/T1_2 1 Tf Examples include: Application data stores, such as relational databases. /Type /Catalog The overwhelming size of big data may create additional challenges in the future, including data privacy and security risks, shortage of data professionals, and difficulties in data storage and processing. /TT2 74 0 R While the problem of working with data that exceeds the computing power or storage of a single computer is not new, the pervasiveness, scale, and value of this type of computing has greatly expanded in recent years. endobj 0 0 595.275 841.89 re [ (applications in v) 25 (ar) 10 (ious \037elds\054) 35 ( including education\056) 35 ( ) 85 (W) 45 (e also descr) 10 (ibe the ) ] TJ [ (ISSUE ) -28 (18 ) ] TJ [ <004b005700570053001d00120012005a005a005a001100460044005000450055004c0047004a00480044005600560048005600560050004800510057001100520055004a00110058004e00120055004800560048004400550046004b0010> -62 <00500044005700570048005500560012> ] TJ EMC T* /GS0 12 0 R Lower costs. /T1_3 1 Tf 0 g (35) Tj /T1_5 25 0 R ( ) Tj /T1_3 42 0 R With a variety of big data sources, sizes and speeds, data preparation can consume huge amounts of time. (36) Tj /T1_2 1 Tf /T1_2 1 Tf << 0.4 0.4 0.4 rg 0 G [ (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 0 -1.576 TD Big data can be an important tool for shaping and improving government policies, programs and services. There are large volumes of data in enterprises in different formats. 0 0 0 1 k 8.468 0 Td Two years ago I wrote an article listing 33 sources of Big Data available for free online. /Length 4833 /Type /Page /T1_1 46 0 R 0 g /T1_0 1 Tf /CS1 78 0 R /T1_2 1 Tf Tourism statistics: early adopters of big data? Traditional data integration mechanisms, such as ETL (extract, transform, and load) generally aren’t up to the task. T* Static files produced by applications, such as we… /T1_2 34 0 R 0 0 595.276 841.89 re 4.855 0 Td /T1_6 25 0 R << The transactions we execute are not fundamentally different transactions from what we would have done traditionally. >> [ (V) 20 (ikas Dha) 20 (w) 25 (an ) ] TJ [ (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 2 0 obj /Font << 0 -1.576 TD Sources of Secondary Data. T* [ (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 Collection of external data is more difficult because the data have much greater variety and the sources are much more numerous. Social Networks provide human-sourced information from: • Twitter and Facebook• Blogs and comments• Pictures: Instagram®, Flickr™, Picasa™, etc.• Videos: YouTube• Internet searches• Mobile data content (text messages)• User-generated maps• E-Mail, These organizations offer customers services or products, • Commercial transactions• Banking/stock records• E-commerce• Credit cards• Medical records, • Sensors: traffic, weather, mobile phone location, etc.• Security, surveillance videos, and images• Satellite images• Data from computer systems (logs, web logs, etc. Open-source software: OpenStack, PostGresSQL 10. [ (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 0 -1.576 TD << EMC /T1_2 34 0 R [ (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 Combining big data from multiple clouds and sources across the globe merges many regional marketing efforts into a single global marketing message strategy. T* /ActualText (�� \010) /BleedBox [ 0 0 595.276 841.89 ] 0.4 0.4 0.4 rg /GS0 12 0 R 0 -1.467 TD value. Organizations are capturing, storing, and analyzing data that has high volume, velocity, and variety and comes from a variety of new sources, including social media, machines, log files, video, text, image, RFID, and GPS. Collaborative Big Data platform concept for Big Data as a Service[34] Map function Reduce function In the Reduce function the list of Values (partialCounts) are worked on per each Key (word). >> [ (Psyc) 10 (holo) 10 (gical M) 21 (ethods\054) ] TJ How could NCHS use the sources identified in Question 1 to improve its work? … The question is: where can you find this valuable Big Data in order to create a plan of action? endobj [ (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 /Resources << /CropBox [ 0 0 595.276 841.89 ] While primary data can be collected through questionnaires, depth interview, focus group interviews, case studies, experimentation and observation; The secondary data can be obtained through. ET endobj 9 1 What is Big Data? Examples Of Big Data. EMC ), formal policy documents, and programme implementation plans and progress reports. 0 Tc /CA 1 4. endobj [ (monitor) 10 (ing and e) 10 (v) 25 (aluation of tests\056) ] TJ /MediaBox [ 0 0 595.276 841.89 ] /GS1 11 0 R Those /T1_5 1 Tf [ (cannot be ef\037cientl) 10 (y handled b) 15 (y tr) 20 (aditional data pr) 10 (ocessing softw) 25 (ar) 10 (e ) ] TJ [ (Sociolo) 10 (gical M) 21 (ethods \046 R) 41 (esear) 15 (c) 10 (h\054) 20 ( ) ] TJ The drive to maximise the value of Big Data is a key business imperative. << /ItalicAngle 0 Data Sources for Scholarly Research: Towards a Guide for Novice Researchers Timothy J. Ellis and Yair Levy Nova Southeastern University Graduate School of Computer and Information Sciences Fort Lauderdale, FL, USA ellist@nova.edu, levyy@nova.edu Abstract One of the biggest challenges the novice researcher faces is determining just where and how to start her or his research. ET For example, most of us have experience with online shopping. (ERS) Tj T* [ (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 Social Media . /T1_6 13 0 R Q /Font << >> /FontName /XSWKMI+Bliss-Bold /OPM 1 endobj [ (\0501\051\054) 35 ( 31\22672\056) ] TJ Sources of Big Data. >> BDC /Type /FontDescriptor 0.378 0 Td → Sources of Data Collection. << Here’s an example: your super-cool big data analytics looks at what item pairs people buy (say, a needle and thread) solely based on your historical data about customer behavior. 510.236 0 l [ (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 0 -1.576 TD /StemV 120 /T1_4 13 0 R /T1_5 1 Tf 96.56 0 Td -0.01 Tc /T1_2 34 0 R The data from these sources can be structured, semi-structured, or unstructured, or any combination of these varieties. /MC0 << W /Length 14583 Big data is a blanket term for the non-traditional strategies and technologies needed to gather, organize, process, and gather insights from large datasets. /BM /Normal [ (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 5 Incredible Ways Big Data Has Changed Financial Trading Forever. where big data can be seen as different from traditional data sources. ), If you are interested to learn Big Data please go through Big data training. 12 0 obj Variety of Big Data refers to structured, unstructured, and semistructured data that is gathered from multiple sources. 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