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iSchool, Data Science, and DaaS (Data as a Serv...

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February 23, 2016

iSchool, Data Science, and DaaS (Data as aย Service)

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February 23, 2016
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  1. iSchool, Data Science and DaaS (Data as a Service) ์˜ค

    ์‚ผ ๊ท  ์„ฑ๊ท ๊ด€๋Œ€ํ•™๊ต, iSchool & Data Science ๊ต์ˆ˜, ํ•™์ˆ ์ •๋ณด๊ด€์žฅ iSchool Caucus Chair-Elect ISO/IEC JTC1/SC34(์ „์ž๋ฌธ์„œ ๋ฐ ์ฒ˜๋ฆฌ์–ธ์–ด) ๊ตญ์ œ์˜์žฅ Dublin Core, Governing Board Member [email protected]
  2. โ€ขํ˜์‹ ์ ์ธ ๊ตญ์ œ์ •๋ณด๋Œ€ํ•™ ํ˜‘์˜ํšŒ โ€“ ์—„๊ฒฉํ•œ ์‹ฌ์‚ฌ๋ฅผ ํ†ตํ•ด ํšŒ์›๊ต ์„ ์ • โ€“

    ํ˜„์žฌ 65๊ฐœ ํšŒ์› ๋Œ€ํ•™ โ€“ ์•„์‹œ์•„ 11๊ฐœ ๋Œ€ํ•™ (ํ•œ๊ตญ 3๊ฐœ ๋Œ€ํ•™: ์„ฑ๋Œ€, ์„œ์šธ๋Œ€, ์—ฐ๋Œ€) iSchool (๊ตญ์ œ์ •๋ณด๋Œ€ํ•™ํ˜‘์˜ํšŒ)
  3. iSchool Focus โ€ข ์ •๋ณด, ๊ธฐ์ˆ , ์‚ฌ๋žŒ ๊ฐ„์˜ ์ƒํ˜ธ์ž‘์šฉ์— ๊ด€ํ•œ ์—ฐ๊ตฌ

    โ€ข ๋ชจ๋“  ์ผ์—์„œ ์ •๋ณด์˜ ์—ญํ• ์„ ์ดํ•ดํ•˜๊ณ  ์Šต๋“ํ•˜๋ ค๋Š” ๋…ธ๋ ฅ โ€ข ๊ณผํ•™, ๋น„์ฆˆ๋‹ˆ์Šค, ๊ต์œก ๋ฐ ๋ฌธํ™”์˜ ๋ฐœ์ „์— ์ •๋ณด ์ „๋ฌธ์„ฑ์ด ํ•„์ˆ˜ โ€ข ์ •๋ณด๊ธฐ์ˆ ๊ณผ ์‘์šฉ, ์ •๋ณด์ด์šฉ ๋ฐ ์ด์šฉ์ž์— ๋Œ€ํ•œ ์ „๋ฌธ์„ฑ
  4. iSchool Governance Structure โ€ข Currently Two Tier Structure โ€“iCaucus member

    schools (currently 25): Due $5000 per year โ€“iConsortium member schools (currently 40): Due $500 per year โ€“Elected iCaucus member schools (currently 4) โ€ข Trying to change โ€“Tier 1($5000), Tier 2($1000), Tier 3($500), Associates($300) โ€“Cooporates (Affiliate, Associate, and Sponsor) โ€ข iSchool Executive Committee Members โ€“Ron Larson (iCaucus Chair), University of Pittsburgh, USA โ€“Sam Oh (iCaucus Chair-Elect), Sungkyunkwan University (SKKU) Korea โ€“Michael Seadle (iCaucus Past-Chair), Humbolt University, Germany โ€“Tom Finholt (Treasurer), University of Michigan, USA โ€“Gobinda Choudhury (Elected), Northumbria Universtiy, UK
  5. iCaucus Member Schools (US:20, EU:3, Asia:2 = 25) โ€ข University

    of California, Berkeley, School of Information โ€ข University of California, Irvine, Donald Bren School of Information and Computer Sciences โ€ข University of California, Los Angeles, Graduate School of Education and Information Studies โ€ข Carnegie Mellon University, School of Information Systems and Management, Heinz College โ€ข Drexel University, College of Information Science and Technology โ€ข Florida State University, College of Communication and Information โ€ข Georgia Institute of Technology, College of Computing โ€ข Humboldt-Universitat zu Berlin, School of Library and Information Science (Europe) โ€ข University of Illinois, Graduate School of Library and Information Science โ€ข Indiana University, School of Informatics and Computing โ€ข University of Maryland, College of Information Studies โ€ข University of Sheffield: Information School (Europe) โ€ข University of Michigan, School of Information โ€ข University of North Carolina, of Information and Library Science โ€ข The Pennsylvania State University, College of Information Sciences and Technology โ€ข University of Pittsburgh, School of Information Sciences โ€ข University of Copenhagen, Royal School of Library and Information Science (Europe) โ€ข Rutgers, the State University of New Jersey, School of Communication and Information โ€ข Singapore Management University, School of Information Systems (Asia) โ€ข Syracuse University, School of Information Studies โ€ข University of North Texas, College of Information โ€ข University of Texas, Austin, School of Information โ€ข University of Toronto, Faculty of Information โ€ข University of Washington, Information School โ€ข Wuhan University, School of Information Management (Asia)
  6. iConsortium Member Schools (40) โ€ข Charles Sturt University: School of

    Information Studies โ€ข Michigan State University: Department of Media and Information โ€ข Nanjing University: School of Information Management โ€ข Northumbria University โ€ข NOVA University of Lisbon: School of Statistics and Information Management โ€ข Open University of Catalonia: Information and Communications Science Studies โ€ข Polytechnic University of Valencia: School of Informatics โ€ข Seoul National University, Korea: School of Convergence Science and Technology โ€ข Sungkyunkwan University, Seoul, Korea: LIS & Data Science โ€ข Sun Yat-sen University, China. School of Information Management (2014) โ€ข TรฉlรฉcomBretagne: Department of Logic Uses, Social Sciences and Information โ€ข University College Dublin: School of Information and Library Studies โ€ข University College London: Department of Information Studies โ€ข University of Amsterdam: Graduate School of Humanities, Archives and Information Studies โ€ข University of Boras: The Swedish School of Library and Information Science โ€ข University of British Columbia: School of Library, Archival and Information Studies โ€ข University College: Oslo and Akershus: Department of Archivistics, Library and Information Science โ€ข University of Glasgow: Humanities Advanced Technology and Information Institute โ€ข University of Kentucky: College of Communications and Information Studies โ€ข University of Maryland, Baltimore County: Department of Information Systems โ€ข University of Melbourne: Melbourne School of Information โ€ข University of Missouri: School of Information Science and Learning Technologies โ€ข University of North Texas: College of Information โ€ข University of Porto: Faculty of Engineering in cooperation with the Faculty of Arts โ€ข University of Siegen: Institute for Media Research โ€ข University of South Australia: School of Computer and Information Science โ€ข University of Strathclyde: Department of Computer and Information Science โ€ข University of Tampere: School of Information Sciences โ€ข University of Tennessee, Knoxville: School of Information Sciences โ€ข University of Tsukuba: Graduate School of Library, Information and Media Studies โ€ข University of Wisconsin, Madison: School of Library and Information Studies โ€ข University of Wisconsin, Milwaukee: School of Information Studies โ€ข Yonsei University, Seoul, Korea, LIS (2014)
  7. Annual iConference โ€ข ๋งค๋…„ 2โ€“ 3์›”์— ์—ด๋ฆผ รผ์ฃผ๋กœ ๋ฏธ๊ตญ์—์„œ ๊ฐœ์ตœ๋˜์—ˆ๊ณ ,

    2014๋…„๋„ ์ฒ˜์Œ์œผ๋กœ ๋…์ผ Berlin ์†Œ์žฌ Humboldt University iSchool์—์„œ ๊ฐœ์ตœ๋จ รผ๋งค๋…„ iConference ๊ธฐ๊ฐ„ ์ค‘์— ์—ด๋ฆฌ๋Š” iCaucus ํšŒ์˜์— ๋ชจ๋“  iSchool ํ•™์žฅ์˜ ์ฐธ์—ฌ ์˜๋ฌด์ , ๋Œ€๋ฆฌ์ฐธ์„ ๋ถˆ๊ฐ€. โ€ข ์ฐจ๊ธฐ ํšŒ์˜ ์ผ์ • รผ2016 3/20-3/23 Drexel University, Philadelphia, USA. รผ2017 Wuhan/SKKU ๊ณต๋™์ฃผ์ตœ ํ™•์ • (Venue: Wuhan): ์•„์‹œ์•„ ์ตœ์ดˆ
  8. 1. Administrative Information 2. Research Information 3. Profile iSchool ๊ฐ€์ž…

    ์ง€์›์„œ ์ฃผ์š”์งˆ๋ฌธ
  9. โ€ข What is the official name of the โ€œschoolโ€ (unit)

    and the university? โ€ข Who is the head of the school? โ€ข What is the title of the person who heads the unit? โ€ข To whom does that person report? โ€ข How many permanent professors does the school have? โ€ข How many other teaching staff does the school have on the regular payroll? โ€ข How many adjuncts (i.e., lecturers paid per class)? โ€ข How many external professors are actively involved with the school? โ€ข How large is the bachelors program (if any)? โ€ข How large is the maters program? Q1. Administrative Information
  10. โ€ข When was the Ph.D. program established? โ€“How many students

    have received Ph.D. in the last 3 years (by year)? โ€“Please list 5 top student projects from your school within the last few years (recently completed or in process) โ€ข How much research money has the school received in the last 3 years (by year)? โ€“Where does funding typically come from? โ€ข Please list 5 significant research areas in your school with 2-3 sentences to describe each. โ€ข Where do faculty typically publish? (3 or 4 examples) โ€ข Please list five conferences that your faculty or doctoral students attend regularly. Q2. Research Information
  11. โ€ขPlease describe briefly why you want to join the iSchoolsand

    what you feel your institution can contribute towards establishing and advancing the identity of iSchoolsand their distinction from other professional disciplines. Q3. Profile
  12. โ€ขA success story of iSchool program in USA โ€“ UW

    iSchool iAffiliate Program โ€“ Membership fee: $2,500, $5,000, or $10,000 per year โ€“ Membership benefit: privilege to interview graduates one month before other companies or organizations iAffiliate Program: UW iSchool
  13. What is Data Science? โ€ข A discipline that incorporates statistics,

    data visualization, computer programming, data mining, machine learning and database engineering to solve complex problems ร˜ ๋ฐ์ดํ„ฐ๊ณผํ•™์€ ํ†ต๊ณ„, ๋ฐ์ดํ„ฐ์‹œ๊ฐํ™”, ์ปดํ“จํ„ฐ ํ”„๋กœ๊ทธ๋ž˜๋ฐ, ๊ธฐ๊ณ„ํ•™์Šต, ๋ฐ์ดํ„ฐ๊ณตํ•™์„ ํ™œ์šฉํ•˜์—ฌ ๋ณต์žกํ•œ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๋Š” ํ•™๋ฌธ (Source: Data Scientist - The definitive guide to becoming a data scientist) โ€ข Data science is the extraction of knowledge from data ร˜ ๋ฐ์ดํ„ฐ๊ณผํ•™์€ ๋ฐ์ดํ„ฐ์—์„œ ์ง€์‹์„ ์ถ”์ถœํ•˜๋Š” ๊ฒƒ์— ๊ด€ํ•œ ํ•™๋ฌธ (Source: Wikipedia)
  14. โ€ขSKKU received 5 years of government funding for BS in

    Data Science starting Spring 2015. โ€“ ์„ฑ๊ท ๊ด€๋Œ€ํ•™๊ต ๋ฌธํ—Œ์ •๋ณดํ•™๊ณผ๋Š” CK์‚ฌ์—…์— ์„ ์ •๋˜์–ด 2015๋…„ ๋ด„ํ•™๊ธฐ๋ถ€ํ„ฐ ๋ฐ์ดํ„ฐ๊ณผํ•™ ํ•™๋ถ€ ์ „๊ณต์„ ์‹œ์ž‘ํ•˜์˜€์Œ. Funding for Data Science
  15. โ€ข iSchool โ€“Dr. Young Man Ko (Research Data Analytics) โ€“Dr.

    Sam Gyun Oh (Data Modeling & Data Analytics) โ€“Dr. Wonsik Jeff Shim (Data Visualization) โ€“Dr. Yongjung Lee (Health Informatics) โ€“Currently searching for Data Science faculty โ€ข Computer Education โ€“Dr. Sungjin Ahn & Dr. Jaehyun Kim (Computer Network & Security) โ€ข Statistics โ€“Dr. Byungtae Seo & Jongsun Hong (Statistical Modeling) โ€ข Consumer Economics โ€“Dr. Sungrim Lee (Consumer Pattern Analytics) โ€ข Linguistics โ€“Dr. Myungwon Choe (Linguistics) & Dr. Moonpyo Hong (Computational Linguistics) โ€ข Business โ€“Dr. Sangman Han (Business Analytics) and Jongwook Kim (MIS) โ€ข Other partners will be added as needed SKKU DS Faculty
  16. โ€ข DS Core (Choose 5) โ€“Introduction to Data Science โ€“Programming

    in Python and JavaScript โ€“Programming in R โ€“Data Visualization โ€“Data Modeling โ€“Statistical Data Mining โ€“Business Intelligence โ€“Visual Programming โ€“Data Curation โ€ข DS Lab (Choose 4) โ€“Research Data Analytics โ€“Social Data Analytics โ€“Big Data Analytics โ€“Health Data Analytics โ€“Data Mining โ€“Machine Learning Data Science @ SKKU iSchool โ€ข DS General (Choose 3) โ€ข Digital Humanities โ€ข Semantic System โ€ข Statistical Modeling โ€ข Information Security and Ethics โ€ข Information Networks โ€ข Computer Graphics โ€ข Multivariate Analytics
  17. DaaS (Data as a Service)์˜ ๊ฐœ๋… โ€œํด๋ผ์šฐ๋“œ ์ปดํ“จํŒ…(Cloud Computing) ํ™˜๊ฒฝ์„

    ๊ธฐ๋ฐ˜์œผ๋กœ / ๋ฒ”์šฉ์˜ ์›น๊ธฐ์ˆ ์„ ํ™œ์šฉํ•˜์—ฌ / ๋ฐ์ดํ„ฐ๋ฅผ ํ™œ์šฉํ•˜๊ณ ์ž ํ•˜๋Š” ์ด์šฉ์ž ๋˜๋Š” ๊ธฐ๊ด€์—๊ฒŒ / ์„œ๋น„์Šค๋กœ์„œ(as a Service) ๋ฐ์ด ํ„ฐ๋ฅผ ์ œ๊ณตํ•˜๋Š” ๊ฒƒโ€ (๊ณผ๊ธˆ ๋˜๋Š” ๋น„๊ณผ๊ธˆ ๋ฐฉ์‹)
  18. DaaS๊ฐ€ ์ค‘์š”ํ•ด์ง€๋Š” ํ™˜๊ฒฝ์  ์š”์ธ ๊ตฌ๋ถ„ ๋‚ด์šฉ Quantification โ€ข ๋ง‰๋Œ€ํ•œ ์–‘์˜

    ๋ฐ์ดํ„ฐ ์ƒ์„ฑ โ€ข IoT(Internet of Things)๋ฅผ ํ†ตํ•œ ๋น…๋ฐ์ดํ„ฐ์˜ ์ƒ์„ฑ๊ณผ ๋ถ„์„ ๊ธฐ์ˆ ์˜ ๋ฐœ์ „์œผ๋กœ ๋ฐ์ดํ„ฐ์˜ ๊ฐ€์น˜๋ฐœ๊ตด์„ ํ†ตํ•œ ์ˆ˜์ต ์ฐฝ์ถœ์˜ ๊ธฐ ํšŒ๊ฐ€ ํ™•๋Œ€๋˜๊ณ  ์žˆ์Œ Appification โ€ข ์ •๋ณด์š”๊ตฌ์˜ ์ฆ‰์‹œ์  ๋งŒ์กฑ์„ ์š”๊ตฌํ•˜๋Š” ์ด์šฉ์ž๊ฐ€ ์ฆ๊ฐ€ํ•˜๋Š” ์ถ”์„ธ (์ธํ„ฐ๋„ท, ๋ชจ๋ฐ”์ผ ๋ฐ ์•ฑ ์ด์šฉ์ž ์ฆ๊ฐ€) โ€ข ์ •๋ณด์ œ๊ณต์ž๋Š” ์ •๋ณด์˜ ์ˆ˜์ง‘, ๊ฐ€๊ณต, ์ œ๊ณต์˜ ์ „๋ฐ˜์ ์ธ ํ”„๋กœ์„ธ์Šค ์— ๋Œ€ํ•œ ํŒจ๋Ÿฌ๋‹ค์ž„ ์ „ํ™˜์ด ํ•„์š” Cloudification โ€ข ๋ฐ์ดํ„ฐ๋ฅผ ์œ ์ง€ํ•˜๊ณ  ์„œ๋น„์Šคํ•˜๊ธฐ ์œ„ํ•œ ์ธํ”„๋ผ ๋น„์šฉ์˜ ์ฆ๊ฐ€ โ€ข ๋น„์šฉ์ ˆ๊ฐ๊ณผ ์„œ๋น„์Šค ์œ ์—ฐํ™”๋ฅผ ์œ„ํ•ด ๋น„์ฆˆ๋‹ˆ์Šค ํ™˜๊ฒฝ์—์„œ ํด๋ผ์šฐ ๋“œ ์ธํ”„๋ผ๋ฅผ ์ฑ„ํƒํ•˜๋Š” ๋น„์œจ ์ฆ๊ฐ€ * ์ถœ์ฒ˜: Data as a Service: A Framework for Providing Reusable Enterprise Data Service, Pushpak Sarkar, John Wiley& Sons, 2015
  19. DaaS์˜ ๊ฐ•์ ๊ณผ ์•ฝ์  ๊ตฌ๋ถ„ ๋‚ด์šฉ ๊ฐ•์  Agility ์‹ ์†ํ•œ ๋ฐ์ดํ„ฐ ์ ‘๊ทผ์„

    ๊ฐ€๋Šฅ์ผ€ ํ•˜๋ฉฐ ๋‹ค์–‘ํ•œ ๋ฐ์ดํ„ฐ ๋ถ„์„์„ ๋น ๋ฅธ ์†๋„๋ก ๊ตฌํ˜„ High Quality Data ์ „๋ฌธ์ ์ธ ๋ฐ์ดํ„ฐ ์‚ฌ์—…์ž์— ์˜ํ•œ ์ง‘์ค‘์  ๊ด€๋ฆฌ๋กœ ๋ฐ์ดํ„ฐ ํ’ˆ์งˆ ํ–ฅ์ƒ Cost Effectiveness ๋ฐ์ดํ„ฐ ์ฒ˜๋ฆฌ๋ฅผ ์œ„ํ•œ ์ปดํ“จํŒ… ์ž์› ์œ ์ง€ ๋ถˆํ•„์š”๋กœ ์ธํ•œ ๋น„์šฉ ๊ฐ์†Œ ์•ฝ์  Privacy ์„œ๋น„์Šค๋กœ ์ œ๊ณต๋˜๋Š” ๋ฐ์ดํ„ฐ์˜ ํ”„๋ผ์ด๋ฒ„์‹œ ๊ด€๋ จ ๋ณต์žกํ•œ ๋ฌธ์ œ Security ์šฉ์ดํ•œ ์ ‘๊ทผ์œผ๋กœ ์ธํ•œ ์ทจ์•ฝํ•œ ๋ณด์•ˆ์„ฑ Data Governance Issues ํšจ๊ณผ์ ์ธ ๋ฐ์ดํ„ฐ ํ†ตํ•ฉ ๋ฐ ํ’ˆ์งˆ ์œ ์ง€์˜ ์–ด๋ ค์›€ * ์ถœ์ฒ˜: dataversity.net
  20. BDaaS (Big Data as a Service) โ€ข ๋น…๋ฐ์ดํ„ฐ์˜ ์œ ํ†ต, ๋ฐ์ดํ„ฐ

    ๋ถ„์„ ๋ฐ ์ปจ์„คํŒ… ๋“ฑ ๋น…๋ฐ์ดํ„ฐ ์„œ๋น„์Šค์˜ ์ฃผ์š” ๊ธฐ ๋Šฅ์ด ํด๋ผ์šฐ๋“œ ์ปดํ“จํŒ…(Cloud Computing) ํ™˜๊ฒฝ์„ ๊ธฐ๋ฐ˜์œผ๋กœ ์ œ๊ณต ๊ฐ•์  ์•ฝ์  โ€ข Rapid provisioning โ€ข Elastic scalability โ€ข Higher availability and efficiency โ€ข Relevant real-time analysis โ€ข Lower up-front costs โ€ข Outages โ€ข Costs of data migration and integration โ€ข Lack of best practices โ€ข Potentially higher costs * ์ถœ์ฒ˜: itproportal.com
  21. as-a-Service์˜ ์œ ํ˜• Amazon Web Services, Windows Azure, Google Compute Engine

    Engine Yard, RedHat Openshift, Heroku Akamai, salesforce, Cloud9 * ์ด๋ฏธ์ง€ ์ถœ์ฒ˜: ๋งˆ์ดํฌ๋กœ์†Œํ”„ํŠธ Technet
  22. ํด๋ผ์šฐ๋“œ ํ™˜๊ฒฝ์—์„œ DaaS์˜ ์œ„์น˜ DaaS in the as-a-service stack *

    ์ด๋ฏธ์ง€ ์ถœ์ฒ˜: the Next Step in the As-a-service Journey,2014, Ovum
  23. DaaS์˜ ์ด์šฉ์ž * ์ด๋ฏธ์ง€ ์ถœ์ฒ˜: Data as a Service: A

    Framework for Providing Reusable Enterprise Data Service, Pushpak Sarkar, John Wiley& Sons, 2015
  24. DaaS ์„œ๋น„์Šค ์ œ๊ณต์ž ์œ ํ˜• ์œ ํ˜• ๋‚ด์šฉ ์ฃผ์š” ์ œ๊ณต์ž ๋Œ€๊ทœ๋ชจ IT

    ๊ธฐ์—… ๋ฐ์ดํ„ฐ ๊ด€๋ฆฌ์™€ ๋น„์ฆˆ๋‹ˆ์Šค ์–ดํ”Œ๋ฆฌ์ผ€์ด์…˜ ๊ด€๋ จ ๊ธฐ์ˆ  ๋ฐ ๋…ธํ•˜์šฐ๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ DaaS ํ”Œ๋žซํผ ๋˜๋Š” ์†”๋ฃจ์…˜ ์ œ๊ณต IBM, Microsoft, Oracle, SAP ์ข…ํ•ฉ๊ด‘๊ณ ๋Œ€ํ–‰์‚ฌ ๋””์ง€ํ„ธ ๋น„์ฆˆ๋‹ˆ์Šค ๊ฒฝํ—˜๊ณผ ๋ฐ์ดํ„ฐ์ฒ˜๋ฆฌ ๊ด€ ๋ จ ๋…ธํ•˜์šฐ๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ํ•œ DaaS ์„œ๋น„์Šค ์ œ๊ณต Dentsu/Aegis Media, Havas, Interpublic(IPG), PublicisOminicom, WPP ์‹œ์Šคํ…œ ํ†ตํ•ฉ/๋น„์ฆˆ๋‹ˆ ์Šค ์„œ๋น„์Šค ์ œ๊ณต์ž ๊ธฐ์ˆ ๊ธฐ๋ฐ˜ ๋น„์ฆˆ๋‹ˆ์Šค ์ปจ์„คํŒ… ๋…ธํ•˜์šฐ๋ฅผ ๋ฐ” ํƒ•์œผ๋กœ DaaS ์„œ๋น„์Šค ์ œ๊ณต Accenture Interactive, Deloitte Digital ๋ฐ์ดํ„ฐ ์‚ฌ์—…์ž ๋ณด์œ ํ•˜๊ณ  ์žˆ๋Š” ๋ฐ์ดํ„ฐ์ฒ˜๋ฆฌ ๊ธฐ์ˆ ๊ณผ ์†”๋ฃจ ์…˜์„ ๋ฐ”ํƒ•์œผ๋กœ DaaS ์„œ๋น„์Šค ์ œ๊ณต Axciom, Experian, Neustar * ์ถœ์ฒ˜: Data-as-a-service: the Next Step in the As-a-service Journey, 2014, Tom Pringle, Ovum
  25. DaaS๋ฅผ ๊ตฌ์„ฑํ•˜๋Š” ๋ฐ์ดํ„ฐ ์œ ํ˜• Foundational: Build a Better Long Term

    Target Data Set Onboarded: Connect Offline and Online IDs Fast: Target the Right In Market Customers and Prospects 1st party data combined with 3rd party and Hard-to-Find Data(HTFD). These specialty HTFD sets have been aggregated from hundreds of Big Data sources and go well beyond third party lists. Offline data transformed into addressable online identities. Onboarding provides new opportunities to reach customers and prospects in the digital universe. Real-time behavioral data. Fast Data aggregates event and behavioral- driven data to determine purchase intent as it occurs * ์ด๋ฏธ์ง€ ์ถœ์ฒ˜: datamentors.com
  26. ์ฃผ์š” DaaS ์‚ฌ๋ก€ โ€ข KT API Store โ€ข IBM Analytics

    for Twitter โ€ข Oracle Data as a Service โ€ข Hoovers.com โ€ข Treasure Data โ€ข UN Data
  27. ์ฃผ์š” DaaS ์‚ฌ๋ก€ 1 โ€ข KT API Store โ€ข ํ”Œ๋žซํผ์„

    ํ†ตํ•ด ๊ตญ๋‚ด์™ธ ๋‹ค์–‘ํ•œ ๋ฐ์ดํ„ฐ API๋ฅผ ์†Œ๊ฐœํ•˜๋Š” ์„œ๋น„์Šค (๊ณผ๊ธˆ /๋น„๊ณผ๊ธˆ ๋ฐฉ์‹ ์ œ๊ณต)
  28. ์ฃผ์š” DaaS ์‚ฌ๋ก€ 2 โ€ข IBM Analytics for Twitter โ€ข

    ๋งค์ผ 5์–ต๊ฑด์˜ ํŠธ์œ„ํ„ฐ๋ฐ์ดํ„ฐ ๋ถ„์„์ •๋ณด๋ฅผ ๊ธฐ์—…์—๊ฒŒ ์ œ๊ณต (์œ ๋ฃŒ๊ตฌ๋…)
  29. ์ฃผ์š” DaaS ์‚ฌ๋ก€ 3 โ€ข Oracle Data as a Service

    โ€ข RDBMS ์‹œ์žฅ์˜ 1์œ„ ์‚ฌ์—…์ž Oracle์— ์˜ํ•ด ์ œ๊ณต๋˜๋Š” DaaS ์„œ๋น„์Šค ( ๋ผ์ด์„ ์Šค ๊ตฌ๋งค) * ์ด๋ฏธ์ง€ ์ถœ์ฒ˜: constellationr.com
  30. ์ฃผ์š” DaaS ์‚ฌ๋ก€ 4 โ€ข Hoovers.com โ€ข ์‚ฐ์—…์ •๋ณด, ํšŒ์‚ฌ์ฃผ์š”์ •๋ณด, ์ธ๋ช…์ •๋ณด

    ๋“ฑ ๋น„์ฆˆ๋‹ˆ์Šค ํŠนํ™” ์ •๋ณด๋ฅผ ์ œ๊ณต ํ•˜๋Š” DaaS ์„œ๋น„์Šค (์œ ๋ฃŒ๊ตฌ๋…)
  31. ์ฃผ์š” DaaS ์‚ฌ๋ก€ 5 โ€ข Treasure Data โ€ข ํด๋ผ์šฐ๋“œ ๊ธฐ๋ฐ˜

    ํ™˜๊ฒฝ์—์„œ ๋น…๋ฐ์ดํ„ฐ์˜ ์ €์žฅ, ํ†ตํ•ฉ ๋ฐ ๋ฐ˜์ถœ ๊ธฐ๋Šฅ์„ ์ œ๊ณต ํ•˜๋Š” DaaS ์„œ๋น„์Šค (์œ ๋ฃŒ๊ตฌ๋…)
  32. ์ฃผ์š” DaaS ์‚ฌ๋ก€ 6 โ€ข UN Data โ€ข UN์ด ๋ณด์œ ํ•œ

    ์ „์„ธ๊ณ„์˜ ํ•ต์‹ฌ ํ†ต๊ณ„ ๋ฐ์ดํ„ฐ๋ฅผ ์ž์ฒด ํ”Œ๋žซํผ์„ ํ†ตํ•ด ์ œ ๊ณตํ•˜๋Š” DaaS ์„œ๋น„์Šค
  33. 5โ˜… Linked Data โ˜… ์˜คํ”ˆ ๋ผ์ด์„ ์Šค์— ๋”ฐ๋ผ (ํฌ๋งท๊ณผ ๊ด€๊ณ„์—†์ด) ์›น์„

    ํ†ตํ•ด ์ •๋ณด๋ฅผ ์ ‘๊ทผ ๊ฐ€๋Šฅํ•˜๋„๋ก ํ•จ โ˜…โ˜… ๊ตฌ์กฐํ™”๋œ ๋ฐ์ดํ„ฐ์˜ ํ˜•ํƒœ(์˜ˆ๋ฅผ ๋“ค์–ด ์ด๋ฏธ์ง€๋ณด๋‹ค๋Š” Excel)๋กœ ์ •๋ณด๋ฅผ ์ ‘๊ทผ ๊ฐ€๋Šฅํ•˜ ๋„๋ก ํ•จ โ˜…โ˜…โ˜… ๋น„๋…์ ์ ์ธ ํฌ๋งท(์˜ˆ๋ฅผ ๋“ค์–ด Excel ๋ณด๋‹ค๋Š” CSV)์œผ๋กœ ์ •๋ณด๋ฅผ ์ ‘๊ทผ ๊ฐ€๋Šฅํ•˜๋„๋ก ํ•จ โ˜…โ˜…โ˜…โ˜… ๋ชจ๋“  ๊ฐœ์ฒด์— URI๋ฅผ ํ• ๋‹นํ•˜์—ฌ ์‹๋ณ„ ๊ฐ€๋Šฅํ•˜๋„๋ก ํ•จ โ˜…โ˜…โ˜…โ˜…โ˜… ๋‹ค๋ฅธ ์ •๋ณด์™€์˜ ์—ฐ๊ฒฐ ์ •๋ณด๋ฅผ ํฌํ•จํ•˜๋„๋ก ํ•จ * ์ถœ์ฒ˜: 5stardata.info
  34. Linked Data์˜ ๊ฐ•์ ๊ณผ ์•ฝ์  ๊ฐ•์  ์•ฝ์  โ€ข ์ˆ˜๋งŽ์€ ๊ธฐ๊ด€์˜ ์ฐธ์—ฌ(LOD

    Cloud) โ€ข RDF, JSON๊ณผ ๊ฐ™์€ ๋ฒ”์šฉ ํ‘œ์ค€ํฌ๋งท ์œผ๋กœ๋ฐ์ดํ„ฐ ์ œ๊ณต โ€ข ์žฌ์‚ฌ์šฉ ๊ฐ€๋Šฅํ•œ ์–ดํœ˜์— ๊ธฐ๋ฐ˜ โ€ข ๋‹จ์ผ ์ฟผ๋ฆฌ์–ธ์–ด(SPARQL)๋ฅผ ํ™œ์šฉ ๋‹ค์ค‘์˜ ์—”๋“œํฌ์ธํŠธ(Endpoint)๋กœ ๋ฐ ์ดํ„ฐ ์กฐํšŒ โ€ข ์—”๋“œํฌ์ธํŠธ์˜ ๋ถˆ์•ˆ์ •์„ฑ โ€ข SPARQL ์ฟผ๋ฆฌ์˜ ๋†’์€ ๋น„์šฉ โ€ข Federated Query ๊ฒฐ๊ณผ ํ†ตํ•ฉ์˜ ์–ด๋ ค ์›€๊ณผ ์„ฑ๋Šฅ ์ด์Šˆ
  35. Linked Data as a Service โ€ข DataGraft โ€ข ๋งํฌ๋“œ ๋ฐ์ดํ„ฐ์˜

    ์ƒ์„ฑ, ํ™œ์šฉ, ์žฌ์‚ฌ์šฉ ์ ˆ์ฐจ๋ฅผ ๋‹จ์ˆœํ™”ํ•˜๊ณ , ์ฒ˜๋ฆฌ์†๋„ ๋ฅผ ๋†’์ด๊ธฐ ์œ„ํ•ด ๊ฐœ๋ฐœ๋œ LDaaS ์„œ๋น„์Šค
  36. Linked Data as a Service โ€ข LOD Laundromat โ€ข ์ „์„ธ๊ณ„์—

    ์กด์žฌํ•˜๋Š” LOD๋ฅผ ์ˆ˜์ง‘ํ•˜๊ณ  ์ •์ œํ•˜์—ฌ ํ•˜๋‚˜์˜ LOD ์ €์žฅ ์†Œ๋กœ ํ†ตํ•ฉํ•˜์—ฌ ์ œ๊ณตํ•˜๋Š” LDaaS
  37. Linked Data as a Service โ€ข DYDRA โ€ข RDF ๋ฐ์ดํ„ฐ์˜

    ์ €์žฅ๊ณผ ๋ฐฐํฌ, SPARQL ์ฟผ๋ฆฌ๋ฅผ ์ง€์›ํ•˜๋Š” LDaaS
  38. Paradigm Shift โ€ข ์—ฐ๊ตฌ ์ตœ์ข…๊ฒฐ๊ณผ๋ฌผ์˜ ๊ด€๋ฆฌ๋ฅผ ๋„˜์–ด, ์—ฐ๊ตฌ๊ณผ์ •์—์„œ ์ƒ์„ฑ๋˜๋Š” ์—ฐ๊ตฌ๋ฐ์ดํ„ฐ

    ๊ด€๋ฆฌ๊นŒ์ง€๋กœ ๋ฒ”์œ„ ํ™•์žฅ๋˜๋Š” ์ถ”์„ธ โ€ข ๊ตญ์ œ์  ์šฐ์ˆ˜ํ•œ ๋Œ€ํ•™๊ต ๋ฐ ์—ฐ๊ตฌ์†Œ๋“ค์ด ์—ฐ๊ตฌ๋ฐ์ดํ„ฐ ๊ด€๋ฆฌ์„œ๋น„์Šค์— ์ ๊ทน ์ฐธ์—ฌ (ํŠนํžˆ, ๋ฏธ๊ตญ, ์˜๊ตญ, ํ˜ธ์ฃผ ๋Œ€ํ•™ ๋ฐ ์ •๋ถ€๊ฐ€ RDM ์„œ๋น„์Šค ๊ฐœ๋ฐœ ๋ฐ ์‹œํ–‰)
  39. What is Research Data? Proposal Planning Writing Project Start Up

    Data Collection Data Analysis Data Sharing End of Project Data Discovery Data Archiving/ Curation Re-use Deposit Re-Purpose Data Life Cycle โ€ข ์—ฐ๊ตฌ๊ฐ€ ์‹œ์ž‘๋˜์–ด ์—ฐ๊ตฌ๊ฐ€ ๋๋‚˜๋Š” ๊ณผ์ •๊นŒ์ง€ ์ƒ์‚ฐ๋˜๋Š” ๋ชจ๋“  ๋ฐ์ดํ„ฐ
  40. What is Research Data? โ€ข ์—ฐ๊ตฌ ๋ฐ์ดํ„ฐ์˜ ํ˜•ํƒœ โ€“ ์ˆ˜์น˜

    (numerical) โ€“ ๊ณต๊ฐ„ (spatial) โ€“ ๋„ํ‘œ (graphical) โ€“ ๋ฌธ์„œ (text) โ€ข ๋ฐ์ดํ„ฐ ์ˆ˜์ง‘ ๋ฐฉ๋ฒ• โ€“ ๊ด€์ธก (Observation): ๋ง์›๊ฒฝ, ์ „์žํ˜„๋ฏธ๊ฒฝ, ์ธ๊ณต์œ„์„ฑ โ€“ ๊ฐ์‹œ (Monitoring): ์„ผ์„œ โ€“ ์กฐ์‚ฌ (Investigation): ์„ค๋ฌธ์กฐ์‚ฌ, ๊ธฐ์ˆ  ๋ฐ ์‹œ์žฅ์กฐ์‚ฌ, ๊ธฐ์ˆ ๊ฐ€์น˜ํ‰๊ฐ€ โ€“ ์‹คํ—˜ (Experiment): ๊ฐ€์†๊ธฐ, ํ™”ํ•™, ๋ฐ”์ด์˜ค ์‹คํ—˜์žฅ๋น„ โ€“ ์—ฐ๊ตฌ ๋ถ„์„ (Research analysis): ๋ถ„์„ ๋„๊ตฌ โ€“ ๊ณ„์‚ฐ (Computation): ์Šˆํผ์ปดํ“จํ„ฐ
  41. Why RDM? โ€ข ๋‹ค์–‘ํ•œ ๋ฐฉ๋ฒ•์œผ๋กœ ์ˆ˜์ง‘๋œ ์—ฐ๊ตฌ๋ฐ์ดํ„ฐ๋Š” ํ”„๋กœ์ ํŠธ๊ฐ€ ๋๋‚œ ํ›„

    ์—ฐ๊ตฌ์ž๊ฐ€ ๊ฐœ๋ณ„๊ด€๋ฆฌํ•˜๊ณ  ๊ทธ ์ดํ›„์˜ ํ™œ์šฉ์—ฌ๋ถ€๋Š” ์•Œ ์ˆ˜ ์—†๋Š” ์ƒํ™ฉ โ€ข ์—ฐ๊ตฌ ๊ณผ์ •์—์„œ ์ƒ์„ฑ๋œ ๋ฐ์ดํ„ฐ์˜ ์žฌํ™œ์šฉ์„ฑ์„ ๋†’์ด๊ณ , ์ƒˆ๋กœ์šด ๋ฐœ๊ฒฌ์œผ๋กœ ์ด์–ด์งˆ ๊ฐ€๋Šฅ์„ฑ์ด ๋†’์€ ๋ฐ์ดํ„ฐ์˜ ์ฒด๊ณ„์ ์ธ ๊ด€๋ฆฌ
  42. Why RDM? โ€ข ํšจ์œจ์ ์ธ ์—ฐ๊ตฌ์ˆ˜ํ–‰ ๋ฟ ์•„๋‹ˆ๋ผ, ์—ฐ๊ตฌ๊ฒฐ๊ณผ์˜ ์‹ ๋ขฐ์„ฑ์„ ๋†’์—ฌ์ฃผ๋ฉฐ,

    ์—ฐ๊ตฌ๊ฒฐ๊ณผ ์กฐ ์ž‘์„ ๋ฏธ์—ฐ์— ๋ฐฉ์ง€ โ€ข ํ™ฉ์šฐ์„์˜ ์ค„๊ธฐ์„ธํฌ ์กฐ์ž‘ ์‚ฌํƒœ๋Š” ์œค๋ฆฌ์˜์‹ ๊ฒฐ์—ฌ์— ๋”ฐ๋ฅธ ๊ฒฐ๊ณผ์ด๋ฉฐ, ์—ฐ๊ตฌ๋ฐ์ด ํ„ฐ ๊ด€๋ฆฌ์˜ ์ค‘์š”์„ฑ์„ ์ธ์‹์ผ€ ํ•œ ์‚ฌ๋ก€
  43. Why RDM? Nature์˜ ๋ฐ์ดํ„ฐ ์š”๊ตฌ์‚ฌํ•ญ โ€ข ๋…ผ๋ฌธ์ œ์ถœ์ž๋Š” Nature Editor๊ฐ€ ์š”์ฒญํ•œ

    ๊ธฐํ•œ ๋‚ด์— ๊ทœ์ •์— ๋”ฐ๋ผ ์—ฐ๊ตฌ์— ์‚ฌ์šฉ๋œ ๋ฐ์ดํ„ฐ์…‹ ์—…๋กœ๋“œ ๋ฐ ๋ฆฌํฌํŠธ๋ฅผ ์ œ์ถœํ•ด์•ผ ํ•จ โ€ข ์ƒ๋ช… ๊ณผํ•™ ๋ถ„์•ผ ์˜ˆ์‹œ Reporting Requirements - ์‹คํ—˜์— ์‚ฌ์šฉ๋œ ๋ถ„์„ ์„ค๊ณ„ ์š”์†Œ์— ๋Œ€ํ•œ ์„ธ๋ถ€์‚ฌํ•ญ์„ ํ‰๊ฐ€์ž์—๊ฒŒ ์ œ์ถœ ex) ์‹คํ—˜ ์—ฐ๊ตฌ ์„ค๊ณ„: Sample size, Randomization, Blinding, Replication ํ†ต๊ณ„ ์—ฐ๊ตฌ: ์‚ฌ์šฉ๋œ ํ†ต๊ณ„๋ฐฉ๋ฒ•๋ก  ๋ฐ ๋ถ„์„ ๋ฐ์ดํ„ฐ ์ƒ์„ธ ์ •๋ณด ์ƒ์„ธํžˆ ๊ธฐ์ˆ  ์‹œ์•ฝ ์—ฐ๊ตฌ: ํ•ญ์ฒด, ์…€ ์ •๋ ฌ ๋“ฑ policy: http://www.nature.com/authors/policies/reporting.pdf - ๋™๋ฃŒ ํ‰๊ฐ€๊ฐ€ ์ง„ํ–‰๋˜๋Š” ๋™์•ˆ ์ฒดํฌ๋ฆฌ์ŠคํŠธ๋ฅผ ์ž‘์„ฑํ•ด์„œ ์ œ์ถœ ex) ์ƒ˜ํ”Œ ์‚ฌ์ด์ฆˆ๋Š” ๊ฐ ๊ทธ๋ฃน๋ณ„, ์ƒํ™ฉ๋ณ„๋กœ ์–ด๋– ํ•œ๊ฐ€? ์ƒ˜ํ”Œ์€ ๋…์ž๊ฐ€ ์ดํ•ดํ•  ์ˆ˜ ์žˆ๊ฒŒ ๋Œ€ํ‘œ์„ฑ์„ ๋ ๋Š”๊ฐ€? ์ฒดํฌ๋ฆฌ์ŠคํŠธ: http://www.nature.com/authors/policies/checklist.pdf ๋ฐ์ดํ„ฐ์…‹ ์ œ์ถœ - ๊ธฐํ•œ ๋‚ด์— ๊ณต๊ณต ๋ฆฌํฌ์ง€ํ† ๋ฆฌ์ธ Figshare, Dryad์— ๋ฐ์ดํ„ฐ์…‹์„ ์ œ์ถœํ•ด์•ผ ํ•˜๋ฉฐ, ์ œ์ถœ๋œ ๋ฐ์ดํ„ฐ๋Š” ๋ฐœํ–‰ ์ด์ „์—๋Š” ๋™๋ฃŒํ‰๊ฐ€์ž๋งŒ ์ ‘๊ทผ๊ฐ€๋Šฅํ•˜๋„๋ก ์„ค์ •๋˜์–ด ์žˆ์Œ. ์ถ”๊ฐ€ ์ •๋ณด: http://www.nature.com/authors/policies/availability.html
  44. Value of RDM โ€ข ๋ฐ์ดํ„ฐ ๊ด€๋ฆฌ ๋ฐ ๋ถ„์„ ์‹œ๊ฐ„ ๊ฐ์†Œ

    โ€ข ๋ฐ์ดํ„ฐ ์†์‹ค์˜ ์œ„ํ—˜์— ์„œ ํ•ด๋ฐฉ โ€ข ๋ฐ์ดํ„ฐ ์žฌํ™œ์šฉ์„ฑ ์ƒ์Šน โ€ข ์ง€์ ์žฌ์‚ฐ๊ถŒ ๋ช…ํ™•ํ™” โ€ข ์—ฐ๊ตฌ์ž ๊ฐ„ ๋„คํŠธ์›Œํฌ ํ™œ์„ฑํ™” โ€ข ์ฒด๊ณ„์ ์ธ ์—ฐ๊ตฌ๊ฒฐ๊ณผ๋ฌผ ๊ด€๋ฆฌ ๋ฐ ๋ฐ์ดํ„ฐ ๋ง์‹ค ์œ„ํ—˜ ๊ฐ์†Œ โ€ข ์—ฐ๊ตฌ๋ฐ์ดํ„ฐ ๊ด€๋ฆฌ ๋ฐ ํ™œ์šฉ ์œผ๋กœ ๊ด€๋ฆฌ๋ฌผ์˜ ์–‘์  ์ฆ๋Œ€ ๋ฐ ์ธ์šฉ๊ณผ ์žฌ์‚ฌ์šฉ์˜ ํšŸ์ˆ˜ ์ฆ๊ฐ€ โ€ข ์ƒˆ๋กœ์šด ์—ฐ๊ตฌ์ž ๋„คํŠธ์›ŒํŠธ ๋ฐœ๊ตด, ํ˜‘๋ ฅ๊ด€๊ณ„ ํ™•๋Œ€, ์—ฐ๊ตฌ ํ”Œ๋žซํผ ํ™œ์šฉํ™•๋Œ€ โ€ข ์—ฐ๊ตฌ๊ฒฐ๊ณผ๋ฌผ + ์›์ฒœ ๋ฐ์ดํ„ฐ โ€ข ์—ฐ๊ตฌ๊ฒฐ๊ณผ๋ฌผ ์‹ ๋ขฐ ์ด์šฉ์ž ๊ธฐ๊ด€ ๋ฐ ์ •๋ณด๊ด€๋ฆฌ์†Œ ์—ฐ๊ตฌ์ž
  45. Research Data Alliance โ€ข Research Data Alliance (์—ฐ๊ตฌ๋ฐ์ดํ„ฐ ์—ฐํ•ฉ์ฒด) โ€“RDA๋Š”

    ๋ฐ์ดํ„ฐ ๊ณต์œ  ๋ฐ ๊ตํ™˜ ์žฅ๋ฒฝ์„ ์ œ๊ฑฐํ•˜๊ฑฐ๋‚˜ ์ค„์ด๋Š” ๋ฐ ์ดˆ์ ์„ ๋‘” ๋ฐ ์ดํ„ฐ ๊ธฐ๋ฐ˜์˜ ๊ธ€๋กœ๋ฒŒ ํ˜์‹ ์„ ๊ฐ€์†ํ™”ํ•˜๊ธฐ ์œ„ํ•œ ์ปค๋ฎค๋‹ˆํ‹ฐ ํ™œ๋™ โ€“ํ˜ธ์ฃผ, ๋ฏธ๊ตญ, ์˜๊ตญ์˜ ํŽ€๋”ฉ ๊ธฐ๊ด€์˜ ์ง€์› ํ•˜์— 2012๋…„ ์ •์‹ ์ถœ๋ฒ”๋จ โ€“100๊ฐœ ๊ตญ๊ฐ€, 3,200๋ช… ์ด์ƒ์˜ ๋ฉค๋ฒ„๋ฅผ ๋ณด์œ ํ•˜๊ณ  ์žˆ์œผ๋ฉฐ, ํ•™๋ฌธ ๋ถ„์•ผ์— ์ œํ•œ ์ด ์—†์ด Data Management ์ „๋ฌธ๊ฐ€๋กœ ๊ตฌ์„ฑ๋˜์–ด ์žˆ์Œ
  46. Preparing RDM ๋ฐ์ดํ„ฐ ๊ด€๋ฆฌ์˜ ๋ชฉํ‘œ ์ˆ˜๋ฆฝ ๋ฐ์ดํ„ฐ ์„ ์ • ๋ฉ”ํƒ€๋ฐ์ดํ„ฐ ๋ฐ์ดํ„ฐ์˜

    ์ €์žฅ, ๋ฐฑ์—…, ๋ณด์•ˆ ๋ฒ•์ , ์œค๋ฆฌ์  ๊ณ ๋ ค์‚ฌํ•ญ: ์ง€์ ์žฌ์‚ฐ๊ถŒ ๋ฐ์ดํ„ฐ ๊ณต์œ ์™€ ์žฌ์‚ฌ์šฉ ๋ฐ์ดํ„ฐ ์•„์นด์ด๋น™
  47. Concluding Remarks โ€ข iSchool Movement รผ Information, Technology, People์˜ ์‚ผ๊ฐ๊ด€๊ณ„๋ฅผ

    ๋™์‹œ์— ์—ฐ๊ตฌํ•˜๋Š” ์‹  ํ•™๋ฌธ๋ถ„์•ผ๋กœ ์ •์ฐฉ รผ ๋Œ€๋ถ€๋ถ„ ์™ธ๊ตญ์˜ Data Science ์ „๊ณต๋„ iSchool ์•ˆ์— ๊ฐœ์„ค๋˜๋Š” ์ถ”์„ธ โ€ข DaaS รผ Data-as-a-Service(DaaS), Big Data-as-a-Service (BDaaS)๋Š” ๋น„์ฆˆ๋‹ˆ์Šค ํ™˜๊ฒฝ์—์„œ ์ด๋ฏธ ํฐ ํ๋ฆ„์„ ํ˜•์„ฑํ•˜๊ณ  ์žˆ์Œ รผ DaaS์˜ ๊ฐ•์ ์€ ์ด์šฉ์ž๊ฐ€ ๋ฐ์ดํ„ฐ ์œ ์ง€๊ด€๋ฆฌ ์—…๋ฌด๋ฅผ ์ตœ์†Œํ™” ํ•  ์ˆ˜ ์žˆ๊ณ , ๋ฐ์ดํ„ฐ ๊ธฐ๋ฐ˜์˜ ์ œํ’ˆ๊ณผ ์ฝ˜ํ…์ธ  ๊ฐœ๋ฐœ์— ์ง‘์ค‘ํ•  ์ˆ˜ ์žˆ๋„๋ก ํ•จ รผ DaaS ๊ฐœ๋…์„ ์ ์šฉํ•œ ๋งํฌ๋“œ ๋ฐ์ดํ„ฐ ์„œ๋น„์Šค๋Š” ๊ธฐ์กด์— ๋งํฌ๋“œ ๋ฐ์ด ํ„ฐ๊ฐ€ ์ง€๋‹Œ ๋‹จ์ ๋“ค์„ ๊ทน๋ณตํ•  ์ˆ˜ ์žˆ๋Š” ๋Œ€์•ˆ์œผ๋กœ์˜ ๊ฐ€๋Šฅ์„ฑ์ด ๋†’์Œ
  48. Concluding Remarks โ€ข Data Management รผ ๋ฐ์ดํ„ฐ ๊ด€๋ฆฌ ๋ฐ ์„œ๋น„์Šค๋Š”

    ์ƒˆ๋กœ์šด ์˜์—ญ์œผ๋กœ ์ƒˆ๋กœ์šด ๊ด€์ ์—์„œ์˜ ์‚ฌ ๊ณ ๊ฐ€ ํ•„์š”ํ•จ รผ ํ•™์ˆ ๋„์„œ๊ด€์˜ ์—ญํ• ์— ๋Œ€ํ•œ ์ƒˆ๋กœ์šด ๊ธฐํšŒ รผ ๋„์„œ๊ด€ ๋ฐ ์—ฐ๊ตฌ์†Œ๊ฐ€ ๋ฐ์ดํ„ฐ๊ฐ€ ์ค‘์š”ํ•œ ์ž์›์ด๋ผ๋Š” ์ƒˆ๋กœ์šด ์ธ์‹์ด ํ•„์š”ํ•จ