13 research outputs found

    ํฐ ๊ทธ๋ž˜ํ”„ ์ƒ์—์„œ์˜ ๊ฐœ์ธํ™”๋œ ํŽ˜์ด์ง€ ๋žญํฌ์— ๋Œ€ํ•œ ๋น ๋ฅธ ๊ณ„์‚ฐ ๊ธฐ๋ฒ•

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์ „๊ธฐยท์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€, 2020. 8. ์ด์ƒ๊ตฌ.Computation of Personalized PageRank (PPR) in graphs is an important function that is widely utilized in myriad application domains such as search, recommendation, and knowledge discovery. Because the computation of PPR is an expensive process, a good number of innovative and efficient algorithms for computing PPR have been developed. However, efficient computation of PPR within very large graphs with over millions of nodes is still an open problem. Moreover, previously proposed algorithms cannot handle updates efficiently, thus, severely limiting their capability of handling dynamic graphs. In this paper, we present a fast converging algorithm that guarantees high and controlled precision. We improve the convergence rate of traditional Power Iteration method by adopting successive over-relaxation, and initial guess revision, a vector reuse strategy. The proposed method vastly improves on the traditional Power Iteration in terms of convergence rate and computation time, while retaining its simplicity and strictness. Since it can reuse the previously computed vectors for refreshing PPR vectors, its update performance is also greatly enhanced. Also, since the algorithm halts as soon as it reaches a given error threshold, we can flexibly control the trade-off between accuracy and time, a feature lacking in both sampling-based approximation methods and fully exact methods. Experiments show that the proposed algorithm is at least 20 times faster than the Power Iteration and outperforms other state-of-the-art algorithms.๊ทธ๋ž˜ํ”„ ๋‚ด์—์„œ ๊ฐœ์ธํ™”๋œ ํŽ˜์ด์ง€๋žญํฌ (P ersonalized P age R ank, PPR ๋ฅผ ๊ณ„์‚ฐํ•˜๋Š” ๊ฒƒ์€ ๊ฒ€์ƒ‰ , ์ถ”์ฒœ , ์ง€์‹๋ฐœ๊ฒฌ ๋“ฑ ์—ฌ๋Ÿฌ ๋ถ„์•ผ์—์„œ ๊ด‘๋ฒ”์œ„ํ•˜๊ฒŒ ํ™œ์šฉ๋˜๋Š” ์ค‘์š”ํ•œ ์ž‘์—… ์ด๋‹ค . ๊ฐœ์ธํ™”๋œ ํŽ˜์ด์ง€๋žญํฌ๋ฅผ ๊ณ„์‚ฐํ•˜๋Š” ๊ฒƒ์€ ๊ณ ๋น„์šฉ์˜ ๊ณผ์ •์ด ํ•„์š”ํ•˜๋ฏ€๋กœ , ๊ฐœ์ธํ™”๋œ ํŽ˜์ด์ง€๋žญํฌ๋ฅผ ๊ณ„์‚ฐํ•˜๋Š” ํšจ์œจ์ ์ด๊ณ  ํ˜์‹ ์ ์ธ ๋ฐฉ๋ฒ•๋“ค์ด ๋‹ค์ˆ˜ ๊ฐœ๋ฐœ๋˜์–ด์™”๋‹ค . ๊ทธ๋Ÿฌ๋‚˜ ์ˆ˜๋ฐฑ๋งŒ ์ด์ƒ์˜ ๋…ธ๋“œ๋ฅผ ๊ฐ€์ง„ ๋Œ€์šฉ๋Ÿ‰ ๊ทธ๋ž˜ํ”„์— ๋Œ€ํ•œ ํšจ์œจ์ ์ธ ๊ณ„์‚ฐ์€ ์—ฌ์ „ํžˆ ํ•ด๊ฒฐ๋˜์ง€ ์•Š์€ ๋ฌธ์ œ์ด๋‹ค . ๊ทธ์— ๋”ํ•˜์—ฌ , ๊ธฐ์กด ์ œ์‹œ๋œ ์•Œ๊ณ ๋ฆฌ๋“ฌ๋“ค์€ ๊ทธ๋ž˜ํ”„ ๊ฐฑ์‹ ์„ ํšจ์œจ์ ์œผ๋กœ ๋‹ค๋ฃจ์ง€ ๋ชปํ•˜์—ฌ ๋™์ ์œผ๋กœ ๋ณ€ํ™”ํ•˜๋Š” ๊ทธ๋ž˜ํ”„๋ฅผ ๋‹ค๋ฃจ๋Š” ๋ฐ์— ํ•œ๊ณ„์ ์ด ํฌ๋‹ค . ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๋†’์€ ์ •๋ฐ€๋„๋ฅผ ๋ณด์žฅํ•˜๊ณ  ์ •๋ฐ€๋„๋ฅผ ํ†ต์ œ ๊ฐ€๋Šฅํ•œ , ๋น ๋ฅด๊ฒŒ ์ˆ˜๋ ดํ•˜๋Š” ๊ฐœ์ธํ™”๋œ ํŽ˜์ด์ง€๋žญํฌ ๊ณ„์‚ฐ ์•Œ๊ณ ๋ฆฌ๋“ฌ์„ ์ œ์‹œํ•œ๋‹ค . ์ „ํ†ต์ ์ธ ๊ฑฐ๋“ญ์ œ๊ณฑ๋ฒ• (Power ์— ์ถ•์ฐจ๊ฐ€์†์™„ํ™”๋ฒ• (Successive Over Relaxation) ๊ณผ ์ดˆ๊ธฐ ์ถ”์ธก ๊ฐ’ ๋ณด์ •๋ฒ• (Initial Guess ์„ ํ™œ์šฉํ•œ ๋ฒกํ„ฐ ์žฌ์‚ฌ์šฉ ์ „๋žต์„ ์ ์šฉํ•˜์—ฌ ์ˆ˜๋ ด ์†๋„๋ฅผ ๊ฐœ์„ ํ•˜์˜€๋‹ค . ์ œ์‹œ๋œ ๋ฐฉ๋ฒ•์€ ๊ธฐ์กด ๊ฑฐ๋“ญ์ œ๊ณฑ๋ฒ•์˜ ์žฅ์ ์ธ ๋‹จ์ˆœ์„ฑ๊ณผ ์—„๋ฐ€์„ฑ์„ ์œ ์ง€ ํ•˜๋ฉด์„œ ๋„ ์ˆ˜๋ ด์œจ๊ณผ ๊ณ„์‚ฐ์†๋„๋ฅผ ํฌ๊ฒŒ ๊ฐœ์„  ํ•œ๋‹ค . ๋˜ํ•œ ๊ฐœ์ธํ™”๋œ ํŽ˜์ด์ง€๋žญํฌ ๋ฒกํ„ฐ์˜ ๊ฐฑ์‹ ์„ ์œ„ํ•˜์—ฌ ์ด์ „์— ๊ณ„์‚ฐ ๋˜์–ด ์ €์žฅ๋œ ๋ฒกํ„ฐ๋ฅผ ์žฌ์‚ฌ์šฉํ•˜ ์—ฌ , ๊ฐฑ์‹  ์— ๋“œ๋Š” ์‹œ๊ฐ„์ด ํฌ๊ฒŒ ๋‹จ์ถ•๋œ๋‹ค . ๋ณธ ๋ฐฉ๋ฒ•์€ ์ฃผ์–ด์ง„ ์˜ค์ฐจ ํ•œ๊ณ„์— ๋„๋‹ฌํ•˜๋Š” ์ฆ‰์‹œ ๊ฒฐ๊ณผ๊ฐ’์„ ์‚ฐ์ถœํ•˜๋ฏ€๋กœ ์ •ํ™•๋„์™€ ๊ณ„์‚ฐ์‹œ๊ฐ„์„ ์œ ์—ฐํ•˜๊ฒŒ ์กฐ์ ˆํ•  ์ˆ˜ ์žˆ์œผ๋ฉฐ ์ด๋Š” ํ‘œ๋ณธ ๊ธฐ๋ฐ˜ ์ถ”์ •๋ฐฉ๋ฒ•์ด๋‚˜ ์ •ํ™•ํ•œ ๊ฐ’์„ ์‚ฐ์ถœํ•˜๋Š” ์—ญํ–‰๋ ฌ ๊ธฐ๋ฐ˜ ๋ฐฉ๋ฒ• ์ด ๊ฐ€์ง€์ง€ ๋ชปํ•œ ํŠน์„ฑ์ด๋‹ค . ์‹คํ—˜ ๊ฒฐ๊ณผ , ๋ณธ ๋ฐฉ๋ฒ•์€ ๊ฑฐ๋“ญ์ œ๊ณฑ๋ฒ•์— ๋น„ํ•˜์—ฌ 20 ๋ฐฐ ์ด์ƒ ๋น ๋ฅด๊ฒŒ ์ˆ˜๋ ดํ•œ๋‹ค๋Š” ๊ฒƒ์ด ํ™•์ธ๋˜์—ˆ์œผ๋ฉฐ , ๊ธฐ ์ œ์‹œ๋œ ์ตœ๊ณ  ์„ฑ๋Šฅ ์˜ ์•Œ๊ณ ๋ฆฌ ๋“ฌ ๋ณด๋‹ค ์šฐ์ˆ˜ํ•œ ์„ฑ๋Šฅ์„ ๋ณด์ด๋Š” ๊ฒƒ ๋˜ํ•œ ํ™•์ธ๋˜์—ˆ๋‹ค1 Introduction 1 2 Preliminaries: Personalized PageRank 4 2.1 Random Walk, PageRank, and Personalized PageRank. 5 2.1.1 Basics on Random Walk 5 2.1.2 PageRank. 6 2.1.3 Personalized PageRank 8 2.2 Characteristics of Personalized PageRank. 9 2.3 Applications of Personalized PageRank. 12 2.4 Previous Work on Personalized PageRank Computation. 17 2.4.1 Basic Algorithms 17 2.4.2 Enhanced Power Iteration 18 2.4.3 Bookmark Coloring Algorithm. 20 2.4.4 Dynamic Programming 21 2.4.5 Monte-Carlo Sampling. 22 2.4.6 Enhanced Direct Solving 24 2.5 Summary 26 3 Personalized PageRank Computation with Initial Guess Revision 30 3.1 Initial Guess Revision and Relaxation 30 3.2 Finding Optimal Weight of Successive Over Relaxation for PPR. 34 3.3 Initial Guess Construction Algorithm for Personalized PageRank. 36 4 Fully Personalized PageRank Algorithm with Initial Guess Revision 42 4.1 FPPR with IGR. 42 4.2 Optimization. 49 4.3 Experiments. 52 5 Personalized PageRank Query Processing with Initial Guess Revision 56 5.1 PPR Query Processing with IGR 56 5.2 Optimization. 64 5.3 Experiments. 67 6 Conclusion 74 Bibliography 77 Appendix 88 Abstract (In Korean) 90Docto

    ํŠธ์œ„ํ„ฐ๋Š” ์†Œ์…œ ๋„คํŠธ์›Œํฌ์ธ๊ฐ€? - ๋„คํŠธ์›Œํฌ ๊ตฌ์กฐ์™€ ์ •๋ณด ์ „ํŒŒ์˜ ๊ด€์ 

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    ์ด ์—ฐ๊ตฌ์—์„œ๋Š” 4100๋งŒ๋ช… ์ด์ƒ์˜ ํŠธ์œ„ํ„ฐ ์‚ฌ์šฉ์ž ์ •๋ณด์™€ 14์–ต 7์ฒœ๋งŒ๊ฐœ์˜ ํŒ”๋กœ(Follow) ๊ด€๊ณ„, ๊ทธ๋ฆฌ๊ณ  ์‚ฌ์šฉ์ž๋“ค์ด ๋‚จ๊ธด 1์–ต๊ฐœ ์ด์ƒ์˜ ํŠธ์œ—๋“ค์„ ์ˆ˜์ง‘, ๋ถ„์„ํ•˜์—ฌ ํŠธ์œ„ํ„ฐ ์‚ฌ์šฉ์ž ๋„คํŠธ์›Œํฌ์˜ ๊ตฌ์กฐ์  ํŠน์„ฑ๊ณผ ์ •๋ณด ์ „ํŒŒ์˜ ํŠน์„ฑ์„ ๋ถ„์„ํ•˜์˜€๋‹ค. ๋‹จ๋ฐฉํ–ฅ ํŒ”๋กœ ๊ด€๊ณ„์˜ ์ƒํ˜ธ์„ฑ(Reciprocity)์€ ์ผ๋ฐ˜์ ์ธ ์‚ฌํšŒ์  ๊ด€๊ณ„ ๋ฐ ์—ฌํƒ€ ์˜จ๋ผ์ธ ์†Œ์…œ ๋„คํŠธ์›Œํฌ์—์„œ ๊ด€์ฐฐ๋˜๋Š” ์ƒํ˜ธ์„ฑ๋ณด๋‹ค ์ƒ๋‹นํžˆ ๋‚ฎ์€ 22.1%๋กœ ๊ด€์ฐฐ๋˜์—ˆ์œผ๋ฉฐ ์ด๋Š” ํŠธ์œ„ํ„ฐ์—์„œ์˜ ํŒ”๋กœ ๊ด€๊ณ„๊ฐ€ ์นœ๋ฐ€ํ•œ ์‚ฌํšŒ์  ๊ด€๊ณ„์— ๊ธฐ๋ฐ˜ํ•˜๊ณ  ์žˆ์ง€๋งŒ์€ ์•Š๋‹ค๋Š” ๊ฒƒ์„ ๋ณด์—ฌ์ค€๋‹ค. ํŠธ์œ„ํ„ฐ์˜ ์‚ฌ์šฉ์ž๋“ค์€ ์˜คํ”„๋ผ์ธ์—์„œ์˜ ์ด์Šˆ์™€ ๊ด€๋ จ๋œ ํŠธ์œ—๋“ค์„ ํ™œ๋ฐœํžˆ ๊ธฐ๋กํ•˜๋ฉฐ, ๋งŽ์€ ํŒ”๋กœ์–ด๋ฅผ ๊ฐ–๋Š” ํ—ˆ๋ธŒ(Hub) ์‚ฌ์šฉ์ž๋“ค์ด ํ’๋ถ€ํ•˜๊ฒŒ ์กด์žฌํ•˜๊ณ , ํŒ”๋กœ์–ด๋ฅผ ๋งŽ์ด ๊ฐ–์ง€ ๋ชปํ•œ ์‚ฌ์šฉ์ž๋“ค๋„ ๋ฆฌํŠธ์œ—์„ ํ†ตํ•ด ์ •๋ณด๋ฅผ ๋น ๋ฅด๊ณ  ๋„“๊ฒŒ ํผ๋œจ๋ฆด ์ˆ˜ ์žˆ๋‹ค๋Š” ๊ฒƒ์„ ์ •๋Ÿ‰์ ์œผ๋กœ ๋ฐํ˜”๋‹ค. ๊ด€๊ณ„์˜ ๋‹จ๋ฐฉํ–ฅ์„ฑ๊ณผ ๋‚ฎ์€ ์ƒํ˜ธ์„ฑ, ๊ทธ๋ฆฌ๊ณ  ๋น ๋ฅด๊ณ  ๋„“์€ ์ •๋ณด ์ „ํŒŒ๋Š” ๋‹ค๋ฅธ ์†Œ์…œ ๋„คํŠธ์›Œํฌ ์„œ๋น„์Šค์—์„œ๋Š” ์ฐพ์•„๋ณผ ์ˆ˜ ์—†๋Š” ํŠธ์œ„ํ„ฐ์˜ ๊ณ ์œ ํ•œ ํŠน์„ฑ์œผ๋กœ์„œ, ์ƒˆ๋กœ์šด ์ •๋ณด ์ „ํŒŒ ๋งค์ฒด๋กœ์„œ์˜ ๊ฐ€๋Šฅ์„ฑ์„ ๋ณด์—ฌ์ค€๋‹ค

    A Study on Spatial Detection Methodology for Influentials Information in LBSNS using Spatial Statistical Analysis Methods

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ฑด์„คํ™˜๊ฒฝ๊ณตํ•™๋ถ€, 2014. 2. ์œ ๊ธฐ์œค.์ตœ๊ทผ ๋‹ค์–‘ํ•œ ์†Œ์…œ ๋ฏธ๋””์–ด(social media)์˜ ํ™œ์„ฑํ™”๋กœ ์ธํ•ด ์†Œ์…œ ๋„คํŠธ์›Œํฌ(social network)์ƒ์—์„œ ์ˆ˜๋งŽ์€ ์ž๋ฐœ์  ์ง€์ง€์ž๋“ค์„ ํ™•๋ณดํ•œ ์ƒˆ๋กœ์šด ํ˜•ํƒœ์˜ ์œ ๋ ฅ์ž(influential)๊ฐ€ ๋Œ€๋‘๋˜๊ณ  ์žˆ๋‹ค. ์ด๋Ÿฌํ•œ ์†Œ์…œ ๋„คํŠธ์›Œํฌ์ƒ์—์„œ์˜ ์œ ๋ ฅ์ž๋ฅผ ํƒ์ƒ‰ํ•˜๋Š” ์—ฐ๊ตฌ๋“ค์ด ์ง„ํ–‰๋˜์–ด ์™”๊ณ , ๊ด€๋ จ ์„œ๋น„์Šค๊ฐ€ ์ œ๊ณต ์ค‘์— ์žˆ์œผ๋‚˜ ์ด๋“ค์€ ์œ ๋ ฅ์ž ๊ทœ๋ช…์— ์žˆ์–ด LBSNS(Location Based Social Network Service)๊ฐ€ ๊ฐ€์ง€๊ณ  ์žˆ๋Š” ์œ„์น˜ ์ •๋ณด์— ๋Œ€ํ•œ ๋ฐ˜์˜์ด ๋ถ€์กฑํ•˜๋‹ค๋Š” ํ•œ๊ณ„์ ์„ ๊ฐ€์ง€๊ณ  ์žˆ์—ˆ๋‹ค. ์ด์— ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๊ณต๊ฐ„ํ†ต๊ณ„๋ถ„์„๊ธฐ๋ฒ•์„ ์ด์šฉํ•˜์—ฌ LBSNS ๋ฐ์ดํ„ฐ๋ฅผ ๋Œ€์ƒ์œผ๋กœ ๋‹ค์–‘ํ•œ ์‚ฌํšŒ๊ฒฝ์ œ์  ์ด์Šˆ์— ๋Œ€ํ•œ ๋ฐœ์–ธ์— ์˜ํ–ฅ๋ ฅ์„ ๊ฐ€์ง€๋Š” ์œ ๋ ฅ์ž๋ฅผ ๊ณต๊ฐ„์ ์œผ๋กœ ํƒ์ƒ‰ํ•˜๊ณ , ์ด๋ฅผ ํ™œ์šฉํ•˜๋Š” ๋ฐฉ์•ˆ์„ ์ œ์‹œํ•˜๊ณ ์ž ํ•œ๋‹ค. ์ด๋ฅผ ์œ„ํ•ด ํ˜„์žฌ ์ƒ์šฉ ์ค‘์ธ ์—ฌ๋Ÿฌ ์ข…๋ฅ˜์˜ LBSNS ์ค‘ ํŠธ์œ„ํ„ฐ(Twitter)๋ฅผ ๋ถ„์„ ๋Œ€์ƒ ๋ฐ์ดํ„ฐ๋กœ ์‚ฌ์šฉํ•˜์˜€์œผ๋ฉฐ, ์„œ์šธ์‹œ๋ฅผ ๊ณต๊ฐ„์  ๋ฒ”์œ„๋กœ ํ•˜์—ฌ ํ•œ ๋‹ฌ ๋™์•ˆ ์ด 168,040๊ฑด์˜ ์œ„์น˜ ์ •๋ณด๊ฐ€ ํฌํ•จ๋œ ํŠธ์œ„ํ„ฐ ๋ฉ”์‹œ์ง€๋ฅผ ์ˆ˜์ง‘ํ•˜์˜€๋‹ค. ๋˜ํ•œ ๊ธฐ์กด์˜ ๋‰ด์Šค ๋ถ„๋ฅ˜ ์ฒด๊ณ„๋ฅผ ์ฐจ์šฉํ•˜์—ฌ ์ •์น˜, ๊ฒฝ์ œ, IT๋ฅผ ์—ฐ๊ตฌ ๋Œ€์ƒ ๋ฒ”์ฃผ๋กœ ์„ค์ •ํ•˜๊ณ , ๋ฐ์ดํ„ฐ ์ˆ˜์ง‘ ๊ธฐ๊ฐ„ ๋™์•ˆ ์ด์Šˆ๊ฐ€ ๋˜์—ˆ๋˜ ํ‚ค์›Œ๋“œ๋“ค์„ ์ฃผ์–ด์ง„ ๋ฒ”์ฃผ๋ณ„๋กœ ๋ถ„๋ฅ˜ํ•˜์˜€๋‹ค. ์ด๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ํ‚ค์›Œ๋“œ์— ๋Œ€ํ•œ ์œ ๋ ฅ์ž๋ฅผ ํŒŒ์•…ํ•˜๊ธฐ ์œ„ํ•œ ๋…ธ์ถœ๋„(exposure index)๋ฅผ ๋„์ถœํ•˜๊ณ , ์ด์— ๋Œ€ํ•ด ์„œ์šธ์‹œ์˜ ํ–‰์ •๋™์„ ๊ธฐ์ค€์œผ๋กœ ๊ณต๊ฐ„ ์กฐ์ธ(spatial join) ์—ฐ์‚ฐ์„ ์‹ค์‹œํ•จ์œผ๋กœ์จ ๊ฐ ํ‚ค์›Œ๋“œ์— ๋Œ€ํ•œ ํ–‰์ •๋™๋ณ„ ๋…ธ์ถœ๋„๋ฅผ ์‚ฐ์ถœํ•˜์˜€๋‹ค. ๊ทธ๋ฆฌ๊ณ  ํ–‰์ •๋™๋ณ„๋กœ ์‚ฐ์ถœ๋œ ๋…ธ์ถœ๋„์˜ ๊ณต๊ฐ„์  ์˜์กด์„ฑ(spatial dependence)์„ ๊ณ ๋ คํ•˜์—ฌ ์œ ๋ ฅ์ง€์ˆ˜(influential index)๋ฅผ ๋„์ถœํ•˜์˜€์œผ๋ฉฐ, ํ‚ค์›Œ๋“œ๋ณ„๋กœ ์ƒ์œ„์˜ ์œ ๋ ฅ์ง€์ˆ˜๋ฅผ ๋ณด์ด๋Š” ์ง€์—ญ์„ ์œ ๋ ฅ์ง€์—ญ(influential area)์œผ๋กœ ์ถ”์ถœํ•˜์—ฌ ์ด๋“ค์˜ ๊ณต๊ฐ„์ ์ธ ๋ถ„ํฌ ํŠน์„ฑ๊ณผ ํ‚ค์›Œ๋“œ๋“ค ๊ฐ„์˜ ๊ณต๊ฐ„์  ์ƒ๊ด€์„ฑ์„ ๋ถ„์„ํ•˜์˜€๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ ์—ฐ๊ตฌ ์„ฑ๊ณผ์— ๋Œ€ํ•œ ํ™œ์šฉ ๋ฐฉ์•ˆ์œผ๋กœ์„œ ๋‹ค์–‘ํ•œ ์‚ฌํšŒ๊ฒฝ์ œ์  ์ด์Šˆ์— ๋Œ€ํ•ด ์ „๋ฌธ์ ์ธ ๋ถ„์„์„ ์ œ๊ณตํ•˜๋Š” ์œ ๋ ฅ์ž ๋ถ„ํฌ ์ง€๋„ ๊ฒ€์ƒ‰ ์„œ๋น„์Šค๋ฅผ ์ œ์•ˆํ•˜์˜€๋‹ค.1. ์„œ๋ก  1 1.1. ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋ชฉ์  1 1.2. ์—ฐ๊ตฌ ๋ฒ”์œ„ ๋ฐ ๋ฐฉ๋ฒ• 3 2. ์ด๋ก ์  ๋…ผ์˜ ๋ฐ ๊ด€๋ จ ์—ฐ๊ตฌ 6 2.1. LBSNS 6 2.1.1. LBSNS์˜ ๊ฐœ๋… ๋ฐ ํŠน์ง• 6 2.1.2. LBSNS ๊ด€๋ จ ์—ฐ๊ตฌ 8 2.2. ์œ ๋ ฅ์ž 8 2.2.1. ์œ ๋ ฅ์ž์˜ ๊ฐœ๋… ๋ฐ ํŠน์ง• 8 2.2.2. ์œ ๋ ฅ์ž ๊ด€๋ จ ์—ฐ๊ตฌ 12 3. ์œ ๋ ฅ์ž ์ •๋ณด์˜ ๊ณต๊ฐ„์  ํƒ์ƒ‰๊ธฐ๋ฒ• 21 3.1. ๋ฐ์ดํ„ฐ ์ˆ˜์ง‘ ๋ฐ ์ „์ฒ˜๋ฆฌ ๊ณผ์ • 21 3.1.1. ๋ฐ์ดํ„ฐ ์ˆ˜์ง‘ 21 3.1.2. ๋ฐ์ดํ„ฐ ์ „์ฒ˜๋ฆฌ 23 3.2. ๊ณต๊ฐ„ ์กฐ์ธ ์—ฐ์‚ฐ์„ ์ด์šฉํ•œ ํ–‰์ •๋™๋ณ„ ๋…ธ์ถœ๋„ ์„ค์ • 24 3.3. Local Morans I๋ฅผ ์ด์šฉํ•œ ์œ ๋ ฅ์ง€์ˆ˜ ์„ค์ • 25 3.4. Spatial Lag Pearsons r์„ ์ด์šฉํ•œ ๊ณต๊ฐ„์  ์ƒ๊ด€์„ฑ ๋ถ„์„ 29 4. ์‹คํ—˜ ์ ์šฉ ๋ฐ ๊ฒฐ๊ณผ 32 4.1. ํ‚ค์›Œ๋“œ๋ณ„ ์œ ๋ ฅ์ง€์ˆ˜ ์‚ฐ์ถœ ๋ฐ ์œ ๋ ฅ์ง€์—ญ ์ถ”์ถœ 32 4.1.1. ์ •์น˜ ๋ฒ”์ฃผ ํ‚ค์›Œ๋“œ 32 4.1.2. ๊ฒฝ์ œ ๋ฒ”์ฃผ ํ‚ค์›Œ๋“œ 37 4.1.3. IT ๋ฒ”์ฃผ ํ‚ค์›Œ๋“œ 41 4.2. ๋ฒ”์ฃผ๋ณ„ ์œ ๋ ฅ์ง€์ˆ˜ ์‚ฐ์ถœ ๋ฐ ์œ ๋ ฅ์ง€์—ญ ์ถ”์ถœ 47 4.3. ์œ ๋ ฅ์ง€์—ญ ๊ฐ„ ๊ณต๊ฐ„์  ์ƒ๊ด€์„ฑ ์ธก์ • 53 5. ์œ ๋ ฅ์ž ๋ถ„ํฌ ์ง€๋„ ๊ฒ€์ƒ‰ ์„œ๋น„์Šค ์ œ์•ˆ 55 6. ๊ฒฐ๋ก  64Maste

    Hierarchical Market Structures on Internet Search Portal

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    Internet search services hold the key to helping consumers to access and sort the wealth of information. As new search Providers enter successively, they are trying to take a competitive advantage in search service market. We estimated the structure of search service market to regard the degree of overlap among search engines' results as a similarity measure. This study identified a market structure in the level of the whole URLs and different structures in the level of search keywords. In conclusion, these models could be a useful tool to aid in developing tool to in developing search service strategy formulation.๋ชฉ์ฐจ ์ œ1์žฅ ์„œ๋ก  = 1 ์ œ2์žฅ ๊ฒ€์ƒ‰ํฌํƒˆ์˜ ์ •์˜ = 4 ์ œ3์žฅ ์ด๋ก ์  ๊ณ ์ฐฐ = 6 3.1. ์ธํ„ฐ๋„ท ๊ฒ€์ƒ‰ํฌํƒˆ์— ๊ด€ํ•œ ์„ ํ–‰์—ฐ๊ตฌ = 6 3.2. ์‹œ์žฅ๊ตฌ์กฐ๋ถ„์„์— ๊ด€ํ•œ ์„ ํ–‰์—ฐ๊ตฌ = 8 ์ œ4์žฅ ์—ฐ๊ตฌ๋ชจํ˜• = 10 4.1. ๊ฒ€์ƒ‰ํฌํƒˆ์˜ ์‹œ์žฅ๊ตฌ์กฐ๋ถ„์„๋ชจํ˜• = 10 4.2. ์˜ˆ์ œ ๋ชจํ˜• = 14 4.3. ๋ชจํ˜•์˜ ํ™•์žฅ = 18 4.4. ๋ถ„์„์ ˆ์ฐจ = 20 ์ œ5์žฅ ์‹ค์ฆ๋ถ„์„ = 21 5.1. ํ‚ค์›Œ๋“œ(Key-Word)์™€ ๊ฒ€์ƒ‰ํฌํƒˆ์˜ ์„ ์ • = 22 5.2. ์ž๋ฃŒ ์ˆ˜์ง‘ ๋ฐ ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค๊ตฌ์ถ• = 23 ์ œ6์žฅ ๊ฒฐ๋ก  ๋ฐ ๋ฏธ๋ž˜ ์—ฐ๊ตฌ ๋ฐฉํ–ฅ = 31 ์ฐธ๊ณ ๋ฌธํ—Œ = 3

    Changes in spatial equity of the Korean intercity rail network in the 2010s

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ํ™˜๊ฒฝ๋Œ€ํ•™์› ํ™˜๊ฒฝ๊ณ„ํšํ•™๊ณผ, 2023. 2. ์žฅ์ˆ˜์€.Korea has supplied railway networks mainly to the Gyeongbu axis for a long time. Cost-efficiency was the predominant rationale for the supply. However, the governmental policy of rail supply has changed to support balanced regional developments. In order to build a railway network based on regional equity, the supply level of the current system should be assessed. Although there are many studies that have dealt with economic changes before and after the opening of high-speed rail, few studies have explored equity changes over the past 10 years. In addition, previous studies mainly measured spatial equity based on socio-economic factors such as land use and population. Since there is an interaction between the structure of the railway network and the travel patterns of people, this study focuses on the characteristics of the transportation system, which is the fundamental cause of regional imbalance. Thus, this research assesses the supply and demand of the current state of rail service for each region using the network analysis. The extent of the inequality was analyzed spatially. A node centrality analysis as one of the methodologies of network analysis was used to reflect the properties of the transportation system. For the centrality index, the strength centrality, betweenness centrality, and closeness centrality, and each index represents connectivity, transferability, and mobility, respectively. The change in spatial equality was analyzed based on the centrality value derived for each region. For this purpose, non-spatial statists and spatial statistics were both used. The changes in rail service unbalance in South Korea over 10 years were identified through the Gini coefficient, and the spatial distribution was observed based on spatial autocorrelation. The Local Morans I index was used as the spatial autocorrelation analysis. The concentrated extent of supply and demand of regions were analyzed. Also, the correlation between supply and demand was determined through the bivariate Moran index. The following summarizes the main results of this paper. First, Gini coefficient analysis results shows that the degree centrality and betweenness centrality values were both 0.8 before and after, and the closeness centrality was about 0.6. It could be identified from the Gini coefficient being between 0 and 1 that the railway service levels of regions were unbalanced in South Korea. Despite the expansion of the rail infrastructure during the past 10 years. the spatial balance had not improved. This also proves that the improvement in spatial balance is not easy only through physical railroad line supply. Secondly, the Moran index analysis shows that the supply level and demand of railway service remains significantly higher in the Gyeongbuk and Chungcheong regions than in other regions in 2011 and 2020. In particular, the betweenness centrality analysis that identifies the transfer hubs shows that the impact of the Chungcheong region had increased over the past 10 years. Transfer hubs refer to nodes with significant control within transportation networks. Therefore, a node which has higher betweenness centrality value could incur problems for the entire network when the corresponding region have falls under danger. On the contrary, the railway supply state and demand of the north of Gyeonggi-do, Jeollanam-do, and some areas of Gangwon-do were low. The results of this study can be used during investment decision-making for the balanced development of regions in the future. In particular, regions with low supply levels relative to the demand can be sorted as investment-priority regions. However, the impact of the Seoul metropolitan region could have been underestimated due to the elimination of the metro from the scope of analysis. Additionally, a complementary method must be considered for the actual balanced development of regions because the land properties within each region differ.๊ตํ†ต ์„œ๋น„์Šค๋Š” ์ง€์—ญ์˜ ์ ‘๊ทผ์„ฑ์„ ํ–ฅ์ƒ์‹œํ‚ค๋ฉฐ, ์‚ฌ๋žŒ๋“ค์˜ ๊ฒฝ์ œํ™œ๋™ ๋ฐ ์‚ถ์˜ ์งˆ์— ์ง๊ฐ„์ ‘์ ์œผ๋กœ ์˜ํ–ฅ์„ ๋ฏธ์นœ๋‹ค. ํŠนํžˆ ์ •์‹œ์„ฑ, ์นœํ™˜๊ฒฝ์„ฑ ๋“ฑ์˜ ํŠน์„ฑ์„ ์ง€๋‹ˆ๋Š” ์ฒ ๋„๋Š” ์ง€์†๊ฐ€๋Šฅํ•œ ๊ตํ†ต์ฒด๊ณ„ ๋ฐ ์ง€์—ญ๊ท ํ˜•๋ฐœ์ „์„ ์œ„ํ•œ ์ •์ฑ…์˜ ์ผํ™˜์œผ๋กœ ์ฃผ๋ชฉ๋ฐ›๊ณ  ์žˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์šฐ๋ฆฌ๋‚˜๋ผ๋Š” ์˜ค๋žœ ๊ธฐ๊ฐ„๋™์•ˆ ๋Œ€๋„์‹œ์™€ ๊ฒฝ๋ถ€์ถ•์„ ์ค‘์‹ฌ์œผ๋กœ ํšจ์œจ์„ฑ ์œ„์ฃผ์˜ ์ฒ ๋„๋ง์ด ๊ณต๊ธ‰๋˜์–ด์™”๋‹ค. ํ•˜์ง€๋งŒ ์ตœ๊ทผ ๋“ค์–ด ใ€Œ์ œ4์ฐจ ๊ตญ๊ฐ€์ฒ ๋„๋ง ๊ตฌ์ถ•๊ณ„ํš ใ€๋“ฑ์—์„œ ์•Œ ์ˆ˜ ์žˆ๋“ฏ์ด, ์ฒ ๋„๋ง ๊ตฌ์ถ•์˜ ๋ชฉํ‘œ๊ฐ€ ๊ท ํ˜•์  ๊ณต๊ธ‰์„ ๊ณ ๋ คํ•˜๋Š” ๋ฐฉํ–ฅ์œผ๋กœ ๋ณ€ํ™”ํ•˜๊ณ  ์žˆ๋‹ค. ํ–ฅํ›„ ์ง€์—ญ ํ˜•ํ‰์„ฑ ๊ธฐ๋ฐ˜์˜ ์ฒ ๋„ ์„œ๋น„์Šค ๊ตฌ์ถ•์„ ์œ„ํ•ด์„œ๋Š” ํ˜„์žฌ ์ฒ ๋„ ๋„คํŠธ์›Œํฌ์˜ ๊ณต๊ธ‰ ์ˆ˜์ค€์„ ๋ถ„์„ํ•  ํ•„์š”๊ฐ€ ์žˆ๋‹ค. ๊ณ ์†์ฒ ๋„ ๊ฐœํ†ต ์ „ํ›„์˜ ๋„์‹œ ๊ฒฝ์ œ ๋ณ€ํ™” ๋“ฑ์„ ๋‹ค๋ฃฌ ์—ฐ๊ตฌ๋“ค์€ ๋งŽ์œผ๋‚˜, ์ตœ๊ทผ 10๋…„๊ฐ„ ์ฒ ๋„๋„คํŠธ์›Œํฌ ๊ตฌ์กฐ ๋ณ€ํ™”์— ๋Œ€ํ•œ ํ˜•ํ‰์„ฑ ๋ณ€ํ™”๋ฅผ ๋‹ค๋ฃฌ ์—ฐ๊ตฌ๋Š” ๋ถ€์กฑํ•˜๋‹ค. ๋˜ํ•œ ๊ธฐ์กด ์—ฐ๊ตฌ๋“ค์€ ์ฃผ๋กœ ํ† ์ง€์ด์šฉ์ด๋‚˜ ์ธ๊ตฌ์™€ ๊ฐ™์€ ์‚ฌํšŒ๊ฒฝ์ œ์  ์š”์ธ์„ ๋ฐ”ํƒ•์œผ๋กœ ํ˜•ํ‰์„ฑ์„ ํ‰๊ฐ€ํ•˜๊ณ  ์žˆ๋‹ค. ์ฒ ๋„ ๋„คํŠธ์›Œํฌ์˜ ๊ตฌ์กฐ์™€ ์‚ฌ๋žŒ๋“ค์˜ ์ด๋™ ํŒจํ„ด ์‚ฌ์ด์—๋Š” ์ƒํ˜ธ์ž‘์šฉ์ด ์กด์žฌํ•˜๊ธฐ ๋•Œ๋ฌธ์— ์ง€์—ญ ๋ถˆ๊ท ํ˜•์˜ ๊ทผ๋ณธ์  ์›์ธ์ธ ๊ตํ†ต์ฒด๊ณ„์˜ ํŠน์„ฑ์— ์ดˆ์ ์„ ๋งž์ถœ ํ•„์š”๊ฐ€ ์žˆ๋‹ค. ์ด์— ๋ณธ ์—ฐ๊ตฌ๋Š” ๋„คํŠธ์›Œํฌ ๋ถ„์„๋ฒ•์„ ํ™œ์šฉํ•˜์—ฌ ์ง€์—ญ ๊ฐ„ ์ฒ ๋„์˜ ๊ณต๊ธ‰ ์ˆ˜์ค€ ๋ฐ ์ˆ˜์š”๋ฅผ ํ‰๊ฐ€ํ•˜๊ณ  ์ด๊ฒƒ์ด ๊ณต๊ฐ„์ ์œผ๋กœ ์–ด๋–ป๊ฒŒ ๋ถˆ๊ท ํ˜•์ ์œผ๋กœ ๋ถ„ํฌํ•˜๊ณ  ์žˆ๋Š”์ง€ ๋ถ„์„ํ•˜์˜€๋‹ค. ๊ตํ†ต์ฒด๊ณ„์˜ ํŠน์„ฑ์„ ๋ฐ˜์˜ํ•˜๊ธฐ ์œ„ํ•ด ๋„คํŠธ์›Œํฌ ๋ถ„์„ ๋ฐฉ๋ฒ•๋ก  ์ค‘ ํ•˜๋‚˜์ธ ๋…ธ๋“œ ์ค‘์‹ฌ์„ฑ ๋ถ„์„์„ ํ™œ์šฉํ•˜์˜€๋‹ค. ์ค‘์‹ฌ์„ฑ ์ง€ํ‘œ๋Š” ์—ฐ๊ฒฐ๊ฐ•๋„ ์ค‘์‹ฌ์„ฑ, ๋งค๊ฐœ ์ค‘์‹ฌ์„ฑ, ๊ทผ์ ‘ ์ค‘์‹ฌ์„ฑ์„ ํ™œ์šฉํ•˜์˜€์œผ๋ฉฐ, ์„ธ ์ง€ํ‘œ๋Š” ๊ฐ๊ฐ ์—ฐ๊ฒฐ์„ฑ(connectivity), ๋งค๊ฐœ์„ฑ(transferability), ๊ทธ๋ฆฌ๊ณ  ์ด๋™์„ฑ(mobility)์„ ๋‚˜ํƒ€๋‚ธ๋‹ค. ๋„์ถœ๋œ ์ง€์—ญ๋ณ„ ์ค‘์‹ฌ์„ฑ ๊ฐ’์„ ๋ฐ”ํƒ•์œผ๋กœ ๊ณต๊ฐ„์  ํ˜•ํ‰์„ฑ์˜ ๋ณ€ํ™”๋ฅผ ๋ถ„์„ํ•˜์˜€๋‹ค. ํ˜•ํ‰์„ฑ ๋ถ„์„์€ ๋น„๊ณต๊ฐ„ ํ†ต๊ณ„๋Ÿ‰๊ณผ ๊ณต๊ฐ„ ํ†ต๊ณ„๋Ÿ‰ ๋ชจ๋‘๋ฅผ ํ™œ์šฉํ•˜์˜€๋‹ค. ์ง€๋‹ˆ๊ณ„์ˆ˜๋ฅผ ํ†ตํ•ด 10๋…„๊ฐ„์˜ ์ „๋ฐ˜์ ์ธ ์šฐ๋ฆฌ๋‚˜๋ผ ์ฒ ๋„ ์„œ๋น„์Šค ๋ถˆ๊ท ํ˜• ์ถ”์ด๋ฅผ ํŒŒ์•…ํ•˜๊ณ , ๊ณต๊ฐ„์  ์ž๊ธฐ์ƒ๊ด€(spatial autocorrelation)์„ ๋ฐ”ํƒ•์œผ๋กœ ๊ณต๊ฐ„์  ๋ถ„ํฌ๋ฅผ ์‚ดํŽด๋ณด์•˜๋‹ค. ๊ณต๊ฐ„ ์ž๊ธฐ์ƒ๊ด€ ๋ถ„์„๋ฒ•์€ Anselin์˜ Local Moran's I ์ง€์ˆ˜๋ฅผ ํ™œ์šฉํ•˜์˜€์œผ๋ฉฐ, ๊ณต๊ธ‰์ˆ˜์ค€๊ณผ ์ˆ˜์š” ๊ฐ๊ฐ์˜ ๊ณต๊ฐ„ ์ง‘์ค‘์ •๋„ ๋ฟ ์•„๋‹ˆ๋ผ ์ด๋ณ€๋Ÿ‰ ๋ชจ๋ž€์ง€์ˆ˜๋ฅผ ํ†ตํ•ด ๊ณต๊ธ‰๊ณผ ์ˆ˜์š”์˜ ์ƒ๊ด€๊ด€๊ณ„ ๋˜ํ•œ ์ธก์ •ํ–ˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์˜ ์ฃผ์š” ๊ฒฐ๊ณผ๋ฅผ ์ •๋ฆฌํ•˜๋ฉด ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. ์ฒซ์งธ, ์ง€๋‹ˆ๊ณ„์ˆ˜ ๋ถ„์„ ๊ฒฐ๊ณผ, 2011๋…„๊ณผ 2020๋…„์˜ ์—ฐ๊ฒฐ๊ฐ•๋„ ์ค‘์‹ฌ์„ฑ๊ณผ ๋งค๊ฐœ ์ค‘์‹ฌ์„ฑ์€ 0.8 ์ „ํ›„, ๊ทผ์ ‘ ์ค‘์‹ฌ์„ฑ์€ 0.6 ์ •๋„์˜ ๊ฐ’์œผ๋กœ ๋„์ถœ๋˜์—ˆ๋‹ค. ์ง€๋‹ˆ๊ณ„์ˆ˜๊ฐ€ 0๊ณผ 1์‚ฌ์ด ๊ฐ’์„ ๊ฐ€์ง€๋Š” ๊ฒƒ์œผ๋กœ ๋ฏธ๋ฃจ์–ด ๋ณด์•˜์„ ๋•Œ ์šฐ๋ฆฌ๋‚˜๋ผ์˜ ์ง€์—ญ ๊ฐ„ ์ฒ ๋„ ๊ณต๊ธ‰ ์ˆ˜์ค€๊ณผ ์ˆ˜์š”๋Š” ๋‹ค์†Œ ๋ถˆ๊ท ํ˜•์ ์ž„์„ ์•Œ ์ˆ˜ ์žˆ๋‹ค. ์ตœ๊ทผ 10๋…„๋™์•ˆ ๋‹ค์ถ•์œผ๋กœ ์ฒ ๋„๋ง์ด ํ™•์žฅ๋˜์—ˆ์Œ์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ  ๊ณต๊ฐ„์  ํ˜•ํ‰์„ฑ์€ ๊ฐœ์„ ๋˜์ง€ ์•Š์€ ๊ฒƒ์ด๋‹ค. ๋ฌผ๋ฆฌ์ ์ธ ์ฒ ๋„ ๋…ธ์„  ๊ณต๊ธ‰๋งŒ์œผ๋กœ๋Š” ๊ณต๊ฐ„์  ํ˜•ํ‰์„ฑ์ด ์‰ฝ๊ฒŒ ๊ฐœ์„ ๋˜์ง€ ์•Š๋Š”๋‹ค๋Š” ๊ฒƒ์„ ํ™•์ธํ•  ์ˆ˜ ์žˆ๋Š” ๋Œ€๋ชฉ์ด๋‹ค. ๋‘˜์งธ, ๊ตญ์ง€์  ๋ชจ๋ž€์ง€์ˆ˜๋ฅผ ๋ถ„์„ํ•œ ๊ฒฐ๊ณผ, 2011๋…„๊ณผ 2020๋…„ ๋ชจ๋‘ ๊ฒฝ๋ถ๊ถŒ๊ณผ ์ถฉ์ฒญ๊ถŒ์˜ ์ฒ ๋„ ์„œ๋น„์Šค ์ˆ˜์ค€์ด ์—ฌ์ „ํžˆ ๋‹ค๋ฅธ ์ง€์—ญ์— ๋น„ํ•ด ๋งค์šฐ ๋†’์€ ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ํŠนํžˆ ํ™˜์Šน ํ—ˆ๋ธŒ๋ฅผ ์‹๋ณ„ํ•˜๋Š” ๋งค๊ฐœ์ค‘์‹ฌ์„ฑ์„ ๋ถ„์„ํ•œ ๊ฒฐ๊ณผ, ์ตœ๊ทผ 10๋…„๋™์•ˆ ์ถฉ์ฒญ๊ถŒ์˜ ์˜ํ–ฅ๋ ฅ์ด ๋”์šฑ ์ฆ๊ฐ€ํ•˜์˜€์Œ์„ ์•Œ ์ˆ˜ ์žˆ๋‹ค. ํ™˜์Šน ํ—ˆ๋ธŒ๋Š” ๋„คํŠธ์›Œํฌ ๋‚ด์—์„œ ํ†ต์ œ๋ ฅ์ด ๊ฐ•ํ•œ ๋…ธ๋“œ๋ฅผ ์˜๋ฏธํ•œ๋‹ค. ๋”ฐ๋ผ์„œ ํŠน์ • ๋…ธ๋“œ์˜ ๋งค๊ฐœ์„ฑ์ด ๊ฐ•ํ•ด์ง€๋ฉด ํ•ด๋‹น ์ง€์—ญ์— ์œ„ํ—˜์ด ๋ฐœ์ƒํ–ˆ์„ ๋•Œ ์ „์ฒด ๋„คํŠธ์›Œํฌ์— ๋ฌธ์ œ๊ฐ€ ๋ฐœ์ƒํ•  ์ˆ˜ ์žˆ๋‹ค. ๋ฐ˜๋ฉด, ๊ฒฝ๊ธฐ๋ถ๋ถ€, ์ „๋ผ๋‚จ๋„ ๋ฐ ์ผ๋ถ€ ๊ฐ•์›๊ถŒ์˜ ์ฒ ๋„ ์„œ๋น„์Šค ์ˆ˜์ค€๊ณผ ์ˆ˜์š”๊ฐ€ ๋‚ฎ์€ ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์˜ ๊ฒฐ๊ณผ๋Š” ํ–ฅํ›„ ์ง€์—ญ๊ท ํ˜•๋ฐœ์ „์„ ์œ„ํ•œ ์ฒ ๋„ ํˆฌ์ž ๋ฐฉํ–ฅ์„ ์„ค์ •ํ•  ๋•Œ ํ™œ์šฉ๋  ์ˆ˜ ์žˆ๋‹ค. ํŠนํžˆ ์ˆ˜์š”์— ๋น„ํ•ด ๊ณต๊ธ‰ ์ˆ˜์ค€์ด ๋‚ฎ์€ ์ง€์—ญ์„ ์šฐ์„  ํˆฌ์ž๋˜์–ด์•ผ ํ•˜๋Š” ์ง€์—ญ์œผ๋กœ ์„ ๋ณ„ํ•  ์ˆ˜ ์žˆ๋‹ค. ๋‹ค๋งŒ, ๊ด‘์—ญ์ฒ ๋„์™€ ๋„์‹œ์ฒ ๋„๋ฅผ ๋ถ„์„ ๋ฒ”์œ„์—์„œ ์ œ์™ธํ•˜์˜€๊ธฐ ๋•Œ๋ฌธ์— ์ˆ˜๋„๊ถŒ์˜ ์˜ํ–ฅ๋ ฅ์ด ๊ณผ์†Œํ‰๊ฐ€๋˜์—ˆ์„ ์ˆ˜ ์žˆ๋‹ค. ๋˜ํ•œ ์ง€์—ญ ๋‚ด์˜ ํ† ์ง€ํŠน์„ฑ ๋“ฑ์ด ๋‹ค๋ฅด๋ฏ€๋กœ ์‹ค์ œ ์ง€์—ญ๊ท ํ˜•๋ฐœ์ „์„ ์œ„ํ•ด์„œ๋Š” ๋ณด์™„์  ํƒ€์ˆ˜๋‹จ์„ ํ•จ๊ป˜ ๊ณ ๋ คํ•˜์—ฌ์•ผ ํ•œ๋‹ค.์ œ 1 ์žฅ ์„œ๋ก  1 ์ œ 1 ์ ˆ ์—ฐ๊ตฌ์˜ ๋ฐฐ๊ฒฝ ๋ฐ ๋ชฉ์  1 ์ œ 2 ์ ˆ ์—ฐ๊ตฌ์˜ ๋ฒ”์œ„ ๋ฐ ๋ฐฉ๋ฒ• 2 1. ์‹œ๊ณต๊ฐ„์  ๋ฒ”์œ„ 2 2. ์ฒ ๋„ ๋ถ„์„ ๋ฒ”์œ„ 2 3. ์—ฐ๊ตฌ์˜ ๋ฐฉ๋ฒ• 6 ์ œ 2 ์žฅ ์ด๋ก  ๋ฐ ๊ธฐ์กด ์—ฐ๊ตฌ ๊ณ ์ฐฐ 7 ์ œ 1 ์ ˆ ์ฒ ๋„ ๋„คํŠธ์›Œํฌ์˜ ํ˜•ํ‰์„ฑ 7 1. ์‚ฌํšŒ์  ํ˜•ํ‰์„ฑ 7 2. ๊ณต๊ฐ„์  ํ˜•ํ‰์„ฑ 8 ์ œ 2 ์ ˆ ์ฒ ๋„ ๋„คํŠธ์›Œํฌ์˜ ์ค‘์‹ฌ์„ฑ 10 1. ๋„คํŠธ์›Œํฌ ์ด๋ก  10 2. ๋…ธ๋“œ ์ค‘์‹ฌ์„ฑ 11 ์ œ 3 ์ ˆ ๊ธฐ์กด ์—ฐ๊ตฌ ์‹œ์‚ฌ์  ๋ฐ ๋ณธ ์—ฐ๊ตฌ์˜ ์ฐจ๋ณ„์„ฑ 13 ์ œ 3 ์žฅ ์ž๋ฃŒ ๋ฐ ์—ฐ๊ตฌ๋ฐฉ๋ฒ•๋ก  14 ์ œ 1 ์ ˆ ์ž๋ฃŒ 14 1. ๋…ธ๋“œ 18 2. ๋งํฌ 22 ์ œ 2 ์ ˆ ์—ฐ๊ตฌ๋ฐฉ๋ฒ•๋ก  18 1. ๋…ธ๋“œ ์ค‘์‹ฌ์„ฑ 18 2. ์ง€๋‹ˆ๊ณ„์ˆ˜ 22 3. LISA ๋ถ„์„ 23 ์ œ 4 ์žฅ ๋ถ„์„๊ฒฐ๊ณผ 26 ์ œ 1 ์ ˆ ๋…ธ๋“œ ์ค‘์‹ฌ์„ฑ 26 1. ์ง€์—ญ๋ณ„ ์ค‘์‹ฌ์„ฑ ๋ถ„์„ ๊ฒฐ๊ณผ 26 2. ๊ณต๊ธ‰์ค‘์‹ฌ์„ฑ๊ณผ ์ˆ˜์š”์ค‘์‹ฌ์„ฑ์˜ ์ƒ๊ด€๊ด€๊ณ„ 39 ์ œ 2 ์ ˆ ๊ณต๊ฐ„์  ํ˜•ํ‰์„ฑ ๋ณ€ํ™” 39 1. ๋น„๊ณต๊ฐ„ ํ†ต๊ณ„๋Ÿ‰ 40 2. ๊ณต๊ฐ„ ํ†ต๊ณ„๋Ÿ‰ 41 ์ œ 3 ์ ˆ ์†Œ๊ฒฐ 51 ์ œ 5 ์žฅ ๊ฒฐ๋ก  52 ์ฐธ๊ณ ๋ฌธํ—Œ 54 ๋ถ€๋ก 60 Abstract 62์„

    ๊ธˆ์œต๊ณตํ•™ IV: Monte Carlo Methods for Finance and Economics

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    ์ตœ๊ทผ ๋“ค์–ด ๋ณธ์ €์ž๊ฐ€ ์ž์ฃผ ํ•˜๋Š” ๋ง์€ ์„ ํ˜• (linear) ์˜ ์‹œ๋Œ€๋Š” ๊ฐ”๋‹ค. ์ด์ œ๋Š” ๋น„์„ ํ˜• (nonlinear)์˜ ์‹œ๋Œ€์ด๋‹ค. ๋‚˜๋Š” ์„ ํ˜•์‹œ๋Œ€์˜ ๋งˆ์ง€๋ง‰ ์ˆ˜ํ˜œ์ž๋‹ค ๋ผ๋Š” ๊ฒƒ์ด๋‹ค. ๋ณธ์ €์ž๊ฐ€ ๋Œ€ํ•™์›๊นŒ์ง€ ๋ฐ›์€ ๊ต์œก์ด ์ถ”๊ตฌํ•˜๋˜ ๋ฐ”๋Š” ์šฐ๋ฆฌ๊ฐ€ ์ดํ•ดํ•˜๊ณ ์ž ํ•˜๋Š” ํ˜„์ƒ์„ ์„ ํ˜•๋ชจํ˜•์œผ๋กœ ๋‚˜ํƒ€๋‚ด๊ณ  ์ด๋ฅผ ํ•ด์„์ ์œผ๋กœ ํ’€์–ด์„œ ์†Œ์œ„ ๋‹ซํžŒํ•ด (closed solution) ๋ฅผ ๊ตฌํ•˜๋Š” ๊ฒƒ์ด์—ˆ๋‹ค. ๋‹ซํžŒํ•ด๋ฅผ ๊ตฌํ•œ๋‹ค๋Š” ๊ฒƒ, ์ฆ‰ ํ•ด์„ํ•ด๋ฅผ ๊ตฌํ•œ๋‹ค๋Š” ๊ฒƒ์€ ๊ณ ๋“ฑ์ˆ˜ํ•™์„ ์‚ฌ์šฉํ•ด์„œ ๋ฌธ์ œ๋ฅผ ํ‘ธ๋Š” ๊ฒƒ์ด๋‹ค. ์„ ํ˜•๋ชจํ˜•์ด๋ผ๋Š” ๊ฒƒ์€ ์šฐ๋ฆฌ๊ฐ€ ์ดํ•ดํ•˜๊ณ ์ž ํ•˜๋Š” ํ˜„์ƒ์„ ๊ทผ์‚ฌ์ ์œผ๋กœ ๋‚˜ํƒ€๋‚ด๋Š” ๊ฒƒ์ด๋ฏ€๋กœ ์›๋ž˜ ๋ฌธ์ œ์˜ ๊ด€์ ์—์„œ ๋ณด๋ฉด ๋‹ซํžŒํ•ด๋Š” ๊ทผ์‚ฌํ•ด์ด๋‹ค. ์ปดํ“จํ„ฐ๊ณผํ•™๊ณผ ๊ณตํ•™์˜ ๊ฒฝ์ด๋กœ์šด ๋ฐœ์ „์€ ์ด๋Ÿฌํ•œ ์‚ฌ๊ณ  ํŒจ๋Ÿฌ๋‹ค์ž„์„ ๋ฐ”๊พธ์–ด๊ฐ€๊ณ  ์žˆ๋‹ค. ์ดํ•ดํ•˜๊ณ ์ž ํ•˜๋Š” ํ˜„์ƒ์„ ์„ ํ˜•๋ชจํ˜•์ด ์•„๋‹Œ ๋น„์„ ํ˜•๋ชจํ˜•์œผ๋กœ ๋‚˜ํƒ€๋‚ด๊ณ , ์ด ๋น„์„ ํ˜•๋ฌธ์ œ๋ฅผ ํ•ด์„์ ์œผ๋กœ ํ‘ธ๋Š” ๊ฒƒ์ด ์•„๋‹ˆ๋ผ ๊ณผํ•™์ ์ปดํ“จํŒ…(scientific computing)์„ ํ†ตํ•ด์„œ ํ•ด๊ฒฐํ•œ๋‹ค. ์šฐ์„  ๋น„์„ ํ˜•๋ชจํ˜•์€ ์„ ํ˜•๋ชจํ˜•์„ ์ผ๋ฐ˜ํ™”ํ•œ ๊ฒƒ์ด๋‹ˆ ์ž˜๋งŒ ์‚ฌ์šฉํ•˜๋ฉด ์„ ํ˜•๋ชจํ˜•๋ณด๋‹ค ํ›จ์”ฌ ๋” ์ดํ•ดํ•˜๊ณ ์ž ํ•˜๋Š” ํ˜„์ƒ์„ ์ž˜ ๋‚˜ํƒ€๋‚ผ ์ˆ˜ ์žˆ๋‹ค. ๋˜ํ•œ, ๊ธ‰๊ฒฉํ•œ ์ปดํ“จํ„ฐ๊ณผํ•™/๊ณตํ•™์˜ ๋ฐœ์ „์€ ๊ทธ๋Ÿฌํ•œ ๋น„์„ ํ˜•๋ฌธ์ œ๋ฅผ ์ข€ ๋” ์ •๋ฐ€ํ•˜๊ณ  ํšจ์œจ์ ์œผ๋กœ ํ’€ ์ˆ˜ ์žˆ๋„๋ก ํ•œ๋‹ค. ์ตœ๊ทผ ์ด๋Ÿฌํ•œ ๊ฒฝํ–ฅ์„ ์ž˜ ๋‚˜ํƒ€๋‚ด๋Š” ๋‹จ์–ด๋“ค๋กœ๋Š” ๋น…๋ฐ์ดํ„ฐ (big data), ๋„คํŠธ์›Œํฌ๊ณผํ•™๊ณผ ์‘์šฉ, ๊ตฌ๊ธ€์˜ ํŽ˜์ด์ง€๋žญํฌ (Page Rank), ์•ŒํŒŒ๊ณ  (Alpha Go), ์•Œ๊ณ ๋ฆฌ์ฆ˜ ํŠธ๋ ˆ์ด๋”ฉ(algorithmic trading), DSGE๋ชจํ˜•(dynamic stochastic general equilibrium model) ๋“ฑ์ด ์žˆ๋‹ค. ๊ธฐ์กด ์ˆ˜ํ•™๋งŒ์„ ์‚ฌ์šฉํ•ด์„œ ๋น„์„ ํ˜•๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๋Š” ๋ฐ๋Š” ํ•œ๊ณ„๊ฐ€ ์žˆ์œผ๋ฏ€๋กœ ์ปดํ“จํ„ฐ์˜ ๋„์›€์„ ํ•„์š”๋กœ ํ•œ๋‹ค. ๋ฌผ๋ก  ์ˆ˜์น˜ํ•ด์„๊ธฐ๋ฒ•์„ ์ ์šฉํ•ด์„œ ๋น„์„ ํ˜•๋ฌธ์ œ๋ฅผ ํ’€ ์ˆ˜๋„ ์žˆ์œผ๋‚˜, ์ˆ˜์น˜ํ•ด์„๊ธฐ๋ฒ•์„ ์ ์šฉํ•  ์ˆ˜ ์—†๋Š” ๋น„์„ ํ˜•๋ฌธ์ œ๊ฐ€ ๋งŽ์ด ์žˆ๊ณ  ๋˜ ์ˆ˜์น˜ํ•ด์„๊ธฐ๋ฒ•์„ ์ ์šฉํ•˜๋Š” ๊ฒƒ๋ณด๋‹ค๋Š” ์‹œ๋ฎฌ๋ ˆ์ด์…˜๊ธฐ๋ฒ•์„ ์‚ฌ์šฉํ•˜๋Š” ๊ฒƒ์ด ํ›จ์”ฌ ๋” ํšจ์œจ์ ์ธ ๊ฒฝ์šฐ๊ฐ€ ๋งŽ๋‹ค. ๋ณธ์„œ์—์„œ๋Š” ์‹œ๋ฎฌ๋ ˆ์ด์…˜๊ธฐ๋ฒ•์˜ ๊ธฐ์ดˆ๋ฅผ ์†Œ๊ฐœํ•œ๋‹ค. ๋ณธ ์ €์ž๋Š” ์ง€๋‚œ 20์—ฌ๋…„ ๋™์•ˆ ํ•™์ƒ๋“ค์—๊ฒŒ ์•ž์œผ๋กœ๋Š” ๊ธˆ์œตํ˜„์ƒ์ด๋‚˜ ๊ฒฝ์ œํ˜„์ƒ์„ ์ดํ•ดํ•˜๊ณ  ๋Œ€์ฒ˜ํ•˜๋Š”๋ฐ ๊ณผํ•™์ ์ปดํ“จํŒ…์„ ์ ์šฉํ•ด์•ผํ•œ๋‹ค๊ณ  ๊ฐ•์กฐํ•˜๋ฉด์„œ, ๊ทธ๋Ÿฌํ•œ ๊ด€์ ์—์„œ ๊ฐ•์˜ํ•˜๊ณ  ์—ฐ๊ตฌ๋ฅผ ํ•ด์˜ค๊ณ  ์žˆ๋‹ค. ์ด์ œ ์€ํ‡ด๋ฅผ ์–ผ๋งˆ ๋‚จ๊ธฐ์ง€ ์•Š์€ ์‹œ์ ์—์„œ ๊ทธ๋™์•ˆ ์ž‘์„ฑํ•ด๋†“์•˜๋˜ ๊ฐ•์˜์•ˆ์„ ์ •๋ฆฌํ•ด์„œ ๊ณผํ•™์ ์ปดํ“จํŒ…์— ๊ด€ํ•œ ์ฑ… ์„ธ ๊ถŒ์„ ์“ธ ์˜ˆ์ •์ด๋‹ค. ๊ทธ ์ค‘์—์„œ ์ฒซ ๋ฒˆ์งธ ์ฑ…์ด ๋ณธ์„œ์ด๋‹ค. ๋ณธ์„œ์—์„œ๋Š” ๋‚œ์ˆ˜๋ฐœ์ƒ๋ถ€ํ„ฐ ์‹œ์ž‘ํ•ด์„œ MCMC์˜ ๊ธฐ์ดˆ๊นŒ์ง€ ๊ทธ๋ฆฌ๊ณ  ํ™•๋ฅ ๋ฏธ๋ถ„๋ฐฉ์ •์‹ ๋“ฑ ํ™•๋ฅ ๋ชจํ˜•์—์„œ ํ‘œ๋ณธ๊ฒฝ๋กœ๋ฅผ ์ƒ์„ฑํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ๋‹ค๋ฃจ๊ณ ์ž ํ•œ๋‹ค. ๋‘ ๋ฒˆ์งธ ์ฑ…์€ ๊ธˆ์œตํ•™๊ณผ ๊ฒฝ์ œํ•™์—์„œ ์‚ฌ์šฉ๋˜๋Š” ์ˆ˜์น˜ํ•ด์„์— ๊ด€ํ•œ ๊ฒƒ์ด๊ณ , ์„ธ ๋ฒˆ์งธ ์ฑ…์€ ๊ธˆ์œตํ•™๊ณผ ๊ฒฝ์ œํ•™์—์„œ ์‚ฌ์šฉ๋˜๋Š” ๋ฒ ์ด์ง€์•ˆ๊ธฐ๋ฒ•์— ๊ด€ํ•œ ๊ฒƒ์ด๋‹ค. ํ•ญ์ƒ ๊ทธ๋ž˜์™”๋“ฏ์ด ๋ณธ์„œ๋ฅผ ๋งŒ๋“œ๋Š” ๋ฐ๋Š” ๋งŽ์€ ์‚ฌ๋žŒ๋“ค์˜ ๋„์›€์ด ์ปธ๋‹ค. ๋ณธ์„œ๋Š” 10๋…„ ์ „์— ์จ๋†“์•˜๋˜ ๊ฐ•์˜์•ˆ์„ ๋ฐ”ํƒ•์œผ๋กœ ํ•œ ๊ฒƒ์ด๋‹ค. ์ด ๊ฐ•์˜์•ˆ์„ ์ž‘์„ฑํ•˜๋Š”๋ฐ ์‚ฌ์šฉํ•œ ์›Œ๋“œํ”„๋กœ์„ธ์„œ๋Š” ํ•œ๊ธ€97์ด๋‹ค. ์šฐ์„  ์ง€๋‚œ 2๋…„ ๋™์•ˆ ์ด ํ•œ๊ธ€97๋กœ ๋œ ํŒŒ์ผ์„ LATEXํŒŒ์ผ๋กœ ์ „ํ™˜ํ•˜๋Š” ์ž‘์—…์„ ํ•ด์ค€ ๋ฐฑ์Šนํ—Œ๊ตฐ์—๊ฒŒ ๊ฐ์‚ฌํ•œ๋‹ค. ๊น€์ฐฌ์ˆ˜๊ตฐ์€ ๋ณธ์„œ์— ์‚ฌ์šฉ๋œ LATEX์Šคํƒ€์ผํŒŒ์ผ์„ ๋งŒ๋“ค์–ด์ฃผ์—ˆ๊ณ , ์›๊ณ ๋ฅผ ์ž์„ธํžˆ ์ฝ๊ณ  ๊ต์ •์„ ํ•ด์ฃผ์—ˆ๋‹ค. ์šฐ์ˆ˜๊ฒฝ์–‘๋„ ๊ผผ๊ผผํžˆ ์›๊ณ ๋ฅผ ์ฝ๊ณ  ๋งŽ์€ ์กฐ์–ธ์„ ํ•ด์ฃผ์—ˆ๋‹ค. ์ด๋ฒˆ์—๋„ ์ดํ•ด์—ฐ์‚ฌ์žฅ๋‹˜์ด ํ‘œ์ง€๋ฅผ ๋””์ž์ธํ•ด์ฃผ์…จ๋‹ค. ๋˜ํ•œ, ๋ณธ์„œ๋ฅผ ์ถœ๊ฐ„ํ•˜๋Š”๋ฐ ์žˆ์–ด ๊ผผ๊ผผํ•˜๊ฒŒ ํ–‰์ •์ ์ธ ์ฒ˜๋ฆฌ๋ฅผ ํ•ด์ค€ ๊น€๊ตฌ์žฌ๋‹จ ๋…ธ์Šน์›๋ถ€์žฅ๊ป˜ ๊ฐ์‚ฌ๋“œ๋ฆฐ๋‹ค. ์ด ๋ถ„๋“ค์˜ ๋„์›€์—†์ด๋Š” ์•„๋งˆ ๋ณธ ์ €์ž์˜ ์€ํ‡ด ์ „์— ๋ณธ์„œ๊ฐ€ ์„ธ์ƒ์— ๋‚˜์˜ค์ง€ ๋ชปํ–ˆ์„ ๊ฒƒ์ด๋‹ค

    ๊ทธ ์›๋ฆฌ์™€ ๋ฒ•์  ์ฑ…์ž„์— ๊ด€ํ•˜์—ฌ

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ์œตํ•ฉ๊ณผํ•™๊ธฐ์ˆ ๋Œ€ํ•™์› ์ˆ˜๋ฆฌ์ •๋ณด๊ณผํ•™๊ณผ, 2023. 2. ์ด์ƒ์›.์›น ํฌ๋กค๋ง์€ ํฌ๋กค๋Ÿฌ ๋˜๋Š” ์ŠคํŒŒ์ด๋”๋ผ๋Š” ํ”„๋กœ๊ทธ๋žจ์„ ์‚ฌ์šฉํ•˜์—ฌ ์›น ๋ฐ์ดํ„ฐ๋ฅผ ์ˆ˜์ง‘ํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ์˜๋ฏธํ•œ๋‹ค. ํฌ๋กค๋Ÿฌ์˜ ๊ธฐ๋ณธ์  ์›๋ฆฌ๋Š” ์ฃผ์–ด์ง„ ์‹œ๋“œ URL์—์„œ ์ถœ๋ฐœํ•˜์—ฌ ๊ทธ URL๊ณผ ์—ฐ๊ฒฐ๋œ ์›น ํŽ˜์ด์ง€๋ฅผ ๋‹ค์šด๋กœ๋“œํ•˜๊ณ , ์—ฌ๊ธฐ์— ํฌํ•จ๋œ ํ•˜์ดํผ๋งํฌ๋ฅผ ์ถ”์ถœํ•˜๊ณ , ์ด๋Ÿฌํ•œ ํ•˜์ดํผ๋งํฌ๋กœ ์‹๋ณ„๋˜๋Š” ์›น ํŽ˜์ด์ง€๋ฅผ ์žฌ๊ท€์ ์œผ๋กœ ๊ณ„์† ๋‹ค์šด๋กœ๋“œํ•˜๋Š” ๊ฒƒ์ด๋‹ค. ์›น ํฌ๋กค๋ง์€ ์ด์ œ ๋ฐ์ดํ„ฐ๊ฐ€ ํ•ต์‹ฌ ์š”์†Œ๊ฐ€ ๋˜๋Š” ๋ชจ๋“  ๊ณณ์—์„œ ๋ฐ์ดํ„ฐ๋ฅผ ์ˆ˜์ง‘ํ•˜๋Š” ๊ฐ€์žฅ ํšจ๊ณผ์ ์ด๊ณ  ์œ ์šฉํ•œ ๋ฐฉ๋ฒ• ์ค‘ ํ•˜๋‚˜๋กœ ๋„๋ฆฌ ์‚ฌ์šฉ๋˜๊ณ  ์žˆ๋‹ค. ํŠนํžˆ ๋น…๋ฐ์ดํ„ฐ ๋˜๋Š” ์ธ๊ณต์ง€๋Šฅ์˜ ๋“ฑ์žฅ์œผ๋กœ ์ธํ•˜์—ฌ, ๋งˆ์ผ€ํŒ… ๋˜๋Š” ๋น„์ฆˆ๋‹ˆ์Šค ์ „๋žต์— ์žˆ์–ด ๊ฒฝ์˜ ํŒ๋‹จ ๋˜๋Š” ์˜์‚ฌ ๊ฒฐ์ • ๊ณผ์ •์—์„œ ์›น ํฌ๋กค๋ง์€ ์ด์ œ ํ•„์ˆ˜ ๋ถˆ๊ฐ€๊ฒฐํ•œ ๊ฒƒ์ด ๋˜์—ˆ๋‹ค. ์›น ํฌ๋กค๋ง์ด ์ ์  ์ค‘์š”ํ•ด์ง€๊ณ  ์žˆ์ง€๋งŒ ์ด๋ฅผ ์‚ฌ์šฉํ•˜๋Š” ๊ฒƒ์˜ ๋ฒ•์  ์ฑ…์ž„์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๋Š” ๊ฑฐ์˜ ์—†์—ˆ๋‹ค. ์ด ๋…ผ๋ฌธ์€ ์›น ํฌ๋กค๋Ÿฌ์˜ ์ž‘๋™ ๋ฉ”์ปค๋‹ˆ์ฆ˜๊ณผ ๊ทธ๊ฒƒ์„ ์‚ฌ์šฉํ•œ ํ–‰์œ„์˜ ๋ฒ•์  ์ฑ…์ž„์„ ์ค‘์‹ฌ์œผ๋กœ ๊ฒ€ํ† ํ•œ๋‹ค. ์ตœ๊ทผ ๋Œ€๋ฒ•์›์€ ํ”ผ๊ณ ์ธ๋“ค์ด ์ˆ™๋ฐ•์ •๋ณด์ œ๊ณต์—…์ฒด์˜ ์ง์›์ด ๊ฒฝ์Ÿ์—…์ฒด์˜ ๋ชจ๋ฐ”์ผ ์• ํ”Œ๋ฆฌ์ผ€์ด์…˜ ์„œ๋ฒ„์— ์ ‘์†ํ•ด ์ž์‹ ์˜ ํฌ๋กค๋ง ํ”„๋กœ๊ทธ๋žจ์„ ํ†ตํ•ด ์ˆ™๋ฐ•์‹œ์„ค ๋ชฉ๋ก ๋“ฑ ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค๋ฅผ ๋ณต์‚ฌํ•œ ์‚ฌ๊ฑด์—์„œ ํ”ผ๊ณ ์ธ๋“ค์„ ๋ฌด์ฃ„๋กœ ํŒ๋‹จํ•œ ๋ฐ” ์žˆ๋‹ค. ๊ทธ ํŒ๊ฒฐ์€ โ‘ ์„œ๋น„์Šค ์ œ๊ณต์ž๊ฐ€ ๋„คํŠธ์›Œํฌ์— ๋Œ€ํ•œ ์ ‘๊ทผ๊ถŒํ•œ์„ ์ œํ•œํ•˜๋Š”์ง€ ์—ฌ๋ถ€๋Š” ๋ณดํ˜ธ์กฐ์น˜๋‚˜ ์ด์šฉ์•ฝ๊ด€ ๋“ฑ์˜ ๋Œ€์ƒ์— ์˜ํ•˜์—ฌ ๊ฒฐ์ •๋˜์–ด์•ผ ํ•˜๋ฉฐ, โ‘ก๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค์˜ ์ƒ๋‹น ๋ถ€๋ถ„์€ ์–‘๊ณผ ์งˆ ๋ชจ๋‘๋ฅผ ๊ธฐ์ดˆ๋กœ ํŒ๋‹จํ•˜์—ฌ์•ผ ํ•œ๋‹ค๊ณ  ํ•˜์˜€๋‹ค. ์ด ๊ฐ™์€ ๋ฒ•๋ฆฌ์— ๊ธฐ์ดˆํ•˜์—ฌ ์ •๋ณดํ†ต์‹ ๋ง์นจ์ž…, ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค ๋„์šฉ์œผ๋กœ ์ธํ•œ ์ €์ž‘๊ถŒ๋ฒ•์œ„๋ฐ˜, ์—…๋ฌด๋ฐฉํ•ด ํ˜์˜ ๋ชจ๋‘์— ๋Œ€ํ•ด์„œ๋Š” ๋ฌด์ฃ„๋ฅผ ์„ ๊ณ ํ•˜์˜€๋‹ค. ์ด๊ฒƒ์€ ํฌ๋กค๋ง์„ ํ†ตํ•œ ๋ฐ์ดํ„ฐ ์ˆ˜์ง‘์— ๋Œ€ํ•œ ์ตœ์ดˆ์˜ ๋Œ€๋ฒ•์› ํŒ๊ฒฐ์ด๋‹ค. ์ด ๊ธ€์—์„œ๋Š” ์œ„ ๋Œ€๋ฒ•์› ํŒ๊ฒฐ์„ ๊ธฐ์ดˆ๋กœ 3๊ฐ€์ง€ ์ธก๋ฉด ์ฆ‰ ์ •๋ณดํ†ต์‹ ๋ง์นจ์ž…, ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค ๋„์šฉ์œผ๋กœ ์ธํ•œ ์ €์ž‘๊ถŒ๋ฒ•์œ„๋ฐ˜, ํ˜•๋ฒ•์ƒ ์—…๋ฌด๋ฐฉํ•ด์— ๋Œ€ํ•ด ๊ตฌ์ฒด์ ์œผ๋กœ ๊ฒ€ํ† ํ•˜๊ณ , ๊ทธ ์™ธ ๋ถ€์ •๊ฒฝ์Ÿ๋ฐฉ์ง€๋ฒ•, ๊ฐœ์ธ์ •๋ณด๋ณดํ˜ธ๋ฒ• ๊ธฐํƒ€ ๊ฒฝ์Ÿ๋ฒ•์  ์ธก๋ฉด์—์„œ๋„ ๊ฒ€ํ† ํ•œ๋‹ค. ๊ทธ ๊ฒฐ๋ก ์€ ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. ์›น ํฌ๋กค๋Ÿฌ ์‚ฌ์šฉ์˜ ๋ฒ•์  ์ฑ…์ž„์€ ์ •๋ณดํ†ต์‹ ๋ง ์ ‘๊ทผ ๋ฒ”์œ„, ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค์ œ์ž‘์ž ๊ถŒ๋ฆฌ ์นจํ•ด ์—ฌ๋ถ€ ๊ทธ๋ฆฌ๊ณ  ์žฅ์• ์—…๋ฌด๋ฐฉํ•ด ์—ฌ๋ถ€๋ฅผ ๊ธฐ์ค€์œผ๋กœ ํŒ๋‹จ๋˜์–ด์•ผ ํ•˜๋Š”๋ฐ, ๊ทธ์™€ ๊ฐ™์€ ๋ฒ•๋ฅ ์  ํ‰๊ฐ€๋Š” ์›น ํฌ๋กค๋Ÿฌ๊ฐ€ ์‚ฌ์šฉ๋˜๋Š” ์ƒํ™ฉ, ์‚ฌ์šฉ์ž์˜ ์˜๋„ ๊ทธ๋ฆฌ๊ณ  ์‚ฌ์šฉ์œผ๋กœ ์ธํ•˜์—ฌ ๋ฐœ์ƒํ•œ ๊ฒฐ๊ณผ์™€ ๊ฐ™์€ ์‚ฌ์ •์„ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•˜์—ฌ์•ผ ํ•œ๋‹ค.Web Crawling means a way of collecting web data using a program which is called Crawler or Spider. A Crawler, given seed URLs, downloads the web pages associated with these URLs, extracts hyperlinks contained in them, and recursively continues to download the web pages identified by these hyperlinks. Web Crawling is now broadly being used as one of the most effective and useful method in data collecting wherever data becomes a key factor. Especially in decision-making process in marketing or business strategy, Web Crawling is now sine qua non with the emerging of big data or artificial intelligence. While Web Crawling is getting more and more important, few studies on legal responsibilities of using it can be found. This article focuses on the working mechanism and legal responsibilities of using Web Crawler. Recently the Supreme Court has found, in the case where the defendants, the employees of a company running a accommodation information offering service accessed the competiters mobile application server and copied the database such as a list of accommodation through their crawling computer program, that the defendants are not guilty. The Case showed new legal principles, โ‘  whether the service provider has set any limitation on access right to network should be determined by object things such as protective actions or terms and conditions of use, โ‘ก significant copy of database should be determined in the aspects of quantity and quality both. And The Case sentenced not guilty to the charges(invasion of network, piracy of database and obstruction of business). The Case was the first judgement of the Supreme Court on crawling data collection. In this article, The Case will be examined thoroughly in the 3 aspects(invasion of network, piracy of database, obstruction of business) including Unfair Competition Prevention Act and competition laws. The conclusion of this article is as follows. Legal responsibilities for using web crawlers should be judged by the standards of the scope of access to networks, whether the rights of database producers are violated and whether there is any obstruction of business, which must be based on the circumstances where web crawlers are used, the intetion of the user and the result of the use.โ… . ์„œ๋ก  1 1. ๋ฌธ์ œ์˜์‹ 1 2. ์—ฐ๊ตฌ์˜ ๋‚ด์šฉ๊ณผ ๋ฐฉ๋ฒ• 3 3. ์ผ๋Ÿฌ๋‘๊ธฐ 5 ๊ฐ€. ์ด ๊ธ€์˜ ํ™”์ž๋กœ์„œ ๋‚˜ 6 ๋‚˜. ์ธ๋”์ŠคํŠธ๋ฆฌ4.0(Industrie4.0) 7 โ…ก. ์›น ํฌ๋กค๋ง(Web Crawling) 10 1. ๋„์ž… 10 2. ์›น ํฌ๋กค๋ง์˜ ๊ฐœ์š” 11 ๊ฐ€. ์›น ํฌ๋กค๋ง๊ณผ ์ฝ˜ํ…์ธ  ์ˆ˜์ง‘ 11 ๋‚˜. ์šฉ์–ด์˜ ์ •๋ฆฌ 12 ๋‹ค. ์›น ํฌ๋กค๋ง์˜ ์—ญ์‚ฌ 16 3. ์›น ํฌ๋กค๋ง์˜ ๊ธฐ์ˆ ์  ์›๋ฆฌ 18 ๊ฐ€. ์›น๊ณผ ์›น ํฌ๋กค๋ง 18 ๋‚˜. ์›น ํฌ๋กค๋ง์˜ ์ž‘๋™ ์›๋ฆฌ 19 ๋‹ค. ํฌ๋กค๋ง ์ •์ฑ…(Crawling policies) 22 1) ์ •์ค‘ํ•จ ์ •์ฑ…(politeness policy) 23 2) ์žฌ๋ฐฉ๋ฌธ ์ •์ฑ…(re-visit policy) 25 3) ์„ ํƒ ์ •์ฑ…(selection policy) 27 4) ๋ณ‘๋ ฌํ™” ์ •์ฑ…(parallelization policy) 28 ๋ผ. ์›น ํฌ๋กค๋Ÿฌ์˜ ์œ ํ˜• 29 4. ์›น ํฌ๋กค๋ง ๋ฐฉ์ง€ ๊ธฐ์ˆ  30 ๊ฐ€. ๋กœ๋ด‡ ๋ฐฐ์ œ ํ”„๋กœํ† ์ฝœ(robot exclusion protocol, robot.txt) 31 ๋‚˜. ๋ฉ”ํƒ€ํƒœ๊ทธ(metatag) 33 ๋‹ค. ์บก์ฐจ(CAPTCHA) 34 5. ์†Œ๊ฒฐ 35 โ…ข. ์›น ํฌ๋กค๋ง ์‚ฌ์šฉ์˜ ํ˜•์‚ฌ๋ฒ•์  ์ฑ…์ž„ 37 1. ๋„์ž… 37 ๊ฐ€. ๊ฐ€์น˜์˜ ์ถฉ๋Œ 37 ๋‚˜. ๋Œ€๋ฒ•์› 2022. 5. 12. ์„ ๊ณ  2021๋„1533 ํŒ๊ฒฐ 39 1) ๊ณต์†Œ์‚ฌ์‹ค์˜ ์š”์ง€ 42 2) ์ œ1์‹ฌ์˜ ํŒ๋‹จ 45 3) ํ•ญ์†Œ์‹ฌ์˜ ํŒ๋‹จ 48 2. ์ •๋ณดํ†ต์‹ ๋ง ์นจ์ž… 51 ๊ฐ€. ์ •๋ณดํ†ต์‹ ๋ง๋ฒ•์ƒ ์ •๋ณดํ†ต์‹ ๋ง์นจ์ž…์ฃ„ 51 ๋‚˜. ์ ‘๊ทผ๊ถŒํ•œ ์œ ๋ฌด์˜ ํŒ๋‹จ๊ธฐ์ค€ - ๊ฐ๊ด€์ ์œผ๋กœ ๋“œ๋Ÿฌ๋‚œ ์‚ฌ์ • 52 1) ๊ฐ๊ด€์  ์ƒํ™ฉ 53 2) ์ด์šฉ์•ฝ๊ด€ 54 3) ๋ณดํ˜ธ์กฐ์น˜ 56 ๋‹ค. ๋ฏธ๊ตญ์˜ CFAA 59 1) ๋ณดํ˜ธ๋ฒ•์ต 61 2) ์ ‘๊ทผ(access) 62 3) ๊ถŒํ•œ(authority) 62 4) CFAA ๊ด€๋ จ ์‚ฌ๋ก€ 64 ๋ผ. ์œ ๋Ÿฝ์—ฐํ•ฉ์˜ ์‚ฌ์ด๋ฒ„๋ฒ”์ฃ„ํ˜‘์•ฝ(Convention on Cybercrime) 70 3. ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค์ œ์ž‘์ž์˜ ๊ถŒ๋ฆฌ ์นจํ•ด 73 ๊ฐ€. ์ €์ž‘๊ถŒ๋ฒ•์ƒ ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค์™€ ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค์ œ์ž‘์ž์˜ ๊ถŒ๋ฆฌ 73 1) ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค 73 2) ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค์ œ์ž‘์ž์˜ ๊ถŒ๋ฆฌ 76 ๋‚˜. ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค์ œ์ž‘์ž ๊ถŒ๋ฆฌ ์นจํ•ด์˜ ํŒ๋‹จ๊ธฐ์ค€ 78 1) ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค์ œ์ž‘์ž์˜ ๊ถŒ๋ฆฌ ์นจํ•ด ์—ฌ๋ถ€์— ๊ด€ํ•œ ๋ฏผ์‚ฌ์‚ฌ๋ก€ 80 2) ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค์ œ์ž‘์ž์˜ ๊ถŒ๋ฆฌ ์นจํ•ด ์—ฌ๋ถ€์— ๊ด€ํ•œ ํ˜•์‚ฌ์‚ฌ๋ก€ 83 ๋‹ค. ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค์ œ์ž‘์ž์˜ ๊ถŒ๋ฆฌ ์นจํ•ด์™€ ์›น ํฌ๋กค๋ง 88 4 ์ปดํ“จํ„ฐ๋“ฑ์žฅ์• ์—…๋ฌด๋ฐฉํ•ด 89 ๊ฐ€. ์ปดํ“จํ„ฐ๋“ฑ์žฅ์• ์—…๋ฌด๋ฐฉํ•ด์ฃ„ 89 1) ํ—ˆ์œ„์˜ ์ •๋ณด ๋˜๋Š” ๋ถ€์ •ํ•œ ๋ช…๋ น 89 2) ์ •๋ณด์ฒ˜๋ฆฌ์— ์žฅ์•  ๋ฐœ์ƒ 91 ๋‚˜. ์ปดํ“จํ„ฐ๋“ฑ์žฅ์• ์—…๋ฌด๋ฐฉํ•ด์™€ ์›น ํฌ๋กค๋ง 92 1) ๋ถ€์ •ํ•œ ๋ช…๋ น์ธ์ง€ ์—ฌ๋ถ€ 92 2) ์ •๋ณด์ฒ˜๋ฆฌ์— ์žฅ์•  ๋ฐœ์ƒ ์—ฌ๋ถ€ 93 5. ์†Œ๊ฒฐ 94 โ…ฃ. ์›น ํฌ๋กค๋ง ์‚ฌ์šฉ์˜ ๊ธฐํƒ€ ๋ฒ•์  ์ฑ…์ž„ 98 1. ๋„์ž… 98 2. ๋ถ€์ •๊ฒฝ์Ÿ๋ฐฉ์ง€ ๋ฐ ์˜์—…๋น„๋ฐ€๋ณดํ˜ธ์— ๊ด€ํ•œ ๋ฒ•๋ฅ ์ƒ ์ฑ…์ž„ 98 ๊ฐ€. ๋ถ€์ •๊ฒฝ์Ÿํ–‰์œ„๋กœ์„œ ์„ฑ๊ณผ๋ฌผ์˜ ๋ถ€์ •์ฐจ์šฉ 99 ๋‚˜. ๋ถ€์ •๊ฒฝ์Ÿํ–‰์œ„๋กœ์„œ ๋ฐ์ดํ„ฐ ๋ถ€์ •์‚ฌ์šฉ 101 ๋‹ค. ์„œ์šธ๊ณ ๋“ฑ๋ฒ•์› 2022. 8. 25. ์„ ๊ณ  2021๋‚˜2034740 ํŒ๊ฒฐ 103 ๋ผ. ๋ฐ์ดํ„ฐ ๋ถ€์ •์‚ฌ์šฉ ๋˜๋Š” ์„ฑ๊ณผ๋ฌผ์˜ ๋ถ€์ •์ฐจ์šฉ๊ณผ ์›น ํฌ๋กค๋ง 105 3. ๋…์ ๊ทœ์ œ ๋ฐ ๊ณต์ •๊ฑฐ๋ž˜์— ๊ด€ํ•œ ๋ฒ•๋ฅ ์ƒ ์ฑ…์ž„ 107 ๊ฐ€. ์›น ํฌ๋กค๋ง์— ๋Œ€ํ•œ ๊ฒฝ์Ÿ๋ฒ•์˜ ํ‰๊ฐ€ 107 ๋‚˜. ๋ถˆ๊ณต์ •๊ฑฐ๋ž˜ํ–‰์œ„ ๋˜๋Š” ์‹œ์žฅ์ง€๋ฐฐ์ ์ง€์œ„ ๋‚จ์šฉ๊ณผ ์›น ํฌ๋กค๋ง 108 4. ์†Œ๊ฒฐ 111 โ…ค. ๊ฒฐ๋ก  112 - ์ฐธ๊ณ ๋ฌธํ—Œ - 117 [Astract] 122 ํ‘œ 1 robot.txt์˜ ์‚ฌ์šฉ์˜ˆ์‹œ 32 ํ‘œ 2 ๋ฉ”ํƒ€ํƒœ๊ทธ ์˜ˆ์‹œ 34 ๊ทธ๋ฆผ 1 ๋ถ„์‚ฐํ˜• ํฌ๋กค๋Ÿฌ์˜ ์ž‘๋™ ์›๋ฆฌ 20 ๊ทธ๋ฆผ 2 robot.txt์˜ ์ ์šฉ/๋ฏธ์ ์šฉ ์•Œ๊ณ ๋ฆฌ์ฆ˜ 31์„

    ๋‚จํ•ด ๋…์ผ๋งˆ์„, ๋ถ€์‚ฐ ์žฅ๋ฆผํฌ๊ตฌ์™€ ํฐ์—ฌ์šธ๋ฌธํ™”๋งˆ์„์„ ์‚ฌ๋ก€๋กœ

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ์‚ฌํšŒ๊ณผํ•™๋Œ€ํ•™ ์ง€๋ฆฌํ•™๊ณผ, 2022.2. ์ด์ •๋งŒ.Although academia has raised considerable concerns and criticisms about the spread of kitsch, it is cleverly penetrating our daily spheres and developing as its own culture. This study is an attempt to understand and conceptualize the properties of kitsch appearing in today's tourism. With the development of the digital environment, this study notes the role of social media as producing discourse and explains how this discourse fits with kitsch tourism. Therefore, this study aims to investigate the kitschization of places and the formation process of kitsch tourism by analyzing the ways in which each party performs their respective roles โ€“ local governments as creating tourism space, social media as forming a tourism discourse, and tourists as practicing such discourse. In this study, โ€˜kitschization of placeโ€™ refers to the dualization of placeness (sense of place) that occurs as a place imitates an idealized other to produce consumption value and gradually becomes separated from its original characteristics. While โ€˜kitsch tourismโ€™ signifies the attitude of consuming symbolic value and aesthetic and exotic images added to a place, regardless of the essentiality or authenticity of the place. In order to more clearly distinguish kitsch attributes within South Korean tourism, this study focuses on the exotic aspects of kitsch and selects three research sites โ€“ Namhae German Village, Busan Janglim Port, and Huinnyeoul Culture Village โ€“ to compare the differences in initiative by local governments and by social media. This study analyzes tourism discourse by collecting data from blogs which are widely used as tourism information sources in South Korea. The results are summarized as follows. First, all three research sites were intentionally or accidentally kitschized by local governments who operated projects to revitalize the local economy and tourism. In the case of the German village, independent of the essential characteristics of Namhae, Namhae-gun planned and intentionally turned the place into kitsch by creating a new place as a settlement for Korean migrant returnees from German. Janglim Port was converted into kitsch as โ€˜Bunezia (Venezia of Busan)โ€™ by Saha-gu, who institutionalized the port's new placeness after it became popularized in social media as resembling Venezia. While Namhae-gun legitimized its kitschization by endowing the place with significance of homeland settlement for Korean miners and nurses dispatched to Germany for the economic development of the country in the 1960s and 1970s, Saha-gu's kitschization of Janglim Port was a response to new tourism demand created by social media. The Huinnyeoul Culture Village originally formed as a settlement for refugees during the Korean War was transformed as a Culture and Arts Village by Yeongdo-gu's project for regeneration of deteriorated residential areas. Kitschization of the village took place in the form of benchmarking Santorini and others to improve the village landscape by coloring buildings. The reason for Yeongdo-gu's benchmarking and this partial and selective imitation can be interpreted as the municipal government recognizing that external similarity to Santorini brings about a kitsch effect that makes the place appear attractive. Second, bloggers represent places online by highlighting visibly aesthetic and exotic images rather than placeness, and form discourses that routinize tourism activities centered on visual enjoyment or play. Bloggers took aesthetically significant aspects of a landscape creating symbols out of them, and generated fantasy of places by projecting symbolic images of other places such as โ€˜Venezia of โ—‹โ—‹โ€™ and โ€˜Santorini of โ—‹โ—‹.โ€™ In addition, โ€˜proof-shotโ€™ spots have become signs representing tourist destinations in blogs. โ€˜Proof-shotsโ€™ are photos visitors take to prove to others, especially through social media, that they have been to a particular location. Therefore, proof-shot spots are not places with historical and cultural significance, but places where bloggers discover aesthetics and create new meanings and values. The bloggers shared their proof-shots in their blogs, drawing attention and interest from potential tourists, instructing them in detail that there is a certain manner of taking these photos that is fashionable, thus making this whole process into a popularized play. As such, these blogs form a discourse that induces or promotes kitsch tourism while lauding the tourism method that consumes aesthetic and exotic images through visual enjoyment and play rather than appreciating placeness. Third, tourists put into practice the tourism discourse produced through blogs and form a culture as they perform kitsch tourism. Most of the tourists referred to blogs to plan their travels, and their behavior practicing the discourse represented in these blogs make up the characteristics of kitsch tourism. Its characteristics can broadly be summarized as play-oriented tourism through signs and images, aesthetic emotional consumption, distinction, and vicarious satisfaction. Kitsch tourism may seem meaningless in that one experiences a place superficially and manually through signs and images of places like mass tourism, but their actions and practices are different from previous tourism, because it entails agency rather than a passive visual experience. For example, tourists may take pictures at a place, performing the โ€˜proof-shotโ€™ method as instructed in social media, but in this process, they actively move their body to obtain the image they want and express themselves. When taking pictures, tourists do not pay attention to the essential attributes of the places, but gave more meaning to the social interaction in which they share space and time with their companions. In this respect, kitsch tourism, which makes use of visibly revealed images of a landscape, is not behavior lacking authenticity. Rather, it is a shift in the meaning of tourism โ€“ from being objects of tourism to experiencing authenticity triggered by the tourism experience. This study has recast the negative perspective of kitsch and contributes to understanding kitsch as a socio-cultural phenomenon, conceptualizing โ€˜kitsch tourismโ€™ as a type of tourism that implies tourists' desires and psychological needs. In addition, this study has moved beyond the framing of tourists as consumers considering them to be producers of tourism discourse through social media such as blogs and investigated the ways in which these discourses conform to kitsch tourism. As the influence of social media on tourism gradually expands along with the development of the digital environment, kitsch tourism is also likely to spread. Some areas of future research in this field include analyses of various social media such as Instagram as well as blogs and physical, economic, and institutional changes in a region due to the tourism discourse of social media.๊ทธ๋™์•ˆ ํ•™๊ณ„์—์„œ ํ‚ค์น˜(kitsch)์˜ ํ™•์‚ฐ์— ๋Œ€ํ•ด ๋งŽ์€ ์šฐ๋ ค์™€ ๋น„ํŒ์„ ์ œ๊ธฐํ•˜์˜€์Œ์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ , ์˜ค๋Š˜๋‚  ํ‚ค์น˜๋Š” ์šฐ๋ฆฌ์˜ ์ผ์ƒ์— ๋”์šฑ ๊ต๋ฌ˜ํ•˜๊ฒŒ ์นจํˆฌํ•˜์—ฌ ํ•˜๋‚˜์˜ ๋ฌธํ™”๋กœ ๋ฐœ์ „ํ•˜๊ณ  ์žˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ์˜ค๋Š˜๋‚  ๊ด€๊ด‘ ์˜์—ญ์—์„œ ๋‚˜ํƒ€๋‚˜๋Š” ํ‚ค์น˜์˜ ์†์„ฑ์„ ๊ณ ์ฐฐํ•˜๊ณ  ์ด์— ๋Œ€ํ•œ ๊ฐœ๋…ํ™”๋ฅผ ์‹œ๋„ํ•˜๊ณ ์ž ํ•˜์˜€๋‹ค. ํŠนํžˆ, ๋””์ง€ํ„ธ ํ™˜๊ฒฝ์˜ ๋ฐœ๋‹ฌ๋กœ ์†Œ์…œ๋ฏธ๋””์–ด๊ฐ€ ์‚ฌํšŒ์  ๋‹ด๋ก ์„ ์ƒ์‚ฐํ•  ์ˆ˜ ์žˆ๊ฒŒ ๋œ ์ ์— ์ฃผ๋ชฉํ•˜์—ฌ ์ด๋Ÿฐ ๋‹ด๋ก ์ด ํ‚ค์น˜์  ๊ด€๊ด‘๊ณผ ์–ด๋–ป๊ฒŒ ์˜ํ•ฉํ•˜๋Š”์ง€๋ฅผ ํŒŒ์•…ํ•˜๊ณ ์ž ํ•˜์˜€๋‹ค. ์ด์— ๋ณธ ์—ฐ๊ตฌ๋Š” ๊ด€๊ด‘์˜ ๋ฌผ๋ฆฌ์  ๊ณต๊ฐ„์„ ์ƒ์„ฑํ•˜๋Š” ์ง€์ž์ฒด, ๊ด€๊ด‘ ๋‹ด๋ก ์„ ํ˜•์„ฑํ•˜๋Š” ์†Œ์…œ๋ฏธ๋””์–ด, ๋‹ด๋ก ์„ ์‹ค์ฒœํ•˜๋Š” ๊ด€๊ด‘๊ฐ์ด ๊ฐ๊ฐ์˜ ์—ญํ• ์„ ์ˆ˜ํ–‰ํ•˜๋Š” ๋ฐฉ์‹์„ ๋ถ„์„ํ•˜์—ฌ ์žฅ์†Œ์˜ ํ‚ค์น˜ํ™”์™€ ํ‚ค์น˜์  ๊ด€๊ด‘์˜ ํ˜•์„ฑ ๊ณผ์ •์„ ๊ทœ๋ช…ํ•˜๋Š” ๊ฒƒ์„ ๋ชฉ์ ์œผ๋กœ ํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ โ€˜์žฅ์†Œ์˜ ํ‚ค์น˜ํ™”(kitschization of place)โ€™๋ž€, ์žฅ์†Œ์˜ ์†Œ๋น„๊ฐ€์น˜๋ฅผ ์ฐฝ์ถœ์‹œํ‚ค๊ธฐ ์œ„ํ•ด ์ด์ƒํ™”๋œ ๋Œ€์ƒ์„ ๋ชจ๋ฐฉํ•จ์œผ๋กœ์จ ์žฅ์†Œ ๋ณธ๋ž˜์˜ ํŠน์„ฑ์œผ๋กœ๋ถ€ํ„ฐ ์ ์ฐจ ๋ถ„๋ฆฌ๋˜์–ด ์žฅ์†Œ์„ฑ์ด ์ด์ค‘ํ™”๋˜๋Š” ํ˜„์ƒ์œผ๋กœ ๋ฐ”๋ผ๋ณด์•˜๋‹ค. ๊ทธ๋ฆฌ๊ณ  โ€˜ํ‚ค์น˜์  ๊ด€๊ด‘(kitsch tourism)โ€™์€ ์žฅ์†Œ์˜ ๋ณธ์งˆ์ฃผ์˜์  ์žฅ์†Œ์„ฑ์ด๋‚˜ ์ง„์ •์„ฑ๊ณผ ๊ด€๊ณ„์—†์ด ์žฅ์†Œ์— ๋ง๋ถ™์—ฌ์ง„ ์ƒ์ง•์  ๊ฐ€์น˜์™€ ๋ฏธํ•™์ ยท์ด๊ตญ์  ์ด๋ฏธ์ง€๋ฅผ ์†Œ๋น„ํ•˜๋Š” ํƒœ๋„๋กœ ์ •์˜ํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ๊ตญ๋‚ด ๊ด€๊ด‘์—์„œ ๋“œ๋Ÿฌ๋‚˜๋Š” ํ‚ค์น˜์  ์†์„ฑ์„ ๋ณด๋‹ค ๋ช…ํ™•ํ•˜๊ฒŒ ํŒŒ์•…ํ•  ์ˆ˜ ์žˆ๋„๋ก ์ด๊ตญ์  ํ‚ค์น˜์— ์ฃผ๋ชฉํ•˜๊ณ ์ž ํ•˜์˜€์œผ๋ฉฐ, ์ง€์ž์ฒด์™€ ์†Œ์…œ๋ฏธ๋””์–ด๊ฐ€ ์ง€๋‹Œ ์—ญํ• ์˜ ์ฃผ๋„์„ฑ ์ฐจ์ด๋ฅผ ๋น„๊ตํ•˜๊ธฐ ์œ„ํ•ด ๋‚จํ•ด ๋…์ผ๋งˆ์„, ๋ถ€์‚ฐ ์žฅ๋ฆผํฌ๊ตฌ์™€ ํฐ์—ฌ์šธ๋ฌธํ™”๋งˆ์„์„ ์—ฐ๊ตฌ๋Œ€์ƒ์ง€๋กœ ์„ ์ •ํ•˜์˜€๋‹ค. ๊ทธ๋ฆฌ๊ณ  ๊ตญ๋‚ด์—์„œ ๊ด€๊ด‘ ์ •๋ณด์›์œผ๋กœ ๋„๋ฆฌ ์ด์šฉ๋˜๊ณ  ์žˆ๋Š” ๋ธ”๋กœ๊ทธ๋กœ๋ถ€ํ„ฐ ์‚ฌ์ง„ ์ด๋ฏธ์ง€์™€ ํ…์ŠคํŠธ ๋ฐ์ดํ„ฐ๋ฅผ ์ˆ˜์ง‘ํ•˜์—ฌ ๊ด€๊ด‘ ๋‹ด๋ก ์„ ๋ถ„์„ํ•˜์˜€๋‹ค. ์—ฐ๊ตฌ๊ฒฐ๊ณผ๋ฅผ ์š”์•ฝํ•˜๋ฉด ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. ์ฒซ์งธ, ์„ธ ์—ฐ๊ตฌ๋Œ€์ƒ์ง€๋Š” ์ง€์ž์ฒด์˜ ์ง€์—ญ๊ฒฝ์ œ ํ™œ์„ฑํ™” ๋ฐ ๊ด€๊ด‘๊ฐœ๋ฐœ ์‚ฌ์—…์— ์˜ํ•ด ์˜๋„์  ํ˜น์€ ์šฐ๋ฐœ์ ์œผ๋กœ ํ‚ค์น˜ํ™”๊ฐ€ ์ด๋ค„์กŒ๋‹ค. ๋…์ผ๋งˆ์„์˜ ๊ฒฝ์šฐ, ์ง€์ž์ฒด๊ฐ€ ๋‚จํ•ด์˜ ์žฅ์†Œ์„ฑ๊ณผ ๋ฌด๊ด€ํ•˜๊ฒŒ ๋…์ผ๊ตํฌ์˜ ์ •์ฐฉ์ดŒ์ด๋ผ๋Š” ์ƒˆ๋กœ์šด ์žฅ์†Œ์„ฑ์„ ์ฐฝ์กฐํ•˜์—ฌ ๊ณ„ํš์ ยท์˜๋„์ ์œผ๋กœ ์žฅ์†Œ๋ฅผ ํ‚ค์น˜ํ™”ํ•˜์˜€๋‹ค. ์žฅ๋ฆผํฌ๊ตฌ๋Š” ์†Œ์…œ๋ฏธ๋””์–ด๋ฅผ ํ†ตํ•ด ๋ฒ ๋„ค์น˜์•„๋ฅผ ๋‹ฎ์€ ํ’๊ฒฝ์œผ๋กœ ํฌ์ฐฉ๋œ ์šฐ๋ฐœ์  ์‚ฌ๊ฑด์— ์˜ํ•ด โ€˜๋ถ€๋„ค์น˜์•„(๋ถ€์‚ฐ์˜ ๋ฒ ๋„ค์น˜์•„)โ€™๋ผ๋Š” ์žฅ์†Œ์„ฑ์ด ์ƒˆ๋กญ๊ฒŒ ์ œ๋„ํ™”๋จ์œผ๋กœ์จ ํ‚ค์น˜ํ™”๊ฐ€ ์ด๋ค„์กŒ๋‹ค. ๋…์ผ๋งˆ์„์€ ์šฐ๋ฆฌ๋‚˜๋ผ์˜ ๊ทผ๋Œ€ํ™”๋ฅผ ์œ„ํ•ด ํ—Œ์‹ ํ•œ ํŒŒ๋… ๊ด‘๋ถ€์™€ ๊ฐ„ํ˜ธ์‚ฌ์˜ ๊ณ ๊ตญ ์ •์ฐฉ์ง€๋กœ ์˜๋ฏธ๋ฅผ ๋ถ€์—ฌํ•˜๋ฉด์„œ ํ‚ค์น˜ํ™”์˜ ์ •๋‹น์„ฑ์„ ํ™•๋ณดํ•œ ๋ฐ˜๋ฉด, ์žฅ๋ฆผํฌ๊ตฌ๋Š” ์†Œ์…œ๋ฏธ๋””์–ด์— ์˜ํ•ด ์ฐฝ์ถœ๋œ ๊ด€๊ด‘ ์ˆ˜์š”์— ๋Œ€์‘ํ•˜๋Š” ๋ชฉ์ ์œผ๋กœ ํ‚ค์น˜ํ™” ์ •์ฑ…์ด ์‹œํ–‰๋˜์—ˆ๋‹ค. ํฐ์—ฌ์šธ๋ฌธํ™”๋งˆ์„์€ ์ง€์ž์ฒด์˜ ๋…ธํ›„์ง€์—ญ ์žฌ์ƒ์‚ฌ์—…์— ์˜ํ•ด โ€˜๋ฌธํ™”์˜ˆ์ˆ ๋งˆ์„โ€™์ด๋ผ๋Š” ์ƒˆ๋กœ์šด ์žฅ์†Œ์„ฑ์ด ๋„์ž…๋˜์—ˆ์œผ๋ฉฐ, ์‚ฐํ† ๋ฆฌ๋‹ˆ ๋“ฑ์„ ๋ฒค์น˜๋งˆํ‚นํ•˜์—ฌ ๋งˆ์„ ๊ฒฝ๊ด€ ๊ฐœ์„  ๋ฐฉ์•ˆ์„ ์ˆ˜๋ฆฝํ•˜๊ณ  ์œ ์‚ฌํ•œ ๋ฐฉ์‹์œผ๋กœ ๊ฑด์ถ•๋ฌผ์„ ์ฑ„์ƒ‰ํ•˜๋ฉด์„œ ํ‚ค์น˜ํ™”๊ฐ€ ์ด๋ค„์กŒ๋‹ค. ์ง€์ž์ฒด๊ฐ€ ์‚ฐํ† ๋ฆฌ๋‹ˆ๋ฅผ ๋ฒค์น˜๋งˆํ‚นํ•œ ๊ฒƒ์€ ์™ธํ˜•์  ์œ ์‚ฌ์„ฑ์ด ์žฅ์†Œ๋ฅผ ๋งค๋ ฅ์ ์œผ๋กœ ํฌ์žฅํ•˜๋Š” ํ‚ค์น˜์˜ ํšจ๊ณผ๋ฅผ ๊ฐ€์ง€๊ณ  ์žˆ๋‹ค๋Š” ๊ฒƒ์„ ์ธ์ง€ํ•˜๊ณ , ๋ถ€๋ถ„์ ยท์„ ํƒ์  ๋ชจ๋ฐฉ์„ ์‹œ๋„ํ•œ ๊ฒƒ์œผ๋กœ ์ดํ•ดํ•  ์ˆ˜ ์žˆ๋‹ค. ๋‘˜์งธ, ๋ธ”๋กœ๊ฑฐ๋“ค์€ ์˜จ๋ผ์ธ์ƒ์—์„œ ์ง€์—ญ์˜ ์žฅ์†Œ์„ฑ๋ณด๋‹ค๋Š” ์™ธ์ ์œผ๋กœ ๋“œ๋Ÿฌ๋‚˜๋Š” ๋ฏธํ•™์ ยท์ด๊ตญ์  ์ด๋ฏธ์ง€๋ฅผ ๋ถ€๊ฐ์‹œ์ผœ ์žฅ์†Œ๋ฅผ ํ‘œ์ƒํ™”ํ•˜๊ณ , ์‹œ๊ฐ์  ์œ ํฌ๋‚˜ ๋†€์ด ์ค‘์‹ฌ์˜ ๊ด€๊ด‘ ๋ฐฉ์‹์„ ๊ทœ์ •ํ™”ํ•˜๋Š” ๋‹ด๋ก ์„ ํ˜•์„ฑํ•˜๋Š” ๊ฒƒ์œผ๋กœ ๋“œ๋Ÿฌ๋‚ฌ๋‹ค. ์ด๋“ค์€ ๋ฏธํ•™์„ฑ์ด ๋›ฐ์–ด๋‚œ ์žฅ์†Œ ์š”์†Œ๋“ค์„ ๊ธฐํ˜ธํ™”ํ•˜๋ฉฐ, ๋‹ค๋ฅธ ์žฅ์†Œ์˜ ์ƒ์ง•์  ์ด๋ฏธ์ง€๋ฅผ ํˆฌ์˜์‹œ์ผœ โ€˜โ—‹โ—‹์˜ ๋ฒ ๋„ค์น˜์•„โ€™, โ€˜โ—‹โ—‹์˜ ์‚ฐํ† ๋ฆฌ๋‹ˆโ€™ ๋“ฑ์œผ๋กœ ํ™˜์ƒ์„ฑ์„ ๋งŒ๋“ค์–ด๋‚ด๊ธฐ๋„ ํ•˜์˜€๋‹ค. ๋˜ํ•œ ๋ธ”๋กœ๊ทธ์—์„œ๋Š” ์ด๋ฅธ๋ฐ” โ€˜์ธ์ฆ์ƒทโ€™ ์žฅ์†Œ๊ฐ€ ๊ด€๊ด‘์ง€๋ฅผ ๋Œ€ํ‘œํ•˜๋Š” ๊ธฐํ˜ธ๋กœ ์ƒ์ง•ํ™”๋˜๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. โ€˜์ธ์ฆ์ƒทโ€™ ์žฅ์†Œ๋Š” ์—ญ์‚ฌยท๋ฌธํ™”์ ์œผ๋กœ ์˜๋ฏธ๊ฐ€ ๊นƒ๋“  ์žฅ์†Œ๊ฐ€ ์•„๋‹ˆ๋ผ, ์†Œ๋น„์ฃผ์ฒด๋“ค์ด ๊ทธ๋“ค๋งŒ์˜ ๋ฐฉ์‹์œผ๋กœ ์žฅ์†Œ๋ฅผ ํ•ด์„ํ•˜๊ณ  ๋ฏธํ•™์„ฑ์„ ๋ฐœ๊ตดํ•˜์—ฌ ์ƒˆ๋กญ๊ฒŒ ์˜๋ฏธ์™€ ๊ฐ€์น˜๋ฅผ ์ฐฝ์ถœ์‹œํ‚จ ์žฅ์†Œ์ด๋‹ค. ๋ธ”๋กœ๊ฑฐ๋“ค์€ ๋“œ๋ผ๋งˆํ‹ฑํ•˜๊ฒŒ ์—ฐ์ถœ๋œ ์ž์‹ ์˜ โ€˜์ธ์ฆ์ƒทโ€™์„ ๋ธ”๋กœ๊ทธ์— ๊ณต์œ ํ•˜๋ฉด์„œ ์ž ์žฌ๊ด€๊ด‘๊ฐ์˜ ๊ด€์‹ฌ๊ณผ ํฅ๋ฏธ๋ฅผ ์ด๋Œ๊ณ  ์ด๋Ÿฐ ์‚ฌ์ง„์„ ์ดฌ์˜ํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ๊ตฌ์ฒด์ ์œผ๋กœ ๊ฐ€๋ฅด์ณ์ฃผ๋ฉด์„œ ๋†€์ด์ฒ˜๋Ÿผ ์œ ํ–‰ํ™”ํ•˜๊ณ  ์žˆ๋‹ค. ์ด์ฒ˜๋Ÿผ ๋ธ”๋กœ๊ทธ๋Š” ์ง€์—ญ์˜ ์žฅ์†Œ์„ฑ์— ๋Œ€ํ•œ ์ดํ•ด๋ณด๋‹ค๋Š” ์‹œ๊ฐ์  ์œ ํฌ์™€ ๋†€์ด๋ฅผ ํ†ตํ•ด ๋ฏธํ•™์ ยท์ด๊ตญ์ ์ธ ์ด๋ฏธ์ง€๋ฅผ ์†Œ๋น„ํ•˜๋Š” ๊ด€๊ด‘์„ ์˜ˆ์ฐฌํ•˜๋ฉด์„œ ํ‚ค์น˜์  ๊ด€๊ด‘์„ ์œ ๋„ ํ˜น์€ ์ด‰์ง„ํ•˜๋Š” ๋‹ด๋ก ์„ ํ˜•์„ฑํ•˜๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์…‹์งธ, ๊ด€๊ด‘๊ฐ์€ ๋ธ”๋กœ๊ทธ๋ฅผ ํ†ตํ•ด ์ƒ์‚ฐ๋œ ๊ด€๊ด‘ ๋‹ด๋ก ์„ ์‹ค์ฒœํ•˜๋ฉด์„œ ๊ณต๊ฐ„์„ ์†Œ๋น„ํ•˜๋Š” ๋™์‹œ์— ํ‚ค์น˜์  ๊ด€๊ด‘์„ ๋ชธ์†Œ ํ–‰ํ•˜๋ฉด์„œ ๊ด€๊ด‘ ์†Œ๋น„๋ฌธํ™”๋ฅผ ํ˜•์„ฑํ•œ๋‹ค. ๋Œ€๋ถ€๋ถ„์˜ ๊ด€๊ด‘๊ฐ๋“ค์€ ๋ธ”๋กœ๊ทธ๋ฅผ ์ด์šฉํ•˜์—ฌ ๊ด€๊ด‘์„ ๊ณ„ํšํ•˜๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋Š”๋ฐ, ์ด๋“ค์ด ๋ธ”๋กœ๊ทธ ๋‹ด๋ก ๊ณผ ์˜ํ•ฉํ•˜๋ฉด์„œ ์œก์ฒด์  ํ–‰์œ„๋กœ ์‹ค์ฒœํ•œ ๊ด€๊ด‘์˜ ํ–‰ํƒœ๋Š” ํ‚ค์น˜์  ๊ด€๊ด‘์œผ๋กœ์„œ์˜ ํŠน์ง•์„ ๋ณด์˜€๋‹ค. ๊ทธ ํŠน์ง•์€ ํฌ๊ฒŒ ๊ธฐํ˜ธ์™€ ์ด๋ฏธ์ง€๋ฅผ ํ†ตํ•œ ์œ ํฌ, ์‹ฌ๋ฏธ์ฃผ์˜์  ๊ฐ์„ฑ ์†Œ๋น„, ๊ตฌ๋ณ„์ง“๊ธฐ, ๋Œ€๋ฆฌ๋งŒ์กฑ์  ๊ฒฝํ—˜์œผ๋กœ ์š”์•ฝํ•  ์ˆ˜ ์žˆ๋‹ค. ํ‚ค์น˜์  ๊ด€๊ด‘์€ ๋Œ€์ค‘๊ด€๊ด‘์ฒ˜๋Ÿผ ๋‹ด๋ก ํ™”๋œ ์žฅ์†Œ ๊ธฐํ˜ธ์™€ ์ด๋ฏธ์ง€๋ฅผ ํ†ตํ•ด ์žฅ์†Œ๋ฅผ ํ”ผ์ƒ์ ยท์ˆ˜๋™์ ์œผ๋กœ ๊ฒฝํ—˜ํ•œ๋‹ค๋Š” ์ ์—์„œ ๋ฌด์˜๋ฏธํ•˜๊ฒŒ ๋ณด์ผ ์ˆ˜ ์žˆ์œผ๋‚˜, ์ด๋“ค์˜ ํ–‰์œ„๋Š” ๋‹จ์ˆœํ•œ ์‹œ๊ฐ์  ๊ฒฝํ—˜์ด ์•„๋‹Œ ์œก์ฒด์  ์‹ค์ฒœ์„ ์ˆ˜๋ฐ˜ํ•จ์œผ๋กœ์จ ์ด์ „์˜ ๊ด€๊ด‘๊ณผ ๋‹ค๋ฅธ ํ–‰์œ„์ฃผ์ฒด์„ฑ์„ ์ง€๋‹Œ๋‹ค. ์ด๋ฅผํ…Œ๋ฉด, ๊ด€๊ด‘๊ฐ์€ ์†Œ์…œ๋ฏธ๋””์–ด์—์„œ ๊ทœ์ •๋œ โ€˜์ธ์ฆ์ƒทโ€™ ์žฅ์†Œ์—์„œ ํ•™์Šต๋œ ๋ฐฉ์‹์œผ๋กœ ์‚ฌ์ง„์„ ์ดฌ์˜ํ•˜์ง€๋งŒ, ์ด ๊ณผ์ •์—์„œ ์ž์‹ ์ด ์›ํ•˜๋Š” ์ด๋ฏธ์ง€๋ฅผ ์–ป๊ธฐ ์œ„ํ•ด ๋Šฅ๋™์ ์œผ๋กœ ๋ชธ์„ ์›€์ง์ด๋ฉด์„œ ์ž์•„๋ฅผ ํ‘œํ˜„ํ•œ๋‹ค. ๊ด€๊ด‘๊ฐ๋“ค์€ ์‚ฌ์ง„์„ ์ดฌ์˜ํ•  ๋•Œ ์žฅ์†Œ์˜ ๋ณธ์งˆ์  ์†์„ฑ์— ๊ด€์‹ฌ์„ ๋‘์ง€ ์•Š๊ณ , ๋™ํ–‰์ธ๊ณผ ํ•จ๊ป˜ ๊ณต๊ฐ„๊ณผ ์‹œ๊ฐ„์„ ๊ณต์œ ํ•˜๋Š” ์‚ฌํšŒ์  ์ƒํ˜ธ์ž‘์šฉ์— ์˜๋ฏธ๋ฅผ ๋ถ€์—ฌํ•˜์˜€๋‹ค. ์ด๋Ÿฐ ์ธก๋ฉด์—์„œ ์‹œ๊ฐ์ ์œผ๋กœ ๋“œ๋Ÿฌ๋‚˜๋Š” ๊ฒฝ๊ด€ ์ด๋ฏธ์ง€๋ฅผ ํ–ฅ์œ ํ•˜๋Š” ํ‚ค์น˜์  ๊ด€๊ด‘์€ ์ง„์ •์„ฑ์ด ๊ฒฐ์—ฌ๋œ ํ–‰์œ„๋ผ๊ธฐ๋ณด๋‹ค ๊ด€๊ด‘์˜ ์˜๋ฏธ๊ฐ€ ๊ด€๊ด‘๋Œ€์ƒ์—์„œ ๊ด€๊ด‘๊ฒฝํ—˜์„ ํ†ตํ•ด ์ด‰๋ฐœ๋˜๋Š” ์ง„์ •์„ฑ์œผ๋กœ ์ดˆ์ ์ด ์˜ฎ๊ฒจ์ง„ ๊ฒƒ์ด๋ผ๊ณ  ๋ณผ ์ˆ˜ ์žˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ํ‚ค์น˜์— ๋Œ€ํ•œ ๋ถ€์ •์ ์ธ ๋…ผ์˜๋“ค์„ ๋Œ€์‹ ํ•˜์—ฌ ํ‚ค์น˜๋ฅผ ํ•˜๋‚˜์˜ ์‚ฌํšŒ๋ฌธํ™”์  ํ˜„์ƒ์œผ๋กœ ์ดํ•ดํ•˜๊ณ  ํ‚ค์น˜์  ๊ด€๊ด‘ ํ–‰์œ„์— ๋‚ดํฌ๋œ ์ธ๊ฐ„์˜ ์š•๊ตฌ๋‚˜ ์‹ฌ๋ฆฌ๋ฅผ ํŒŒ์•…ํ•˜๊ณ ์ž ํ•˜์˜€์œผ๋ฉฐ ์ด์— ๋Œ€ํ•œ ๊ฐœ๋…ํ™”๋ฅผ ์‹œ๋„ํ•˜์˜€๋‹ค๋Š” ์ ์—์„œ ์˜์˜๋ฅผ ์ง€๋‹Œ๋‹ค. ๋˜ํ•œ ๊ด€๊ด‘๊ฐ์„ ์†Œ๋น„์ฃผ์ฒด๋กœ๋งŒ ๊ฐ„์ฃผํ•˜๋˜ ๊ธฐ์กด์˜ ๊ด€์ ์„ ํƒˆํ”ผํ•˜์—ฌ ๋ธ”๋กœ๊ทธ์™€ ๊ฐ™์€ ์†Œ์…œ๋ฏธ๋””์–ด๋ฅผ ํ†ตํ•ด ๊ด€๊ด‘ ๋‹ด๋ก ์„ ์ƒ์‚ฐํ•˜๋Š” ์ฃผ์ฒด๋กœ ๋ฐ”๋ผ๋ณด๊ณ , ์ด๋Ÿฌํ•œ ๋‹ด๋ก ์ด ์–ด๋–ค ์ธก๋ฉด์—์„œ ํ‚ค์น˜์  ๊ด€๊ด‘๊ณผ ์˜ํ•ฉํ•˜๋Š”์ง€๋ฅผ ๊ทœ๋ช…ํ•˜์˜€๋‹ค. ๋””์ง€ํ„ธ ํ™˜๊ฒฝ์˜ ๋ฐœ์ „๊ณผ ํ•จ๊ป˜ ์†Œ์…œ๋ฏธ๋””์–ด๊ฐ€ ๊ด€๊ด‘ ์˜์—ญ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ๋ ฅ์ด ์ ์ฐจ ํ™•๋Œ€๋จ์— ๋”ฐ๋ผ ์•ž์œผ๋กœ ํ‚ค์น˜์  ๊ด€๊ด‘์ด ๋”์šฑ ํ™•์‚ฐ๋  ๊ฐ€๋Šฅ์„ฑ์ด ์žˆ๊ธฐ ๋•Œ๋ฌธ์— ํ–ฅํ›„ ๋ธ”๋กœ๊ทธ๋ฟ ์•„๋‹ˆ๋ผ ์ธ์Šคํƒ€๊ทธ๋žจ ๋“ฑ ๋‹ค์–‘ํ•œ ์†Œ์…œ๋ฏธ๋””์–ด์— ๋Œ€ํ•œ ๋ถ„์„์ด ํ•„์š”ํ•  ๊ฒƒ์œผ๋กœ ๋ณด์ด๋ฉฐ ์†Œ์…œ๋ฏธ๋””์–ด์˜ ๊ด€๊ด‘ ๋‹ด๋ก ์— ์˜ํ•œ ์ง€์—ญ์˜ ๋ฌผ๋ฆฌ์ ยท๊ฒฝ์ œ์ ยท์ œ๋„์  ๋ณ€ํ™”๋ฅผ ๊ทœ๋ช…ํ•˜๋Š” ์—ฐ๊ตฌ๋“ค์ด ํญ๋„“๊ฒŒ ์ด๋ค„์ ธ์•ผ ํ•˜๊ฒ ๋‹ค.์ œ 1 ์žฅ ์„œ๋ก  1 ์ œ 1 ์ ˆ ๋ฌธ์ œ์ œ๊ธฐ์™€ ์—ฐ๊ตฌ๋ชฉ์  1 ์ œ 2 ์ ˆ ์—ฐ๊ตฌ์ง€์—ญ๊ณผ ์—ฐ๊ตฌ๋ฐฉ๋ฒ• 7 1. ์—ฐ๊ตฌ์ง€์—ญ ์„ ์ • 7 2. ์—ฐ๊ตฌ๋ฐฉ๋ฒ• 10 ์ œ 3 ์ ˆ ๋…ผ๋ฌธ์˜ ๊ตฌ์„ฑ 14 ์ œ 2 ์žฅ ์ด๋ก ์  ๊ณ ์ฐฐ 16 ์ œ 1 ์ ˆ ํ‚ค์น˜์˜ ๊ฐœ๋…๊ณผ ์žฅ์†Œ์˜ ํ‚ค์น˜ํ™” 16 1. ํ‚ค์น˜์˜ ๊ฐœ๋… ๋ฐ ๋ฐœ๋‹ฌ๋ฐฐ๊ฒฝ 16 2. ๊ด€๊ด‘์— ์˜ํ•œ ์žฅ์†Œ์˜ ํ‚ค์น˜ํ™” 26 ์ œ 2 ์ ˆ ํ‚ค์น˜์  ๊ด€๊ด‘๊ณผ ์‚ฌํšŒ๋ฌธํ™”์  ํ˜•์„ฑ 34 1. ํƒˆ๊ทผ๋Œ€๊ด€๊ด‘์œผ๋กœ์˜ ์ „ํ™˜๊ณผ ์ง„์ •์„ฑ์˜ ํ•ด์ฒด 34 2. ํ‚ค์น˜์  ๊ด€๊ด‘์˜ ๊ฐœ๋… ๋ฐ ํŠน์ง• 39 3. ๊ด€๊ด‘ ์†Œ๋น„๋ฌธํ™”์˜ ํ˜•์„ฑ ๊ณผ์ •: ๊ด€๊ด‘๊ฐ์˜ ์‹œ์„  ๊ตฌ์„ฑ 46 ์ œ 3 ์ ˆ ๊ด€๊ด‘๋‹ด๋ก  ์ƒ์‚ฐ๋งค์ฒด๋กœ์„œ ์†Œ์…œ๋ฏธ๋””์–ด์˜ ์†์„ฑ 52 1. ์†Œ์…œ๋ฏธ๋””์–ด์˜ ๊ฐœ๋… ๋ฐ ์ •๋ณด์  ํŠน์„ฑ 52 2. ๊ด€๊ด‘์—์„œ ์†Œ์…œ๋ฏธ๋””์–ด์˜ ์—ญํ•  60 3. ๋ธ”๋กœ๊ทธ ๊ด€๊ด‘๋‹ด๋ก ์˜ ์žฌํ˜„์„ฑ๊ณผ ์ˆ˜ํ–‰์„ฑ 67 ์ œ 4 ์ ˆ ์—ฐ๊ตฌ์˜ ๋ถ„์„ํ‹€ 75 1. ์—ฐ๊ตฌ๋ถ„์„ํ‹€ 75 2. ๋ถ„์„๋ฐฉ๋ฒ• 76 ์ œ 3 ์žฅ ์‚ฌ๋ก€์ง€์—ญ์˜ ํ‚ค์น˜ํ™” ๊ณผ์ • 89 ์ œ 1 ์ ˆ ๋‚จํ•ด ๋…์ผ๋งˆ์„: ๊ณ„ํš์  ํ‚ค์น˜ํ™” 89 1. ์žฅ์†Œ์„ฑ์˜ ์ด์ค‘ํ™”: ๋‚จํ•ด์™€ ๋…์ผ 89 2. ์ด์ƒํ™”๋œ ๋Œ€์ƒ์˜ ๋ชจ๋ฐฉ: ๋…์ผ์— ๋Œ€ํ•œ ์ •๊ตํ•œ ๋ชจ๋ฐฉ 95 3. ๊ฒฝ์ œ์  ํšจ์šฉ์„ฑ ์ฐฝ์กฐ: ๋…์ผ์˜ ์ƒํ’ˆํ™” 103 ์ œ 2 ์ ˆ ๋ถ€์‚ฐ ์žฅ๋ฆผํฌ๊ตฌ: ์šฐ๋ฐœ์  ํ‚ค์น˜ํ™” 107 1. ์žฅ์†Œ์„ฑ์˜ ์ด์ค‘ํ™”: ์žฅ๋ฆผํ•ญ๊ณผ ๋ถ€๋„ค์น˜์•„ 107 2. ์ด์ƒํ™”๋œ ๋Œ€์ƒ์˜ ๋ชจ๋ฐฉ: ์œ ๋Ÿฝ์˜ ์ด๊ตญ์  ๊ธฐํ˜ธ๋“ค์˜ ๊ฒฐํ•ฉ 114 3. ๊ฒฝ์ œ์  ํšจ์šฉ์„ฑ ์ฐฝ์กฐ: ๋ถ€๋„ค์น˜์•„์˜ ์ƒํ’ˆํ™” 116 ์ œ 3 ์ ˆ ๋ถ€์‚ฐ ํฐ์—ฌ์šธ๋ฌธํ™”๋งˆ์„: ๋ถ€๋ถ„์  ํ‚ค์น˜ํ™” 121 1. ์žฅ์†Œ์„ฑ์˜ ์ด์ค‘ํ™”: ํ”ผ๋‚œ๋ฏผ์ดŒ๊ณผ ๋ฌธํ™”์˜ˆ์ˆ ๋งˆ์„ 121 2. ์ด์ƒํ™”๋œ ๋Œ€์ƒ์˜ ๋ชจ๋ฐฉ: ์‚ฐํ† ๋ฆฌ๋‹ˆ ๋“ฑ์˜ ๊ฒฝ๊ด€ ์ด๋ฏธ์ง€ ๋ชจ๋ฐฉ 128 3. ๊ฒฝ์ œ์  ํšจ์šฉ์„ฑ ์ฐฝ์กฐ: ๋ฒฝํ™” ํ™•๋Œ€ ๋ฐ ๊ด€๊ด‘ ์ž์›ํ™” 132 ์ œ 4 ์ ˆ ์†Œ๊ฒฐ 139 ์ œ 4 ์žฅ ๋ธ”๋กœ๊ทธ์˜ ๊ด€๊ด‘ ๋‹ด๋ก  ํ˜•์„ฑ 144 ์ œ 1 ์ ˆ ๊ด€๊ด‘๋‹ด๋ก  ์ƒ์‚ฐ์ฃผ์ฒด๋กœ์„œ ๋ธ”๋กœ๊ฑฐ์˜ ์†์„ฑ 144 ์ œ 2 ์ ˆ ๋‚จํ•ด ๋…์ผ๋งˆ์„: ์ด๊ตญ์ ์ธ ๋…์ผ ํ…Œ๋งˆ๊ด€๊ด‘์ง€ 154 1. ์žฅ์†Œ์˜ ๊ธฐํ˜ธ ์ƒ์‚ฐ: ๋…์ผ์‹ ์Œ์‹๊ณผ ์ฃผํƒ 154 2. ๊ด€๊ด‘ ๋ฐฉ์‹์˜ ๊ทœ์ •ํ™”: ๋…์ผ์Œ์‹์„ ์†Œ๋น„ํ•˜๋Š” ์ด๊ตญ์  ์žฅ์†Œ 164 ์ œ 3 ์ ˆ ๋ถ€์‚ฐ ์žฅ๋ฆผํฌ๊ตฌ: ์ด์ƒ‰์  ์‚ฌ์ง„ ๋ช…์†Œ 175 1. ์žฅ์†Œ์˜ ๊ธฐํ˜ธ ์ƒ์‚ฐ: ์•Œ๋ก๋‹ฌ๋กํ•œ ์–ด๊ตฌ์ฐฝ๊ณ  175 2. ๊ด€๊ด‘ ๋ฐฉ์‹์˜ ๊ทœ์ •ํ™”: โ€˜์ธ์ƒ์ƒทโ€™ ์ดฌ์˜์†Œ 184 ์ œ 4 ์ ˆ ๋ถ€์‚ฐ ํฐ์—ฌ์šธ๋ฌธํ™”๋งˆ์„: ๋ฏธํ•™์  ํ’๊ฒฝ์˜ ์žฅ์†Œ 197 1. ์žฅ์†Œ์˜ ๊ธฐํ˜ธ ์ƒ์‚ฐ: ์นดํŽ˜, โ€˜๋ณ€ํ˜ธ์ธโ€™์ดฌ์˜์†Œ, ํ•ด์•ˆํ„ฐ๋„ 197 2. ๊ด€๊ด‘ ๋ฐฉ์‹์˜ ๊ทœ์ •ํ™”: โ€˜์นดํ๋ง›์ง‘โ€™๊ณผ ์‚ฌ์ง„๋ช…์†Œ 206 ์ œ 5 ์ ˆ ์†Œ๊ฒฐ 218 ์ œ 5 ์žฅ ๊ด€๊ด‘๊ฐ์˜ ํ‚ค์น˜์  ๊ด€๊ด‘ ์ˆ˜ํ–‰๊ณผ ์‚ฌํšŒ๋ฌธํ™” ํ˜•์„ฑ 222 ์ œ 1 ์ ˆ ๊ด€๊ด‘๋‹ด๋ก  ์‹ค์ฒœ์ฃผ์ฒด๋กœ์„œ ๊ด€๊ด‘๊ฐ์˜ ์†์„ฑ 222 ์ œ 2 ์ ˆ ๊ด€๊ด‘๊ฐ์˜ ํ‚ค์น˜์  ๊ด€๊ด‘ ์ˆ˜ํ–‰ 228 1. ๊ธฐํ˜ธ์™€ ์ด๋ฏธ์ง€๋ฅผ ํ†ตํ•œ ์œ ํฌ 228 2. ์‹ฌ๋ฏธ์ฃผ์˜์  ๊ฐ์„ฑ ์†Œ๋น„ 238 3. ๊ตฌ๋ณ„์ง“๊ธฐ 242 4. ๋Œ€๋ฆฌ๋งŒ์กฑ์  ๊ฒฝํ—˜ 250 ์ œ 3 ์ ˆ ์†Œ๊ฒฐ 254 ์ œ 6 ์žฅ ๊ฒฐ๋ก  258 ์ œ 1 ์ ˆ ์ข…ํ•ฉํ† ๋ก  ๋ฐ ์ œ์–ธ 258 ์ œ 2 ์ ˆ ์š”์•ฝ ๋ฐ ๊ฒฐ๋ก  269 ์ฐธ๊ณ ๋ฌธํ—Œ 278 ๋ถ€๋ก 305๋ฐ•

    ๋””์ง€ํ„ธ ์ฐฝ์˜ ๋…ธ๋™์ž๋Š” ์–ด๋–ป๊ฒŒ ๋ถˆ์•ˆ์ •์„ฑ์— ๋Œ€์‘ํ•˜๋Š”๊ฐ€? : ํ•œ๊ตญ์˜ ๊ฒŒ์ž„ ๊ฐœ๋ฐœ์ž๋“ค์„ ์ค‘์‹ฌ์œผ๋กœ

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์‚ฌํšŒ๊ณผํ•™๋Œ€ํ•™ ์–ธ๋ก ์ •๋ณดํ•™๊ณผ, 2018. 8. ๊ฐ•๋ช…๊ตฌ.์ด ์—ฐ๊ตฌ๋Š” ์˜ค๋Š˜๋‚  ํ•œ๊ตญ์˜ ๊ฒŒ์ž„ ๊ฐœ๋ฐœ์ž๋“ค์„ ์ค‘์‹ฌ์œผ๋กœ, ๋””์ง€ํ„ธ ์ฐฝ์˜ ๋…ธ๋™์ž(digital creative worker)๋“ค์˜ ๋ถˆ์•ˆ์ •์„ฑ์ด ์–ด๋–ป๊ฒŒ ์‹ฌํ™”๋˜๊ณ  ์žˆ๋Š”์ง€๋ฅผ ๊ทœ๋ช…ํ•˜๊ณ , ๊ทธ์— ๋Œ€ํ•œ ๋Œ€์‘๊ณผ ๊ทน๋ณต์˜ ๊ฐ€๋Šฅ์„ฑ์„ ํƒ๊ตฌํ•œ๋‹ค. ์ตœ๊ทผ๊นŒ์ง€ ํ•œ๊ตญ์˜ ๊ฒŒ์ž„ ์‚ฐ์—…์€ ์–‘์  ๊ทœ๋ชจ๋ฉด์—์„œ๋Š” ํฌ๊ฒŒ ์„ฑ์žฅํ–ˆ์ง€๋งŒ, ๊ทธ ์ด๋ฉด์—๋Š” ๋…ธ๋™ํ™˜๊ฒฝ๊ณผ ๊ด€๋ จํ•˜์—ฌ ์—ฌ๋Ÿฌ ๋ฌธ์ œ์ ๊ณผ ํ•œ๊ณ„๋ฅผ ๋“œ๋Ÿฌ๋‚ด๊ณ  ์žˆ๋‹ค. ๊ฒฉํ™”๋œ ๊ธ€๋กœ๋ฒŒ ๊ฒฝ์Ÿ๊ณผ ๋ชจ๋ฐ”์ผ ๊ฒŒ์ž„ ์‹œ์žฅ์œผ๋กœ์˜ ๊ธ‰๊ฒฉํ•œ ์ด๋™ ์†์—์„œ, ํ•œ๊ตญ ๊ฒŒ์ž„ ์‚ฐ์—…์€ ๊ณผ๊ฑฐ๋ณด๋‹ค ๋” ํฐ ๋ฆฌ์Šคํฌ์™€ ๋ถˆ์•ˆ์ •์„ฑ์— ์ง๋ฉดํ•˜๊ฒŒ ๋˜์—ˆ๋‹ค. ๊ทธ ๋ถ€๋‹ด์€ ๊ณ ์Šค๋ž€ํžˆ ๊ฒŒ์ž„ ๊ฐœ๋ฐœ์ž๋“ค์—๊ฒŒ ์ „๋‹ฌ๋˜์–ด, ๋…ธ๋™ํ™˜๊ฒฝ๊ณผ ์ž‘์—…๊ด€ํ–‰์—์„œ์˜ ๋ฌธ์ œ์ ์œผ๋กœ ๋‚˜ํƒ€๋‚˜๊ธฐ ์‹œ์ž‘ํ–ˆ๋‹ค. ํŠนํžˆ ์ตœ๊ทผ ํ•œ๊ตญ ์‚ฌํšŒ์—์„œ๋Š” ๊ณผ๋„ํ•œ ์•ผ๊ทผ๊ณผ ์—ด์•…ํ•œ ์ฒ˜์šฐ๋กœ ์ธํ•ด ๊ธฐ๋ณธ์ ์ธ ๋…ธ๋™๊ถŒ์„ ๋ณด์žฅ๋ฐ›์ง€ ๋ชปํ•˜๊ฑฐ๋‚˜, ๋ถ„์ ˆํ™”๋˜๊ณ  ํŒŒํŽธํ™”๋œ ๋‹จ์ˆœ๋ฐ˜๋ณต ๋…ธ๋™์œผ๋กœ ์ธํ•ด ์ผ์˜ ๊ฐ€์น˜์™€ ์˜๋ฏธ๋ฅผ ์ƒ์‹คํ•˜๊ฒŒ ๋œ ๊ฒŒ์ž„ ๊ฐœ๋ฐœ์ž๋“ค์— ๋Œ€ํ•œ ๋ณด๋„๊ฐ€ ์ด์–ด์กŒ๋‹ค. ์ด์ œ ๋” ์ด์ƒ ๊ฒŒ์ž„ ์‚ฐ์—… ๋…ธ๋™์€ ์ž์œ ๋ถ„๋ฐฉํ•˜๊ณ  ์žฌ๋Šฅ ์žˆ๋Š” ์ธ๋ ฅ๋“ค์ด ์ˆ˜ํ–‰ํ•˜๋Š” ์ฐฝ์˜์ ์ด๊ณ  ์ž์œจ์ ์ธ ์ž‘์—…์ด ์•„๋‹ˆ๋ผ, ๊ณ ๋„ํ™”๋œ ์ฐฉ์ทจ๊ตฌ์กฐ ์†์—์„œ ์ขŒ์ ˆ๊ณผ ์ ˆ๋ง๊ฐ์„ ์•ˆ๊ฒจ์ฃผ๋Š” ๊ณ ํ†ต์Šค๋Ÿฌ์šด ๋…ธ๋™์ด ๋˜์–ด ๊ฐ€๊ณ  ์žˆ์—ˆ๋‹ค. ๋”ฐ๋ผ์„œ ๊ฒŒ์ž„ ์‚ฐ์—…์˜ ํ˜„์‹ค์€ ์‚ฐ์—…๊ทœ๋ชจ๋‚˜ ์ˆ˜์ถœ ์ฐจ์›์ด ์•„๋‹Œ, ๋…ธ๋™ํ™˜๊ฒฝ๊ณผ ๋…ธ๋™์ฃผ์ฒด์˜ ๋ฌธ์ œ์—์„œ๋ถ€ํ„ฐ ์ ‘๊ทผํ•ด์•ผ ํ•  ํ•„์š”์„ฑ์ด ์ œ๊ธฐ๋œ๋‹ค. ๊ตฌ์ฒด์ ์œผ๋กœ ์ด ๋…ผ๋ฌธ์€ ๋‹ค์Œ์˜ ์„ธ ๊ฐ€์ง€ ์—ฐ๊ตฌ๋ฌธ์ œ๋ฅผ ์ œ๊ธฐํ•œ๋‹ค. ์ฒซ์งธ, ๊ฒŒ์ž„ ์‚ฐ์—…์˜ ์ƒ์‚ฐ ํ”„๋กœ์„ธ์Šค ๋ฐ ๋…ธ๋™ ์กฐ์งํ™”์˜ ์–‘์ƒ์€ ์–ด๋– ํ•œ๊ฐ€? ์ด ๋ฌผ์Œ์€ ๊ฒŒ์ž„ ์‚ฐ์—…์˜ ์ƒ์‚ฐ ์ธ๋ ฅ์ด ์–ด๋–ป๊ฒŒ ๊ตฌ์„ฑ๋˜๊ณ  ์กฐ์งํ™”๋˜๋Š”์ง€, ๊ฐœ๋ฐœ๊ณผ์ •์ด ์–ด๋–ป๊ฒŒ ์ฒด๊ณ„์ ์œผ๋กœ ๊ด€๋ฆฌ๋˜๊ณ  ์ง„ํ–‰๋˜๋Š”์ง€, ๋˜ ๊ทธ ๊ณผ์ •์—์„œ ํ˜•์„ฑ๋œ ๊ฐœ๋ฐœ์ž๋“ค์˜ ๋…ธ๋™๋ฌธํ™”๋Š” ์–ด๋–ค ํŠน์ง•์„ ๋ ๋Š”์ง€ ์‚ดํŽด๋ณด๋ ค๋Š” ๊ฒƒ์ด๋‹ค. ๋‘˜์งธ, ๊ฒŒ์ž„ ์ƒ์‚ฐ ํ™˜๊ฒฝ์˜ ๋ณ€ํ™” ์†์—์„œ ๊ฒŒ์ž„ ๊ฐœ๋ฐœ์ž๋“ค์˜ ๋ถˆ์•ˆ์ •์„ฑ(precarity)์€ ์–ด๋–ป๊ฒŒ ์‹ฌํ™”๋˜๋Š”๊ฐ€? ์ด๊ฒƒ์€ 2010๋…„๋Œ€ ์ดํ›„ ํ•œ๊ตญ ๊ฒŒ์ž„ ์‚ฐ์—…์˜ ๋ณ€ํ™”๊ฐ€ ์–ด๋–ป๊ฒŒ ์ผ์–ด๋‚ฌ์œผ๋ฉฐ, ๊ทธ ๊ณผ์ •์—์„œ ๊ฒŒ์ž„ ๊ฐœ๋ฐœ์ž๋“ค์ด ๋ณธ๋ž˜์ ์œผ๋กœ ๊ฐ€์ง€๊ณ  ์žˆ์—ˆ๋˜ ๋ถˆ์•ˆ์ •์„ฑ์ด ์–ด๋–ป๊ฒŒ ์‹ฌํ™”๋˜์—ˆ๋Š”๊ฐ€๋ฅผ ๋ฌป๋Š” ๊ฒƒ์ด๋‹ค. ์…‹์งธ, ๊ฒŒ์ž„ ๊ฐœ๋ฐœ์ž๋“ค์€ ๋ถˆ์•ˆ์ •์„ฑ์— ์–ด๋–ป๊ฒŒ ๋Œ€์‘ํ•˜๋Š”๊ฐ€? ์ด๊ฒƒ์€ ๊ฒŒ์ž„ ๊ฐœ๋ฐœ์ž๋“ค์ด ์ž์‹ ์ด ์ฒ˜ํ•œ ๋…ธ๋™์˜ ๋ถˆ์•ˆ์ •์„ฑ์— ์–ด๋–ป๊ฒŒ ๋Œ€์‘ํ•˜๊ณ  ๋‚˜์•„๊ฐ€ ๊ทธ๊ฒƒ์„ ๊ทน๋ณตํ•  ์ˆ˜ ์žˆ๋Š”๊ฐ€๋ฅผ ๋ชจ์ƒ‰ํ•˜๋ ค๋Š” ๊ฒƒ์ด๋‹ค. ์œ„์˜ ๋ฌธ์ œ๋“ค์— ๋Œ€๋‹ตํ•˜๊ธฐ ์œ„ํ•ด, ์—ฐ๊ตฌ์ž๋Š” ์œ ๋ช… ๋Œ€๊ธฐ์—… ์†Œ์† ๊ฒŒ์ž„ ๊ฐœ๋ฐœ์ž์—์„œ๋ถ€ํ„ฐ ์ค‘์†Œ๊ทœ๋ชจ ๊ฐœ๋ฐœ์‚ฌ, ์Šคํƒ€ํŠธ์—… ๋ฐ 1์ธ ๊ฐœ๋ฐœ์ž๋“ค์— ์ด๋ฅด๊ธฐ๊นŒ์ง€ ๋‹ค์–‘ํ•œ ์ง๊ตฐ๊ณผ ๋ถ„์•ผ์˜ ๊ฐœ๋ฐœ์ž๋“ค 42๋ช…์— ๋Œ€ํ•œ ์‹ฌ์ธต ์ธํ„ฐ๋ทฐ์™€, ๊ฒŒ์ž„ ์ƒ์‚ฐ์ด ์ด๋ฃจ์–ด์ง€๋Š” ์ž‘์—… ํ˜„์žฅ์— ๋Œ€ํ•œ ์ฐธ์—ฌ๊ด€์ฐฐ์„ ์ˆ˜ํ–‰ํ–ˆ๋‹ค. ๋˜ํ•œ ๊ฒŒ์ž„ ์‚ฐ์—… ๊ด€๋ จ ๊ฐ์ข… ํ–‰์‚ฌ์™€ ์ปจํผ๋Ÿฐ์Šค, ์„ธ๋ฏธ๋‚˜์— ์ฐธ์„ํ•˜์—ฌ ํ˜„์žฅ ๊ด€๊ณ„์ž๋“ค๊ณผ ๊ต๋ฅ˜ํ•˜๋ฉฐ ์ž๋ฃŒ ์ˆ˜์ง‘์„ ์ง„ํ–‰ํ–ˆ๋‹ค. ์ด๋ ‡๊ฒŒ ์ˆ˜์ง‘๋œ ์ž๋ฃŒ๋“ค์„ ๋ฐ”ํƒ•์œผ๋กœ ์‹ฌ์ธต์ ์ธ ์‚ฌ๋ก€์—ฐ๊ตฌ๋ฅผ ์‹ค์‹œํ–ˆ๋‹ค. ์ด๋ก ์ ์œผ๋กœ, ์ด ์—ฐ๊ตฌ๋Š” ๋ถˆ์•ˆ์ •์„ฑ(precarity) ๊ฐœ๋…๊ณผ ๋””์ง€ํ„ธ ์ฐฝ์˜ ๋…ธ๋™(digital creative work) ๊ฐœ๋…์„ ์ค‘์‹ฌ์œผ๋กœ ๊ฒŒ์ž„ ๊ฐœ๋ฐœ์ž๋“ค์ด ์ฒ˜ํ•œ ๊ตฌ์กฐ์  ํ•œ๊ณ„ ์กฐ๊ฑด๊ณผ, ๊ทธ๊ฒƒ์„ ๊ทน๋ณตํ•  ์ˆ˜ ์žˆ๋Š” ์ž ์žฌ๋ ฅ์— ๋Œ€ํ•ด ํƒ๊ตฌํ•˜๋ ค ํ–ˆ๋‹ค. ์ด๋ก ์  ๋…ผ์˜ ๋ถ€๋ถ„์—์„œ๋Š” ๋จผ์ €, ๋น„ํŒ์  ๋…ธ๋™์‚ฌํšŒํ•™์˜ ์ „ํ†ต์ ์ธ ํ…Œ๋งˆ์˜€๋˜, ๋…ธ๋™ ๋ถ„์—…๊ณผ ํƒˆ์ˆ™๋ จ์œผ๋กœ ์ธํ•œ ๋…ธ๋™์†Œ์™ธ ๋ฌธ์ œ๋ฅผ ๊ฒ€ํ† ํ–ˆ๋‹ค. ์ด์–ด์„œ ์˜ค๋Š˜๋‚  ์ฐฝ์˜ ์‚ฐ์—… ๋…ธ๋™์ฃผ์ฒด๋ฅผ ๋Š์ž„์—†์ด ๋ถˆ์•ˆ์ •ํ•˜๊ฒŒ ๋งŒ๋“ค๊ณ  ์žˆ๋Š”, ํ›„๊ธฐ ์ž๋ณธ์ฃผ์˜ ์‹œ๋Œ€์˜ ์œ ์—ฐํ™”๋œ ๋…ธ๋™๊ตฌ์กฐ์™€ ์‹ ์ž์œ ์ฃผ์˜์  ํ†ต์น˜์„ฑ์˜ ๋ฌธ์ œ์— ๋Œ€ํ•ด ๋…ผ์˜ํ–ˆ๋‹ค. ๊ฒฐ๊ตญ ์ด ์—ฐ๊ตฌ๊ฐ€ ์ฃผ๋ชฉํ•˜๋Š” ๊ฒŒ์ž„ ๊ฐœ๋ฐœ์ž๋“ค์˜ ๋ถˆ์•ˆ์ •์„ฑ(precarity)์˜ ์–‘์ƒ์€, ์ „ํ†ต์  ์‚ฐ์—…์ž๋ณธ์ฃผ์˜ ์‹œ๋Œ€์˜ ๋ถ„์—…ํ™”ยทํƒˆ์ˆ™๋ จํ™”๋กœ ์ธํ•œ ๋…ธ๋™์†Œ์™ธ์™€, ์‹ ์ž์œ ์ฃผ์˜ ํ†ต์น˜์„ฑ์ด ์ž‘๋™ํ•˜๋Š” ํ›„๊ธฐ ์ž๋ณธ์ฃผ์˜ ์‹œ๋Œ€์˜ ๊ทผ์›์ ์ธ ๋ถˆ์•ˆ์ •์„ฑ, ์ด ๋‘ ๊ฐ€์ง€๊ฐ€ ์ค‘์ฒฉ๋˜๊ณ  ๊ฒฐํ•ฉ๋œ ์ƒํƒœ๋กœ ๋‚˜ํƒ€๋‚œ๋‹ค๋Š” ์ ์ด ํŠน์ง•์ด๋‹ค. ์—ฐ๊ตฌ์ž๋Š” ์ด๋Ÿฌํ•œ ์ค‘์ฒฉ๋œ ํ˜•ํƒœ์˜ ๋ถˆ์•ˆ์ •์„ฑ์ด ๋‹น๋Œ€ ํ•œ๊ตญ์˜ ๊ฒŒ์ž„ ๊ฐœ๋ฐœ์ž๋“ค์ด ๊ฒฝํ—˜ํ•˜๋Š” ๊ณ ํ†ต์˜ ์‹ค์ฒด๋ผ๊ณ  ํ•ด์„ํ•œ๋‹ค. ๋‹ค๋ฅธ ํ•œ ํŽธ์œผ๋กœ๋Š”, ์ฐฝ์˜ ๋…ธ๋™(creative labor), ๋น„๋ฌผ์งˆ ๋…ธ๋™(immaterial labor), ๋””์ง€ํ„ธ ๋…ธ๋™(digital labor) ๋“ฑ์— ๊ด€ํ•œ ๊ธฐ์กด์˜ ์ด๋ก ์  ํ๋ฆ„์„ ๋น„ํŒ์ ์œผ๋กœ ๊ฒ€ํ† ํ•˜๊ณ  ์ค‘์š”ํ•œ ํ†ต์ฐฐ๋“ค์„ ์ข…ํ•ฉํ•˜์—ฌ, ๋””์ง€ํ„ธ ์ฐฝ์˜ ์ž‘์—… ๊ฐœ๋…์„ ์ œ์•ˆํ•˜์˜€๋‹ค. ์ด๊ฒƒ์€ ๊ฒŒ์ž„ ๊ฐœ๋ฐœ์ž๋“ค์ด ํŒŒํŽธํ™”๋œ ๋…ธ๋™(labor)์˜ ํ•œ๊ณ„๋ฅผ ๋„˜์–ด, ๋ณด๋‹ค ์ž์œจ์ ์ธ ์œตํ•ฉ์  ์ž‘์—…(converged work)์„ ์ˆ˜ํ–‰ํ•  ์ˆ˜ ์žˆ๋Š”, ๊ทธ๋ฆฌ๊ณ  ๋„คํŠธ์›Œํฌํ™”๋œ ์ฐฝ์˜์  ํ˜‘์—…(network creative collaboration)์„ ํ†ตํ•ด ๊ฒŒ์ž„ ์ƒ์‚ฐ์„ ์œ„ํ•œ ๋ฌธํ™”์  ๊ณต๋™์ฒด๋ฅผ ํ˜•์„ฑํ•  ์ˆ˜ ์žˆ๋Š” ์ž ์žฌ๋ ฅ๊ณผ ์—ญ๋Ÿ‰์„ ํ•จ์ถ•ํ•˜๋Š” ๊ฐœ๋…์ด๋‹ค. ๋…ผ๋ฌธ์˜ ์ฃผ์š” ์—ฐ๊ตฌ๊ฒฐ๊ณผ๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. ์ฒซ์งธ, ๊ฒŒ์ž„์ƒ์‚ฐ์„ ์œ„ํ•ด ์ธ๋ ฅ์ด ์กฐ์งํ™”๋˜๋Š” ๋ฐฉ์‹๊ณผ ๊ฐœ๋ฐœ ํ”„๋กœ์„ธ์Šค์˜ ๋ฐ ๋…ธ๋™๋ฌธํ™”์˜ ํŠน์ง•์— ๋Œ€ํ•ด ํƒ๊ตฌํ–ˆ๋‹ค. ์ด๋ฅผ ์œ„ํ•ด ๋Œ€๊ทœ๋ชจ ๊ฐœ๋ฐœํŒ€์˜ MMORPG ๊ฒŒ์ž„ ๊ฐœ๋ฐœ์‚ฌ๋ก€, ์ค‘์†Œ๊ฐœ๋ฐœ์‚ฌ์˜ ๋ชจ๋ฐ”์ผ RPG ๊ฒŒ์ž„ ๊ฐœ๋ฐœ์‚ฌ๋ก€, ๊ทธ๋ฆฌ๊ณ  ์†Œ๊ทœ๋ชจ ์Šคํƒ€ํŠธ์—… ๋ฐ 1์ธ ๊ฐœ๋ฐœ์ž๋“ค์˜ ์ž‘์—… ์‚ฌ๋ก€๋“ค์„ ์ค‘์‹ฌ์œผ๋กœ, ๊ฐ ํ”„๋กœ์ ํŠธ์˜ ์ธ๋ ฅ๊ตฌ์„ฑ ๋ฐ ์กฐ์งํ™”๋ฐฉ์‹, ๊ธฐ์ˆ ์  ์‹œ์Šคํ…œ ๋ฐ ๊ฐœ๋ฐœ ํ”„๋กœ์„ธ์Šค, ๊ทธ๋ฆฌ๊ณ  ๋…ธ๋™๋ฌธํ™”๋ฅผ ๋น„๊ต๋ถ„์„ํ–ˆ๋‹ค. ๊ทธ ๊ฒฐ๊ณผ, ๋Œ€๊ธฐ์—…์€ ๊ฐ ๋ถ„์•ผ์˜ ์ „๋ฌธ์„ฑ์„ ์‚ด๋ฆฌ๋ฉด์„œ๋„ ๊ฑฐ๋Œ€ํ•œ ๊ฐœ๋ฐœ์กฐ์ง์„ ๊ด€๋ฆฌํ•  ์ˆ˜ ์žˆ๋Š” ๊ณ ๋„๋กœ ์ฒด๊ณ„ํ™”๋œ ๋ถ„์—…ํ™” ๋ฐ ๊ด€๋ฆฌ ์‹œ์Šคํ…œ์„ ๊ฐ–์ถ”๊ณ  ์žˆ์—ˆ๋‹ค. ๋‹ค์–‘ํ•œ ์†Œํ”„ํŠธ์›จ์–ด ํˆด์„ ํ†ตํ•œ ์—…๋ฌด๊ด€๋ฆฌ์™€ ํ˜‘์—…์ด ์ด๋ฃจ์–ด์ง€๊ณ  ์žˆ์—ˆ๊ณ , ๊ฐœ๋ฐœ์ž๋“ค์€ ์„ธ๋ถ„ํ™”๋œ ์ „๋ฌธ์„ฑ์„ ์ถ•์ ํ•ด๋‚˜๊ฐ€๊ณ  ์žˆ์—ˆ๋‹ค. ๋ฐ˜๋ฉด, ์ค‘์†Œ๊ฐœ๋ฐœ์‚ฌ๋‚˜ 1์ธ ๊ฐœ๋ฐœ์ž๋กœ ๊ฐˆ์ˆ˜๋ก ๊ธฐ์ˆ ๋ ฅ์ด๋‚˜ ๊ธฐ๋ฐ˜ ์‹œ์Šคํ…œ์— ๋Œ€ํ•œ ์ฒด๊ณ„์ ์ธ ๊ด€๋ฆฌ๊ฐ€ ๋ฏธ์•ฝํ•˜๊ฑฐ๋‚˜ ๋ถ€์žฌํ•œ ์–‘์ƒ์„ ๋ณด์˜€๋‹ค. ๋˜ํ•œ ํ”„๋กœ์ ํŠธ์˜ ๋ฐฉํ–ฅ์„ฑ๋„ ์ž์ฃผ ๋ฐ”๋€Œ๋Š” ๋“ฑ ์ผ์ •๊ด€๋ฆฌ์™€ ์ธ๋ ฅ๊ด€๋ฆฌ ์ฐจ์›์—์„œ ๋ฆฌ์Šคํฌ๋ฅผ ํ›จ์”ฌ ๋” ๋งŽ์ด ๋– ์•ˆ๊ณ  ์žˆ์—ˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๊ฐœ๋ฐœ์ž๋“ค์˜ ์ž์œจ์ ์ด๊ณ  ์ฐธ์—ฌ์ ์ธ ๋ฌธํ™”๋ฅผ ๋น„๋กฏํ•ด ๊ฐœ์„ฑ๊ณผ ์ฐฝ์˜์„ฑ์˜ ๋ฐœํœ˜ ๊ธฐํšŒ๊ฐ€ ๋งŽ์ด ์ฃผ์–ด์ง„๋‹ค๋Š” ๊ฒƒ์ด ํŠน์ง•์ด์—ˆ๋‹ค. ๋˜ํ•œ ์‹ ์†ํ•˜๊ณ  ์œ ์—ฐํ•œ ๊ฐœ๋ฐœ ํ”„๋กœ์„ธ์Šค๋ฅผ ์ง„ํ–‰ํ•˜๋Š” ๊ณผ์ •์—์„œ ์‹œํ–‰์ฐฉ์˜ค๋ฅผ ๊ฒช์œผ๋ฉฐ, ๊ฒŒ์ž„ ์ƒ์‚ฐ์—์„œ ์œ ํ†ต์— ์ด๋ฅด๋Š” ์ „ ๊ณผ์ •์„ ๊ฒฝํ—˜ํ•จ์œผ๋กœ์จ ์ œ๋„ˆ๋Ÿด๋ฆฌ์ŠคํŠธ์  ์ „๋ฌธ์„ฑ๊ณผ ๋…ธํ•˜์šฐ๋ฅผ ํš๋“ํ•  ์ˆ˜ ์žˆ๋‹ค๋Š” ๊ฒƒ์ด ์ž‘์€ ๊ฐœ๋ฐœ์กฐ์ง์˜ ์žฅ์ ์ด์ž ๊ธฐํšŒ๋กœ ๋ถ„์„๋˜์—ˆ๋‹ค. ํ•œํŽธ, ์ฒด๊ณ„์ ์ธ ํ˜‘์—…๊ณผ ์ปค๋ฎค๋‹ˆ์ผ€์ด์…˜์„ ์œ„ํ•œ ํƒœ๋„์™€ ์Šคํ‚ฌ์€ ๋ชจ๋“  ๋ถ„์•ผ์—์„œ ๊ณตํ†ต์ ์œผ๋กœ ๊ฐ•์กฐ๋˜๊ณ  ์žˆ์—ˆ๋‹ค. ๋‘˜์งธ, 2010๋…„๋Œ€ ์ดํ›„ ํ•œ๊ตญ ๊ฒŒ์ž„ ์‚ฐ์—…์˜ ๊ตฌ์กฐ์  ๋ณ€ํ™”๋ฅผ ๊ฒ€ํ† ํ•˜๋ฉด์„œ, ๋ถˆ์•ˆ์ •์„ฑ์ด ์‹ฌํ™”๋˜๋Š” ๋ฐฐ๊ฒฝ์— ๋Œ€ํ•ด ์‚ดํŽด๋ณด์•˜๋‹ค. ๋ชจ๋ฐ”์ผ ์˜คํ”ˆ ๋งˆ์ผ“์ด ๋„์ž…๋˜๊ณ , ์…ง๋‹ค์šด์ œ๊ฐ€ ๋ณธ๊ฒฉํ™”๋˜๋Š” ๋“ฑ์˜ ์‹œ์žฅ ๋ฐ ์ œ๋„์  ์š”์ธ ์†์—์„œ ํ•œ๊ตญ ๊ฒŒ์ž„ ์‚ฐ์—…์˜ ์ฃผ๋ฅ˜๋Š” PC ์˜จ๋ผ์ธ์—์„œ ๋ชจ๋ฐ”์ผ ํ”Œ๋žซํผ์œผ๋กœ ๊ธ‰๊ฒฉํ•˜๊ฒŒ ์ด๋™ํ–ˆ๋‹ค. ์ด ๊ณผ์ •์—์„œ ๋Œ€๊ธฐ์—… ๋ฐ ํผ๋ธ”๋ฆฌ์…” ์ค‘์‹ฌ์˜ ๋…๊ณผ์ ์  ์ง€๋ฐฐ๊ตฌ์กฐ๊ฐ€ ๋งŒ๋“ค์–ด์กŒ๊ณ , ํ™•๋ฅ ํ˜• ์•„์ดํ…œ ์ค‘์‹ฌ์˜ ๋น„์ฆˆ๋‹ˆ์Šค ๋ชจ๋ธ์ด ์œ ๋ช… IP๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•œ MMORPG ๊ฒŒ์ž„๊ณผ ๊ฒฐํ•ฉํ•จ์œผ๋กœ์จ, ์ˆ˜์ต์„ฑ์ด ๊ฒ€์ฆ๋œ ์œ ์‚ฌํ•œ ๊ฒŒ์ž„๋“ค์ด ๋Œ€๋Ÿ‰์ƒ์‚ฐ๋˜๋Š” ํ‘œ์ค€์  ๊ด€ํ–‰์ด ํ˜•์„ฑ๋˜์—ˆ๋‹ค. ์‚ฐ์—… ๋‚ด ๊ฒฝ์Ÿ์€ ์น˜์—ดํ•ด์กŒ๊ณ , ๊ฒŒ์ž„ ๊ฐœ๋ฐœ๊ธฐ๊ฐ„๊ณผ ๊ฒŒ์ž„์˜ ์ƒ์• ์ฃผ๊ธฐ๋Š” ์ ์ฐจ ์งง์•„์กŒ๋‹ค. ์ด๊ฒƒ์€ ๋งˆ์นจ๋‚ด ํฌ๋Ÿฐ์น˜ ๋ชจ๋“œ(crunch mode)๋กœ ๋ถˆ๋ฆฌ๋Š” ๋…ธ๋™์ฐฉ์ทจ ๊ด€ํ–‰์„ ๋”์šฑ ์•…ํ™”์‹œ์ผฐ์œผ๋ฉฐ, ๊ฐœ๋ฐœ์ž๋“ค์€ ์žฅ์‹œ๊ฐ„ ๋…ธ๋™, ์ €์ž„๊ธˆ, ์ž์œจ์„ฑ๊ณผ ์ฐฝ์˜์„ฑ์ด ์ œ์•ฝ๋˜๋Š” ๊ตฌ์กฐ ์†์—์„œ ํš์ผ์ ์œผ๋กœ ์ฃผ๋ฌธ์— ๋งž์ถฐ ์ฐ์–ด๋‚ด๋Š” ๊ณต์žฅ ๋…ธ๋™์ž์™€ ๊ฐ™์€ ์ผ์„ ํ•˜๊ฒŒ ๋˜์—ˆ๋‹ค. ์ด๋Š” ์ƒˆ๋กœ์šด ํ˜•์‹์˜ ๋…ธ๋™์†Œ์™ธ๋ฅผ ๋ฐœ์ƒ์‹œ์ผฐ๊ณ , ๊ทธ ์†์—์„œ ๊ฐœ๋ฐœ์ž๋“ค์€ ๊ทธ๋“ค์ด ๋ณธ๋ž˜ ๊ฐ๋‚ดํ–ˆ์–ด์•ผ ํ–ˆ๋˜ ๊ณ ์šฉ ๋ฐ ์ปค๋ฆฌ์–ด์˜ ๋ถˆ์•ˆ์ •์„ฑ, ๊ฒฝ์ œ์  ๋ถˆ์•ˆ์ •์„ฑ์— ๋”ํ•˜์—ฌ ์ž์œจ์„ฑ๊ณผ ์ฐฝ์˜์„ฑ์˜ ์ œ์•ฝ, ์ „๋ฌธ์„ฑ์˜ ๊ตด์ ˆ ๋ฐ ํƒˆ์ˆ™๋ จํ™” ๋“ฑ์œผ๋กœ ์ธํ•ด ๋ณด๋‹ค ์‹ฌํ™”๋œ ๋ถˆ์•ˆ์ •์„ฑ์— ๋†“์ด๊ฒŒ ๋˜์—ˆ๋‹ค. ๊ทธ๋“ค์ด ๊ฒฝํ—˜ํ•˜๋Š” ๋ถˆ์•ˆ์ •์„ฑ์€ ํŠนํžˆ ๊ฐœ๋ฐœ์ž๋“ค์ด ์†ํ•œ ํšŒ์‚ฌ๋‚˜ ์กฐ์ง์˜ ๊ทœ๋ชจ, ์ง๊ตฐ, ์„ธ๋Œ€์™€ ์  ๋” ๋“ฑ์— ๋”ฐ๋ผ ๊ทธ ์ •๋„์™€ ์–‘์ƒ์—์„œ ์ฐจ์ด๋ฅผ ๋ณด์˜€๋‹ค. ์ผ๋ฐ˜์ ์œผ๋กœ ํ”„๋กœ๊ทธ๋ž˜๋จธ๋ณด๋‹ค ์•„ํ‹ฐ์ŠคํŠธ๋‚˜ ๊ธฐํš์ž๋“ค์˜ ๋ถˆ์•ˆ์ •์„ฑ์ด ๋” ์ปธ๊ณ , QA, ์šด์˜, CS ์ข…์‚ฌ์ž๋“ค์˜ ๋ถˆ์•ˆ์ •์„ฑ์ด ๊ฐ€์žฅ ์‹ฌ๊ฐํ–ˆ๋‹ค. ๋˜ํ•œ ๊ธฐ์„ฑ์„ธ๋Œ€ ๊ฐœ๋ฐœ์ž๋“ค๋ณด๋‹ค ์ฒญ๋…„ ๊ฐœ๋ฐœ์ž๋“ค์˜ ๋ถˆ์•ˆ์ •์„ฑ์ด ๋” ์ปธ์œผ๋ฉฐ, ์—ฌ์„ฑ ๊ฐœ๋ฐœ์ž๋“ค์ด ๋‚จ์„ฑ๋“ค๋ณด๋‹ค ๋” ๋ถˆ์•ˆ์ •ํ•˜๊ณ  ์ทจ์•ฝํ•œ ์ƒํƒœ์— ์ฒ˜ํ•ด ์žˆ์—ˆ๋‹ค. ์…‹์งธ, ๊ทธ๋ ‡๋‹ค๋ฉด ๊ฒŒ์ž„ ์‚ฐ์—…์—์„œ์˜ ๋…ธ๋™ ๋ถˆ์•ˆ์ •์„ฑ์„ ์–ด๋–ป๊ฒŒ ๊ทน๋ณตํ•  ๊ฒƒ์ธ๊ฐ€? ์ด ์—ฐ๊ตฌ๋Š” ์˜ค๋Š˜๋‚  ๊ฒŒ์ž„ ๊ฐœ๋ฐœ์ž๋“ค์ด, ๊ทธ๋“ค์ด ์ฒ˜ํ•œ ๊ตฌ์กฐ์  ํ•œ๊ณ„ ์†์—์„œ๋„ ๋…ธ๋™์˜ ์ž์œจ์„ฑ์„ ํšŒ๋ณตํ•˜๊ณ  ์ „๋ฌธ์„ฑ์„ ์ถ•์ ํ•ด๋‚˜๊ฐ€๋ฉฐ ๊ทธ๋“ค๋งŒ์˜ ์ฐฝ์˜์„ฑ์„ ๋ฐœํœ˜ํ•  ์ˆ˜ ์žˆ๋Š” ์—ญ๋Ÿ‰์„ ๊ฐ–์ถ”๊ณ  ์žˆ๋‹ค๋Š” ์ ์— ์ฃผ๋ชฉํ•œ๋‹ค. ๊ทธ๋“ค์€ ๊ณ ๋ฆฝ๋˜๊ณ  ํŒŒํŽธํ™”๋œ ๊ฐœ์ธ์˜ ๋…ธ๋™์— ๋จธ๋ฌผ์ง€ ์•Š๊ณ , ์—ฌ๋Ÿฌ ์†Œํ”„ํŠธ์›จ์–ด ํˆด๊ณผ ํ”Œ๋žซํผ์„ ํ™œ์šฉํ•˜์—ฌ ์ž์œจ์ ์ด๊ณ  ์œตํ•ฉ์ ์ธ ์ž‘์—…(converged work)์„ ์ฃผ๋„์ ์œผ๋กœ ์ˆ˜ํ–‰ํ•  ์ˆ˜ ์žˆ๋‹ค. ๋˜ํ•œ, ๊ฒŒ์ž„ ํ•˜์œ„๋ฌธํ™”(game subculture)์˜ ๊ณต๋™์ฒด๋ฅผ ํ•จ๊ป˜ ๊ตฌ์„ฑํ•˜๊ณ  ์žˆ๋Š” ๊ทธ๋“ค์˜ ๋™๋ฃŒ ๋ฐ ๊ฒŒ์ž„ ์œ ์ €๋“ค๊ณผ์˜ ํ˜‘์—…์„ ํ†ตํ•ด์„œ ๋…ธ๋™์˜ ๋ถˆ์•ˆ์ •์„ฑ์„ ๊ทน๋ณตํ•  ์ˆ˜ ์žˆ๋Š” ๊ธฐํšŒ๋ฅผ ๋งˆ๋ จํ•  ์ˆ˜ ์žˆ๋‹ค. ์ด๊ฒƒ์€ ๊ฒŒ์ž„์˜ ๋ณธ์งˆ์  ์žฌ๋ฏธ์™€ ๊ฐ€์น˜๋ฅผ ์ถ”๊ตฌํ•˜๋Š” ์‚ฌ๋žŒ๋“ค ์‚ฌ์ด์˜ ์ž๋ฐœ์  ํ˜‘๋ ฅ์„ ํ†ตํ•ด ์‚ฌํšŒ์  ์—ฐ๋Œ€์™€ ๊ณต๋™์ฒด์  ๋Œ€์‘์„ ๊ฐ€๋Šฅ์ผ€ ํ•˜๋Š” ๊ฒƒ์œผ๋กœ์„œ, ๋„คํŠธ์›Œํฌํ™”๋œ ์ฐฝ์˜์  ํ˜‘์—…(networked creative collaboration) ๊ฐœ๋…์œผ๋กœ ํŒŒ์•…๋  ์ˆ˜ ์žˆ๋‹ค. ์ด๋Ÿฌํ•œ ๊ณผ์ •์„ ํ†ตํ•ด ๊ทธ๋“ค์€ ํ˜‘์—…๊ณผ ์—ฐ๋Œ€์˜ ๋„คํŠธ์›Œํฌ๋ฅผ ๊ฐ•ํ™”ํ•  ์ˆ˜ ์žˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ด๋ฅผ ์œ„ํ•ด์„œ๋Š” ๊ฒŒ์ž„ ๊ฐœ๋ฐœ์ž๋“ค์˜ ๋…ธ๋ ฅ๋งŒ์œผ๋กœ๋Š” ๋ถ€์กฑํ•˜๋ฉฐ, ๋ฒ•์ ยท์ œ๋„์ ยท์ •์ฑ…์  ์ฐจ์›์˜ ๋Œ€์‘๋ฐฉ์•ˆ์ด ๋’ท๋ฐ›์นจ๋˜์–ด์•ผ ํ•œ๋‹ค. ์˜ˆ์ปจ๋Œ€, ๊ทผ๋กœ์‹œ๊ฐ„ ํ•ฉ๋ฆฌํ™”, ํฌ๊ด„์ž„๊ธˆ์ œ ๊ฐœ์„ , ํ‘œ์ค€๊ณ„์•ฝ ๊ฐ€์ด๋“œ๋ผ์ธ ๋งˆ๋ จ ๋ฐ ์‚ฌํšŒ๋ณดํ—˜ ํ™•๋Œ€ ๋“ฑ์„ ํ†ตํ•ด ๋…ธ๋™์ž๋กœ์„œ์˜ ๊ถŒ์ต์„ ๋ณดํ˜ธํ•˜๋Š” ๋ฒ•์ ยท์ œ๋„์  ๊ฐœ์„ ์ด ์š”๊ตฌ๋œ๋‹ค. ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ๊ฒŒ์ž„ ๊ฐœ๋ฐœ์ž๋“ค์˜ ์ฐฝ์˜์  ์—ญ๋Ÿ‰์„ ๊ฐ•ํ™”ํ•˜๊ณ  ๊ทธ๋“ค์ด ์ž์œจ์ ์œผ๋กœ ์ผํ•  ์ˆ˜ ์žˆ๋„๋ก ๋•๋Š” ์ง€์›์ •์ฑ…๋“ค์ด ํ•„์š”ํ•˜๋‹ค. ์˜ˆ๋ฅผ ๋“ค์–ด ์ง์—…๊ต์œก, ๋ฉ˜ํ† ๋ง, ์ž‘์—…๊ณต๊ฐ„ ๋ฐ ๋„คํŠธ์›Œํ‚น ๊ธฐํšŒ ์ œ๊ณต, ์œ ํ†ต ๋ฐ ํผ๋ธ”๋ฆฌ์‹ฑ ์ง€์› ์‚ฌ์—… ๋“ฑ์ด ๋ณด๋‹ค ๊ฐ•ํ™”๋  ํ•„์š”๊ฐ€ ์žˆ๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ ํ˜„์žฌ ๊ฒŒ์ž„ ์‚ฐ์—… ๊ตฌ์กฐ์— ๋Œ€ํ•œ ๊ฒŒ์ž„ ์—…๊ณ„ ๋‚ด๋ถ€์˜ ์ž์ƒ์  ์„ฑ์ฐฐ๊ณผ ํ˜์‹ ์ด ์š”๊ตฌ๋œ๋‹ค. ๊ทธ๊ฒƒ์€ ๊ฐœ๋ฐœ์ž๋“ค์ด ์ฒ˜ํ•œ ๋ถˆ์•ˆ์ •์„ฑ์˜ ์‹ฌํ™”๋ผ๋Š” ๋ฌธ์ œ๋ฅผ ๊ทผ๋ณธ์ ์œผ๋กœ ํ•ด๊ฒฐํ•˜๊ณ ์ž ํ•˜๋Š” ์˜์ง€๋ฅผ ํ•„์š”๋กœ ํ•œ๋‹ค. ์ด ๋ชจ๋“  ๋Œ€์‘ ๋ฐฉ๋ฒ•๋“ค์€ ๊ถ๊ทน์ ์œผ๋กœ ๊ฒŒ์ž„ ๊ฐœ๋ฐœ์ž๋“ค์˜ ๋…ธ๋™์กฐํ•ฉ ์„ค๋ฆฝ๊ณผ ๊ฐ™์€ ์กฐ์งํ™” ๋ฐ ๊ต์„ญ๋ ฅ ๊ฐ•ํ™”๋ฅผ ๋„๋ชจํ•˜๋Š” ๊ฒƒ์ด๋ฉฐ, ๊ทธ๋“ค์˜ ์ž์œจ์„ฑ๊ณผ ๋…๋ฆฝ์„ฑ, ํ˜‘์—…์„ ์œ„ํ•œ ๋„คํŠธ์›Œํฌ๋ฅผ ํ™•์žฅํ•˜๋Š” ๊ฒƒ์œผ๋กœ ์ด์–ด์งˆ ์ˆ˜ ์žˆ๋‹ค. ์ด๊ฒƒ์€ ๊ฒŒ์ž„ ๊ฐœ๋ฐœ์ž๋“ค์ด ์ง‘ํ•ฉ์ (collective), ํ˜‘์—…์ (collaborative), ๊ณต๋™์ฒด์ (communal) ์ฐฝ์˜์„ฑ์„ ํ˜•์„ฑํ•˜๊ณ , ๋ถˆ์•ˆ์ •์„ฑ์— ๋Œ€ํ•˜์—ฌ ์‚ฌํšŒ์  ์ฐจ์›์—์„œ ๋Œ€์‘ํ•˜๋ฉฐ ๋ณด๋‹ค ์•ˆ์ •์ ์œผ๋กœ ๊ฒŒ์ž„์„ ๋งŒ๋“ค์–ด๋‚ผ ์ˆ˜ ์žˆ๋Š” ํ† ๋Œ€๋ฅผ ํ˜•์„ฑํ•˜๋„๋ก ํ•ด ์ค„ ๊ฒƒ์ด๋‹ค. ์ด๋Š” ๊ถ๊ทน์ ์œผ๋กœ ์ง€์†๊ฐ€๋Šฅํ•œ ๊ฒŒ์ž„ ์ƒํƒœ๊ณ„์˜ ํ˜•์„ฑ์„ ๋ชฉํ‘œ๋กœ ํ•œ๋‹ค. ์ด ์—ฐ๊ตฌ๋Š” ์˜ค๋Š˜๋‚  ์‚ฌํšŒ๋ฌธํ™”์ ์œผ๋กœ ์ค‘์š”ํ•œ ์—ฐ๊ตฌ ์ฃผ์ œ๊ฐ€ ๋˜๊ณ  ์žˆ๋Š”, ์ฐฝ์˜ ์‚ฐ์—…์˜ ๋…ธ๋™์ด ๊ฐ€์ง„ ํ•œ๊ณ„์™€ ๊ฐ€๋Šฅ์„ฑ์„ ๊ฒŒ์ž„ ์‚ฐ์—…์ด๋ผ๋Š” ํ•˜๋‚˜์˜ ์ „ํ˜•์ ์ธ ์‚ฌ๋ก€ ์†์—์„œ ์‹ฌ์ธต์ ์œผ๋กœ ๊ทœ๋ช…ํ•ด๋ณด๋ ค๋Š” ์‹œ๋„๋ผ๋Š” ์ ์—์„œ ํฐ ์˜๋ฏธ๋ฅผ ๊ฐ–๋Š”๋‹ค.์ œ1์žฅ. ์„œ๋ก  1 1์ ˆ. ๋ฌธ์ œ์ œ๊ธฐ 1 2์ ˆ. ์—ฐ๊ตฌ์˜ ์‹œ๊ฐ ๋ฐ ๋…ผ๋ฌธ ๊ตฌ์„ฑ 4 ์ œ2์žฅ. ์„ ํ–‰์—ฐ๊ตฌ ๊ฒ€ํ†  ๋ฐ ์—ฐ๊ตฌ๋ฌธ์ œ 8 1์ ˆ. ์ฐฝ์˜ ์‚ฐ์—…๊ณผ ๋…ธ๋™์˜ ๋ณ€ํ™” : ์ฐฝ์˜์„ฑ, ์œ ์—ฐ์„ฑ ๊ทธ๋ฆฌ๊ณ  ๋ถˆ์•ˆ์ •์„ฑ 8 1. ์ฐฝ์˜ ์‚ฐ์—…์˜ ์ •์น˜๊ฒฝ์ œ์  ๋ฐฐ๊ฒฝ๊ณผ ํŠน์„ฑ 8 2. ์ฐฝ์˜ ์‚ฐ์—… ๋…ธ๋™์˜ ํŠน์ง•๊ณผ ์ฃผ์š” ์Ÿ์ ๋“ค 10 2์ ˆ. ๊ฒŒ์ž„ ์‚ฐ์—…์˜ ๋…ธ๋™๊ณผ ๊ฒŒ์ž„ ๊ฐœ๋ฐœ์ž๋“ค 18 1. ๊ฒŒ์ž„ ์‚ฐ์—… ๋…ธ๋™๊ณผ ๊ฐœ๋ฐœ์ž์— ๊ด€ํ•œ ๊ธฐ์กด ์—ฐ๊ตฌ๋“ค 18 2. ๊ฒŒ์ž„ ์‚ฐ์—… ๋…ธ๋™์˜ ํŠน์„ฑ๊ณผ ๊ธฐ์กด ์—ฐ๊ตฌ๊ฒฝํ–ฅ์— ๋Œ€ํ•œ ์ •๋ฆฌ 27 3์ ˆ. ์—ฐ๊ตฌ๋ฌธ์ œ 31 ์ œ3์žฅ. ์ด๋ก ์  ๋…ผ์˜ 34 1์ ˆ. ๋น„ํŒ์  ๋…ธ๋™์‚ฌํšŒํ•™์œผ๋กœ ๋ณธ ๋…ธ๋™ 34 1. ๋ถ„์—…ํ™”, ํƒˆ์ˆ™๋ จ, ๊ทธ๋ฆฌ๊ณ  ๋…ธ๋™์†Œ์™ธ 34 2. ๋…ธ๋™ ์œ ์—ฐํ™”์™€ ํฌ์ŠคํŠธํฌ๋””์ฆ˜ ์‹œ๋Œ€์˜ ๋…ธ๋™์œค๋ฆฌ 38 3. ์‹ ์ž์œ ์ฃผ์˜ ํ†ต์น˜์„ฑ์— ๋Œ€ํ•œ ๋น„ํŒ์  ๋…ผ์˜ 42 2์ ˆ. ๊ฒŒ์ž„ ์‚ฐ์—… ๋…ธ๋™ ์ฃผ์ฒด์˜ ๋ถˆ์•ˆ์ •์„ฑ(precarity) 46 1. ๋ถˆ์•ˆ์ •์„ฑ(precarity)์— ๊ด€ํ•œ ์ด๋ก ์  ๋…ผ์˜๋“ค 46 2. ๊ฒŒ์ž„ ์‚ฐ์—… ๋…ธ๋™์—์„œ์˜ ๋ถˆ์•ˆ์ •์„ฑ ๋ฌธ์ œ 50 3. ๋ถˆ์•ˆ์ •์„ฑ์˜ ๊ทน๋ณต ๊ฐ€๋Šฅ์„ฑ์— ๊ด€ํ•œ ๋…ผ์˜๋“ค 54 3์ ˆ. ์ฐฝ์˜ ์‚ฐ์—… ๋…ธ๋™์— ๋Œ€ํ•œ ์ด๋ก ์  ํ๋ฆ„ 60 1. ์ฐฝ์˜ ๋…ธ๋™ 60 2. ๋น„ํŒ์  ์ •์น˜๊ฒฝ์ œํ•™์—์„œ ๋ณธ ๋””์ง€ํ„ธ ์‹œ๋Œ€์˜ ๋…ธ๋™ 63 3. ์ง‘๋‹จ ์ฐฝ์˜์„ฑ์— ๋Œ€ํ•œ ์ด๋ก ์  ๋ชจ์ƒ‰ 70 4์ ˆ. ๋””์ง€ํ„ธ ์ฐฝ์˜ ์ž‘์—…(digital creative work) 75 1. ๋””์ง€ํ„ธ ์ฐฝ์˜ ์ž‘์—…์˜ ๊ฐœ๋…ํ™” 75 2. ๋””์ง€ํ„ธ ์ฐฝ์˜ ์ž‘์—…์˜ ๊ตฌ์„ฑ์š”์†Œ๋“ค 77 3. ๊ฒŒ์ž„ ์‚ฐ์—… ๋…ธ๋™์—์„œ์˜ ๋””์ง€ํ„ธ ์ฐฝ์˜ ์ž‘์—… 85 ์ œ4์žฅ. ์—ฐ๊ตฌ๋Œ€์ƒ ๋ฐ ์—ฐ๊ตฌ๋ฐฉ๋ฒ• 89 1์ ˆ. ์—ฐ๊ตฌ๋Œ€์ƒ : ํ•œ๊ตญ์˜ ๊ฒŒ์ž„ ๊ฐœ๋ฐœ์ž ๋ฐ ๊ฒŒ์ž„ ์ƒ์‚ฐ์˜ ํ˜„์žฅ 89 2์ ˆ. ์—ฐ๊ตฌ๋ฐฉ๋ฒ•๋ก ๊ณผ ์—ฐ๊ตฌ์˜ ๊ณผ์ • 93 1. ์‹ฌ์ธต ์ธํ„ฐ๋ทฐ 93 2. ์ฐธ์—ฌ๊ด€์ฐฐ 97 3. ๋ฌธํ—Œ์ž๋ฃŒ ๋ฐ ๋‹ด๋ก ๋ถ„์„ 99 3์ ˆ. ์ž๋ฃŒ์˜ ๋ถ„์„๊ณผ ํ•ด์„์  ํ‹€ 100 ์ œ5์žฅ. ๊ฒŒ์ž„์˜ ์ƒ์‚ฐ๊ณผ์ •๊ณผ ๋…ธ๋™์˜ ์กฐ์งํ™” 103 1์ ˆ. ๊ฒŒ์ž„์€ ๋ˆ„๊ฐ€ ์–ด๋–ป๊ฒŒ ๋งŒ๋“ค์–ด๋‚ด๋Š”๊ฐ€? 103 1. ๊ฒŒ์ž„ ์‚ฐ์—…์˜ ์ฃผ์š” ์ง๊ตฐ๋“ค 103 2. ๊ฒŒ์ž„ ๊ฐœ๋ฐœ์˜ ์ผ๋ฐ˜์  ๊ณผ์ • 106 3. ๊ฒŒ์ž„ ๊ฐœ๋ฐœ์˜ ๊ธฐ์ˆ ์  ๋ฐฐ๊ฒฝ 109 2์ ˆ. ๊ฒŒ์ž„ ๊ฐœ๋ฐœ ์‚ฌ๋ก€๋ถ„์„ : ๊ฒŒ์ž„ ๊ทœ๋ชจ์— ๋”ฐ๋ฅธ ์ธ๋ ฅ๊ตฌ์„ฑ, ๊ฐœ๋ฐœ ํ”„๋กœ์„ธ์Šค, ๋…ธ๋™๋ฌธํ™”์˜ ์–‘์ƒ 112 1. N์‚ฌ ๋Œ€ํ˜• ์ฝ˜์†”๊ฒŒ์ž„ ๊ฐœ๋ฐœ ์‚ฌ๋ก€ 113 2. ์ค‘์†Œ๊ฐœ๋ฐœ์‚ฌ ๋ชจ๋ฐ”์ผ ๊ฒŒ์ž„ ๊ฐœ๋ฐœ์‚ฌ๋ก€ 127 3. ์Šคํƒ€ํŠธ์—…, 1์ธ ๊ฐœ๋ฐœ์‚ฌ์˜ ๋ชจ๋ฐ”์ผ ๊ฒŒ์ž„๊ฐœ๋ฐœ ์‚ฌ๋ก€๋“ค 136 3์ ˆ. ๊ฒŒ์ž„ ์ƒ์‚ฐ๊ณผ์ •์˜ ๋ฆฌ์Šคํฌ๋“ค๊ณผ ๋…ธ๋™ ๋ฌธํ™”๋ฅผ ๋‘˜๋Ÿฌ์‹ผ ์Ÿ์ ๋“ค 147 1. ๊ฒŒ์ž„ ์ƒ์‚ฐ๊ณผ์ •์—์„œ ๋ฐœ์ƒํ•˜๋Š” ๋‹ค์–‘ํ•œ ๋ฆฌ์Šคํฌ 147 2. ๋…ธ๋™๋ฌธํ™”๋ฅผ ๋‘˜๋Ÿฌ์‹ผ ์Ÿ์ ๋“ค 153 4์ ˆ. ์†Œ๊ฒฐ : ๊ฒŒ์ž„ ์ƒ์‚ฐ๊ณผ์ •์˜ ํ•œ๊ณ„์™€ ๊ฐ€๋Šฅ์„ฑ๋“ค 159 1. ๊ฒŒ์ž„ ์ƒ์‚ฐ ํ”„๋กœ์„ธ์Šค์˜ ์ฃผ์š” ํŠน์ง•๊ณผ ํ•จ์˜ 159 2. ์ธ๋ ฅ๊ตฌ์„ฑ, ๊ฐœ๋ฐœ ํ”„๋กœ์„ธ์Šค, ๋…ธ๋™๋ฌธํ™”์˜ ํŠน์ง• 160 3. ๊ฒŒ์ž„ ์‚ฐ์—…์˜ ๋ฆฌ์Šคํฌ์™€ ๊ธฐํšŒ๋“ค 162 ์ œ6์žฅ. ๊ฒŒ์ž„ ์ƒ์‚ฐ ํ™˜๊ฒฝ์˜ ๋ณ€ํ™”์™€ ๊ฐœ๋ฐœ์ž๋“ค์˜ ๋ถˆ์•ˆ์ •์„ฑ ์‹ฌํ™” 165 1์ ˆ. ํ•œ๊ตญ์˜ ๊ฒŒ์ž„ ์‚ฐ์—…๊ณผ ๊ฐœ๋ฐœ์ž๋“ค์˜ ํ˜„์‹ค 165 1. ํ•œ๊ตญ ๊ฒŒ์ž„์˜ ๊ณผ๊ฑฐ์™€ ์˜ค๋Š˜ 165 2. ํ•œ๊ตญ ๊ฒŒ์ž„ ์‚ฐ์—…์˜ ํ˜„ํ™ฉ 166 3. ํ•œ๊ตญ ๊ฒŒ์ž„ ๊ฐœ๋ฐœ์ž๋“ค์˜ ํ˜„์‹ค 170 2์ ˆ. ๋ชจ๋ฐ”์ผ ๊ฒŒ์ž„ ์‹œ๋Œ€, ํ•œ๊ตญ ๊ฒŒ์ž„ ์‚ฐ์—…์˜ ๊ตฌ์กฐ์  ๋ฌธ์ œ 173 1. ํ•œ๊ตญ ๊ฒŒ์ž„ ์‚ฐ์—…์˜ ๋ชจ๋ฐ”์ผ ์ง‘์ค‘ํ™”๋ฅผ ๋‘˜๋Ÿฌ์‹ผ ๋ฌธ์ œ๋“ค 173 2. ๋Œ€๊ธฐ์—…ยทํผ๋ธ”๋ฆฌ์…” ์ค‘์‹ฌ์˜ ๋…๊ณผ์ ์  ์ง€๋ฐฐ๊ตฌ์กฐ 180 3. ํŠน์ • ์ˆ˜์ต๋ชจ๋ธ์„ ์ค‘์‹ฌ์œผ๋กœ ํš์ผํ™”๋˜๋Š” ๊ฒŒ์ž„๋“ค 185 4. ๋ชจ๋ฐ”์ผ ๊ฒŒ์ž„ ์‹œ๋Œ€์˜ ๊ฐœ๋ฐœํ™˜๊ฒฝ๊ณผ ๋…ธ๋™์˜ ๋ณ€ํ™” 192 3์ ˆ. ๋” ๋ถˆ์•ˆ์ •ํ•ด์ง„ ๊ฒŒ์ž„ ๊ฐœ๋ฐœ์ž์˜ ์ปค๋ฆฌ์–ด : ๊ณ ์šฉ ๋ถˆ์•ˆ์ •๊ณผ ๊ฒฝ์ œ์  ์ทจ์•ฝ์„ฑ 195 1. ๊ณ ์šฉ ๋ฐ ์ปค๋ฆฌ์–ด ์ฐจ์›์˜ ๋ถˆ์•ˆ์ •์„ฑ 195 2. ๊ฒฝ์ œ์  ์ฐจ์›์˜ ๋ถˆ์•ˆ์ •์„ฑ 203 4์ ˆ. ๊ณ ๋„ํ™”๋œ ์ฐฉ์ทจ์™€ ๋…ธ๋™ ์†Œ์™ธ : ๊ฐˆ์•„ ๋„ฃ์–ด์ง€๋Š” ๊ฐœ๋ฐœ์ž๋“ค์˜ ํŒŒํŽธํ™”๋œ ๋…ธ๋™๊ณผ์ • 208 1. ๊ณ ๋„ํ™”๋œ ์ฐฉ์ทจ๋กœ ์ธํ•œ ์‚ถ์˜ ์งˆ ํ•˜๋ฝ : ํฌ๋Ÿฐ์น˜(crunch)์— ์‹ ์Œํ•˜๋Š” ๊ฐœ๋ฐœ์ž๋“ค 208 2. ๋ถ€ํ’ˆํ™”๋œ ๋…ธ๋™์ž์™€ ํŒŒํŽธํ™”๋œ ๊ฐœ๋ฐœ ์ž‘์—… : ๊ฐœ๋ฐœ์ž๋“ค์˜ ๋…ธ๋™์†Œ์™ธ ์–‘์ƒ 213 5์ ˆ. ๋ถˆ์•ˆ์ •์„ฑ์˜ ๋‹ค์–‘ํ•œ ์ธต์œ„์™€ ์ฐจ์ด์˜ ์–‘์ƒ๋“ค 224 1. ๋ถˆ์•ˆ์ •์„ฑ์˜ ์‹ฌ์—ฐ : ์ฒด๋…๊ณผ ์‹ค๋ง 224 2. ๋ถˆ์•ˆ์ •์„ฑ์˜ ๋‹ค์–‘ํ•œ ์ธต์œ„์™€ ์ฐจ์ด์˜ ์–‘์ƒ 227 6์ ˆ. ์†Œ๊ฒฐ : ๊ฐ์–‘๊ฐ์ƒ‰์œผ๋กœ ์‹ฌํ™”๋˜๋Š” ๊ฒŒ์ž„ ๊ฐœ๋ฐœ์ž๋“ค์˜ ๋ถˆ์•ˆ์ •์„ฑ 237 ์ œ7์žฅ. ๋ถˆ์•ˆ์ •์„ฑ์— ์–ด๋–ป๊ฒŒ ๋Œ€์‘ํ•  ๊ฒƒ์ธ๊ฐ€? 241 1์ ˆ. ๊ฒŒ์ž„ ์‚ฐ์—…์„ ๋‘˜๋Ÿฌ์‹ผ ๊ธฐ์ˆ , ์†Œํ”„ํŠธ์›จ์–ด, ํ”Œ๋žซํผ์˜ ์ง„ํ™” 241 1. ์ƒˆ๋กœ์šด ๊ธฐ์ˆ  ๋„์ž…๊ณผ ํƒˆ์ˆ™๋ จํ™” ๋…ผ์˜์— ๋Œ€ํ•œ ์žฌ๊ฒ€ํ†  241 2. ์ƒ์šฉ์—”์ง„์˜ ๋ณด๊ธ‰๊ณผ ๊ฒŒ์ž„ ๊ฐœ๋ฐœ๊ณผ์ •์˜ ๋ณ€ํ™” 243 3. ๊ฒŒ์ž„ ์ƒ์‚ฐ์„ ์œ„ํ•œ ์†Œํ”„ํŠธ์›จ์–ด์™€ ํ”Œ๋žซํผ์˜ ์ง„ํ™” 249 4. ์ƒ์‚ฐ๊ณผ์ •์—์„œ ์ด๋ฃจ์–ด์ง€๋Š” ์ปจ๋ฒ„์ „์Šค 253 2์ ˆ. ๋””์ง€ํ„ธ ์ฐฝ์˜ ์ž‘์—…(digital creative work)์œผ๋กœ์„œ์˜ ๊ฒŒ์ž„ ์ƒ์‚ฐ ๊ฐ€๋Šฅ์„ฑ : ๋ถ„์ ˆํ™”๋œ ๊ฐœ๋ฐœ ๋…ธ๋™์—์„œ ์œตํ•ฉ์  ์ƒ์‚ฐ ์ž‘์—…์œผ๋กœ 255 1. ๋””์ง€ํ„ธ ์ฐฝ์˜ ์ž‘์—…์˜ ์–‘์ƒ๊ณผ ๊ฐ€๋Šฅ์„ฑ 255 2. ๋‹ค์ค‘์ˆ™๋ จ : ์œตํ•ฉ์  ์ž‘์—…๊ณผ์ •์˜ ์ถ•์ ๊ณผ ํ™•์‚ฐ 260 3. ๊ฐœ์ธํ™”๋œ ๋””์ง€ํ„ธ ์ฐฝ์˜ ์ž‘์—…์˜ ์–‘๋ฉด์„ฑ๊ณผ ํ•œ๊ณ„ : ์ œ๋„ˆ๋Ÿด๋ฆฌ์ŠคํŠธ, ์ง„์ •์„ฑ, ๊ทธ๋ฆฌ๊ณ  ์ž๊ธฐ์ฐฉ์ทจ์˜ ๋ฌธ์ œ 269 3์ ˆ. ๊ฒŒ์ž„ ๊ฐœ๋ฐœ์ž๋“ค์˜ ์ƒ์กด๊ณผ ์—ฐ๋Œ€์˜ ์ „๋žต๋“ค 274 1. ๊ฐœ๋ฐœ์ž๋“ค ๊ฐœ์ธ์˜ ์ƒ์กด ์ „๋žต๋“ค 274 2. ๊ฐœ๋ฐœ์ž๋“ค์˜ ์—ฐ๋Œ€์™€ ์ƒ์กด ๋„คํŠธ์›Œํฌ 276 3. ์ž์ƒ์  ์‹ค์ฒœ์˜ ํ•œ๊ณ„์™€ ๋‚จ๊ฒจ์ง„ ๊ณผ์ œ๋“ค 284 4์ ˆ. ๋„คํŠธ์›Œํฌํ™”๋œ ์ฐฝ์˜์  ํ˜‘์—…๊ณผ ๊ณต๋™์ฒด์  ๋Œ€์‘์˜ ๋ชจ์ƒ‰ 287 1. ํ˜‘์—… ๋„คํŠธ์›Œํฌ์˜ ํ™•์‚ฐ 287 2. ๋…ธ๋™์—์„œ ์ž‘์—…์œผ๋กœ, ๊ทธ๋ฆฌ๊ณ  ์ฐฝ์˜์  ํ˜‘์—…์œผ๋กœ : ๊ณต๋™์ฒด์  ๋Œ€์‘์˜ ๋ชจ์ƒ‰ 293 5์ ˆ. ๋ถˆ์•ˆ์ •์„ฑ ๊ทน๋ณต์„ ์œ„ํ•œ ์ œ๋„์ ยท์ •์ฑ…์  ๋Œ€์‘ ๋ฐฉ์•ˆ 297 1. ๋Œ€์‘๊ณผ ๊ทน๋ณต์„ ์œ„ํ•œ ๋ฐฉํ–ฅ์„ฑ 297 2. ์ œ๋„ ๊ฐœ์„ ์„ ํ†ตํ•œ ๊ฒŒ์ž„ ๊ฐœ๋ฐœ์ž์˜ ๊ถŒ์ต ๋ณดํ˜ธ 299 3. ๊ฒŒ์ž„ ๊ฐœ๋ฐœ์ž ์—ญ๋Ÿ‰๊ฐ•ํ™” ๋ฐ ์ƒ์ƒ์„ ์œ„ํ•œ ์ง€์› ์ •์ฑ… 302 4. ๊ฒŒ์ž„ ์—…๊ณ„์˜ ํ˜์‹  ๋…ธ๋ ฅ๋“ค 306 6์ ˆ. ์†Œ๊ฒฐ : ๋””์ง€ํ„ธ ์ฐฝ์˜ ๋…ธ๋™์ž๋“ค์€ ์–ด๋–ป๊ฒŒ ๋ถˆ์•ˆ์ •์„ฑ์— ๋Œ€์‘ํ•ด์•ผ ํ•˜๋Š”๊ฐ€? 309 ์ œ8์žฅ. ๊ฒฐ๋ก  313 1์ ˆ. ์—ฐ๊ตฌ์˜ ์ฃผ์š” ๊ฒฐ๊ณผ ๋ฐ ํ•ด์„ 313 2์ ˆ. ๋ถˆ์•ˆ์ •์„ฑ ๊ทน๋ณต๊ณผ ์ง€์†๊ฐ€๋Šฅํ•œ ๊ฒŒ์ž„ ์ƒํƒœ๊ณ„ ํ˜•์„ฑ์„ ์œ„ํ•œ ๊ณผ์ œ๋“ค 317 3์ ˆ. ์—ฐ๊ตฌ๊ฒฐ๊ณผ์— ๋Œ€ํ•œ ๋…ผ์˜์™€ ์„ฑ์ฐฐ 319 ์ฐธ๊ณ ๋ฌธํ—Œ 323 Abstract 335Docto

    Top-k ๊ฐœ์ธํ™”๋œ ํŽ˜์ด์ง€๋žญํฌ ์งˆ์˜ ์ฒ˜๋ฆฌ ์•Œ๊ณ ๋ฆฌ๋“ฌ

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์ „๊ธฐยท์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€, 2012. 2. ์ด์ƒ๊ตฌ.์˜ค๋Š˜๋‚  ๊ทธ๋ž˜ํ”„ ๋ฐ์ดํ„ฐ์˜ ์ง€์†์ ์ธ ์ถ•์ ๊ณผ ์ƒ์„ฑ์œผ๋กœ ์ธํ•ด ๊ทธ๋ž˜ํ”„ ๋ฐ์ดํ„ฐ๋ฅผ ํšจ์œจ์ ์œผ๋กœ ๋ถ„์„, ๊ณ„์‚ฐํ•˜์—ฌ ์ƒˆ๋กœ์šด ๋ฐ์ดํ„ฐ๋“ค์„ ๋นจ๋ฆฌ ๊ฒฐ๊ณผ์— ๋ฐ˜์˜ํ•˜๋Š” ์ž‘์—…์ด ์ค‘์š”ํ•ด์ง€๊ณ  ์žˆ๋‹ค. ๊ฐœ์ธํ™”๋œ ํŽ˜์ด์ง€๋žญํฌ๋Š” ๋ถ๋งˆํฌ ํ˜น์€ ์ถœ๋ฐœ ๋…ธ๋“œ๋ผ ๋ถˆ๋ฆฌ๋Š” ๋ช‡๋ช‡ ์ฃผ์š” ๋…ธ๋“œ์— ์„ ํ˜ธ๋„๋ฅผ ๋ถ€์—ฌํ•˜์—ฌ ๋‹ค๋ฅธ ๋…ธ๋“œ๋“ค์˜ ์ƒ๋Œ€์  ์ค‘์š”๋„ ํ˜น์€ ์—ฐ๊ด€๋„๋ฅผ ๊ณ„์‚ฐํ•˜๋Š” ๋ฐฉ๋ฒ•์œผ๋กœ, ๊ทธ๋ž˜ํ”„ ๋ฐ์ดํ„ฐ๋ฅผ ์ด์šฉํ•œ ๋Œ€ํ‘œ์ ์ธ ๊ณ„์‚ฐ ๋ฐฉ๋ฒ• ์ค‘ ํ•˜๋‚˜์ด๋‹ค. ํ•˜์ง€๋งŒ ๊ฐœ์ธํ™”๋œ ํŽ˜์ด์ง€๋žญํฌ๋ฅผ ๊ณ„์‚ฐํ•˜๋Š” ์ž‘์—…์€ ๋ง‰๋Œ€ํ•œ ๊ณ„์‚ฐ ๋น„์šฉ์„ ์ดˆ๋ž˜ํ•œ๋‹ค. ๋งŽ์€ ์‘์šฉ์—์„œ๋Š” ์ด ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด ๋ชจ๋“  ํ˜น์€ ์ผ๋ถ€์˜ ์ถœ๋ฐœ ๋…ธ๋“œ์— ๋Œ€ํ•œ ๊ฐœ์ธํ™”๋œ ํŽ˜์ด์ง€๋žญํฌ ๊ฐ’์„ ๋ฏธ๋ฆฌ ๊ณ„์‚ฐํ•˜์—ฌ ๊ทธ ๊ฒฐ๊ณผ๋ฅผ ์ €์žฅํ•œ ํ›„, ํ•„์š”ํ•  ๋•Œ๋งˆ๋‹ค ๊ทธ ๊ฒฐ๊ณผ๋ฅผ ์ฝ์–ด ๋“ค์—ฌ ๋ณด์—ฌ์ฃผ๋Š” ๋ฐฉ์‹์„ ์ด์šฉํ•œ๋‹ค. ํ•˜์ง€๋งŒ ์ด ๋ฐฉ์‹์€ ๋Œ€๊ทœ๋ชจ์˜ ์ €์žฅ ๊ณต๊ฐ„์ด ํ•„์š”ํ•˜๋ฉฐ, ์ƒˆ๋กœ์šด ๋ฐ์ดํ„ฐ์˜ ๋ฐ˜์˜์ด ๋Šฆ์–ด์ง€๋Š” ๋ฌธ์ œ๋ฅผ ๋ฐœ์ƒ์‹œํ‚จ๋‹ค. ๊ฒ€์ƒ‰ ๋กœ๊ทธ๋‚˜ ์†Œ์…œ ๋„คํŠธ์›Œํฌ์ฒ˜๋Ÿผ ์‹ค์‹œ๊ฐ„์œผ๋กœ ๋ฐ์ดํ„ฐ๊ฐ€ ์ƒ์„ฑ๋˜๋Š” ์‹œ์Šคํ…œ์—์„œ๋Š” ์ด๋Ÿฌํ•œ ๋ฐฉ์‹์€ ์˜ฌ๋ฐ”๋ฅธ ๊ณ„์‚ฐ ๊ฒฐ๊ณผ๋ฅผ ๋ณด์—ฌ์ค€๋‹ค๊ณ  ํ•˜๊ธฐ ์–ด๋ ต๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๊ฐœ์ธํ™”๋œ ํŽ˜์ด์ง€๋žญํฌ๋ฅผ ์‚ฌ์šฉํ•˜๋Š” ์‘์šฉ๋“ค์ด ์ฃผ๋กœ ์ƒ์œ„ k๊ฐœ์˜ ๋…ธ๋“œ๋ฅผ ํ™œ์šฉํ•˜๋Š” ์ ๊ณผ ์•ฝ๊ฐ„์˜ ์˜ค์ฐจ๊ฐ€ ํ—ˆ์šฉ๋œ๋‹ค๋Š” ์ ์— ์ฐฉ์•ˆํ•˜์—ฌ, ์ฃผ์–ด์ง„ ์ถœ๋ฐœ ๋…ธ๋“œ์˜ ๋ถ„ํฌ์— ๋Œ€ํ•˜์—ฌ ๊ฐœ์ธํ™”๋œ ํŽ˜์ด์ง€๋žญํฌ ๊ฐ’์„ ๊ตฌํ•˜๋Š” ๋Œ€์‹  ์ƒ์œ„ k๊ฐœ์˜ ๊ฐœ์ธํ™”๋œ ํŽ˜์ด์ง€๋žญํฌ ๊ฐ’์„ ๊ฐ–๋Š” ๋…ธ๋“œ๋“ค์„ ์‹ค์‹œ๊ฐ„์œผ๋กœ ์ฐพ์•„์ฃผ๋Š” ๋ฐฉ๋ฒ•์ธ ProbScoring์„ ์ œ์‹œํ•œ๋‹ค. ๊ธฐ์กด์˜ ๋ฐฉ๋ฒ•๋“ค์ด ์„ ๊ณ„์‚ฐ ๋ฐฉ์‹์„ ์ด์šฉํ•˜๊ธฐ ๋•Œ๋ฌธ์— ์•ž์„œ ์‚ดํŽด๋ณธ ์ €์žฅ ๊ณต๊ฐ„์˜ ๋ฌธ์ œ, ์ƒˆ๋กœ์šด ๋ฐ์ดํ„ฐ์˜ ๋ฐ˜์˜ ๋ฌธ์ œ ๋“ฑ์ด ๋ฐœ์ƒํ•œ ๋ฐ˜๋ฉด, ๋ณธ ์—ฐ๊ตฌ์—์„œ ์ œ์‹œํ•˜๋Š” ๋ฐฉ๋ฒ•์€ ์งˆ์˜๊ฐ€ ๋ฐœ์ƒํ•˜๋Š” ์‹œ์ ์— ์‹ค์‹œ๊ฐ„์œผ๋กœ ๋น ๋ฅด๊ฒŒ ๊ณ„์‚ฐํ•˜๋Š” ๋ฐฉ์‹์„ ์ด์šฉํ•˜๊ธฐ ๋•Œ๋ฌธ์— ์ด๋Ÿฌํ•œ ๋ฌธ์ œ์ ์ด ์ƒ๊ธฐ์ง€ ์•Š๋Š”๋‹ค. ์‹ค์‹œ๊ฐ„ ๊ณ„์‚ฐ์„ ์œ„ํ•ด ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๊ณ„์ธตํ™”๋œ ๊ทธ๋ž˜ํ”„์˜ ๊ฐœ๋…์„ ๋„์ž…ํ•˜๊ณ , ๊ณ„์ธตํ™”๋œ ๊ทธ๋ž˜ํ”„์™€ ๊ฐœ์ธํ™”๋œ ํŽ˜์ด์ง€๋žญํฌ์˜ ๊ด€๊ณ„๋ฅผ ํŒŒ์•…ํ•œ๋‹ค. ๊ทธ๋ฆฌ๊ณ  ๊ณ„์ธตํ™”๋œ ๊ทธ๋ž˜ํ”„๋ฅผ ํ™œ์šฉํ•œ ProbScoring์˜ ์ž์„ธํ•œ ์ง„ํ–‰ ๊ณผ์ •์„ ์„ค๋ช…ํ•œ๋‹ค. ์‹ค์ œ ๊ทธ๋ž˜ํ”„ ๋ฐ์ดํ„ฐ๋ฅผ ๋Œ€์ƒ์œผ๋กœ ์‹คํ—˜ํ•œ ๊ฒฐ๊ณผ ProbScoring์€ ์„ ๊ณ„์‚ฐ ๋ฐฉ์‹ ์—†์ด๋„ ์งˆ์˜๊ฐ€ ๋ฐœ์ƒํ•œ ์‹œ์ ์— ๊ธฐ์กด์˜ ๋ฐฉ๋ฒ•๋“ค๋ณด๋‹ค ํšจ์œจ์ ์œผ๋กœ ์ƒ์œ„ k๊ฐœ์˜ ๋…ธ๋“œ๋ฅผ ์ฐพ์•„์ฃผ๋Š” ๋™์‹œ์— ๋†’์€ ์ˆ˜์ค€์˜ ์ •ํ™•๋„๋ฅผ ๋ณด์—ฌ์ค€๋‹ค. ๊ฒฐ๋ก ์ ์œผ๋กœ ProbScoring์€ ์„ ๊ณ„์‚ฐ ๋ฐฉ์‹ ์—†์ด๋„ ์งˆ์˜๊ฐ€ ๋ฐœ์ƒํ•œ ์‹œ์ ์— ์‹ค์‹œ๊ฐ„์œผ๋กœ ์ƒ์œ„ k์˜ ๊ฐœ์ธํ™”๋œ ํŽ˜์ด์ง€๋žญํฌ ๊ฐ’์„ ๊ฐ–๋Š” ๋…ธ๋“œ๋“ค์„ ์ฐพ์•„์ค€๋‹ค.In recent years, an efficient method of performing analyses and computations on graph networks, regarding newly created data, has been needed due to continuous growth of datasets. Personalized PageRank is one of the most well-known computation methods for graphs. Personalized PageRank computes the relative importance or relevance with respect to a set of given nodes, called start nodes or bookmark. However, the online computation of Personalized PageRank on a massive size of datasets is infeasible due to its huge computational cost. So many applications have considered the way of precomputing Personalized PageRank for all or some nodes, keeping them in a secondary storage, and reading the results on demand. This method, though, has two problems; it requires an enormous size of storages and it is difficult to use new data to affect the results. For applications that cope with frequent updates to data such as search logs or social networks, the precomputation is not a suitable method. In this thesis, the two observations can be noticed. One is that applications using Personalized PageRank mainly need to find the top-k nodes with the largest values of Personalized PageRank. The other is that an erroneous detection of a small number of nodes in the top-k is allowable. With these observations, I propose ProbScoring, an online approximate method of finding the top-k nodes with the largest values of Personalized PageRank with respect to a distribution of start nodes, rather than computing the Personalized PageRank for each node. While the previous methods have faced the challenges of storages of keeping precomputation results and hardness of utilizing new data, ProbScoring is far from those issues since it computes top-k nodes quickly at query time. For online computation, I introduce the notion of layered graph and discover the relationship between layered graph and Personalized PageRank. And I explain the process of ProbScoring using the layered graph. The experimental results verify that ProbScoring shows better efficiency and accuracy of finding top-k nodes than previous methods. In conclusion, ProbScoring is an online computation method of finding top-k nodes with the largest values of Personalized PageRank at query time without precomputation.Maste
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