 0 g <> 7 0 0 7 42.5197 27.6981 Tm 0 -1.576 TD << [ (fr) 10 (om the \037r) 10 (st sitting of a GCSE will count in perf) 15 (ormance tables\056) 35 ( ) 70 (T) 15 (his is ) ] TJ /Parent 1 0 R >> BDC 16 0 obj /Type /Page [ (10\054) 35 ( but c) -10 (hang) 15 (es to accountability measur) 10 (es mean that onl) 10 (y the r) 10 (esult ) ] TJ /Span << 1 0.67 0 0.23 k /T1_3 38 0 R (9) Tj <> -1.031 -1.576 Td /TrimBox [ 0 0 595.276 841.89 ] endobj Volume 34 Article 65 Tutorial: Big Data Analytics: Concepts, Technologies, and Applications Hugh J. Watson Department of MIS, University of Georgia hwatson@uga.edu We have entered the big data era. /Type /Catalog /MediaBox [ 0 0 595.276 841.89 ] /FontStretch /Normal -0.01 Tc /Contents 58 0 R <> endobj T* 0 -1.576 TD [ (T) 75 (ec) -10 (hnolo) 10 (g) 15 (ical adv) 25 (ances in r) 10 (ecent y) 10 (ear) 10 (s ha) 10 (v) 10 (e led to a signi\037cant amount ) ] TJ /MediaBox [ 0 0 595.276 841.89 ] ( ) Tj [ <004b005700570053001d00120012005a005a005a001100460044005000450055004c0047004a00480044005600560048005600560050004800510057001100520055004a00110058004e00120055004800560048004400550046004b0010> -62 <00500044005700570048005500560012> ] TJ [ (\221Big data\222) 45 ( is f) 10 (ast becoming an ar) 10 (ea of gr) 10 (eat impor) -15 (tance f) 15 (or businesses ) ] TJ Q >> 0 -1.467 TD /F2 18 0 R >> [ (lik) 15 (el) 10 (y to lead to a f) 10 (all in earl) 10 (y entry because sc) -10 (hools ma) 15 (y w) 25 (ant to w) 25 (ait ) ] TJ /ActualText (�� \010) <> /Annots [ 57 0 R ] [ (C) 37 (ommer) 20 (cial or) 15 (g) 15 (anisations\054) 35 ( r) 10 (esear) 20 (c) -10 (h bodies and g) 15 (o) 15 (v) 10 (ernments ha) 10 (v) 10 (e star) -15 (ted ) ] TJ [ (J) 5 (our) -10 (nal of Economic S) 26 (ur) -35 (v) 15 (e) -10 (ys\054) ] TJ /BleedBox [ 0 0 595.276 841.89 ] /BaseEncoding /WinAnsiEncoding ��?�,����!8[���p,�` ��8�UC%�� }!�G=F���X�����H���)���:��,�]rЉ ��K'�;�f�&�K��u�@F��&��Z1-�ac�.�h\�Vk. endobj /T1_5 1 Tf [ (implementation of pr) 10 (opensity scor) 10 (e matc) -10 (hing) -30 (\056) 35 ( ) ] TJ [ (GCSEs earl) 10 (y) 45 (\056) 35 ( F) 49 (ur) -15 (ther r) 10 (esear) 20 (c) -10 (h could also estimate the a) 10 (v) 10 (er) 20 (ag) 15 (e tr) 10 (eatment) -10 ( ) ] TJ /CapHeight 659 <>/Metadata 1915 0 R/ViewerPreferences 1916 0 R>> T* 0 Tc • – – – ata analytics is necessarily a Big d joint effort by researchers from academic institutions, government and society and industry. endobj <> 0 g n ET >> << 0.378 0 Td (and ) Tj ( ) Tj /T1_5 1 Tf /GS1 11 0 R [ (and g) 15 (o) 15 (v) 10 (ernance\054) 35 ( spor) -15 (ts\054) 35 ( enter) -15 (tainment\054) 35 ( science\054) 35 ( education and health\056) 35 ( ) ] TJ endobj [ (monitor) 10 (ing and e) 10 (v) 25 (aluation of tests\056) ] TJ stream /T1_0 46 0 R