10 research outputs found

    Digital Healthcare for Airway Diseases from Personal Environmental Exposure

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    Digital technologies have emerged in various dimensions of human life, ranging from education to professional services to well-being. In particular, health products and services have expanded by the use and development of artificial intelligence, mobile health applications, and wearable electronic devices. Such advancements have enabled accurate and updated tracking and modeling of health conditions. For instance, digital health technologies are capable of measuring environmental pollution and predicting its adverse health effects. Several health conditions, including chronic airway diseases such as asthma and chronic obstructive pulmonary disease, can be exacerbated by pollution. These diseases impose substantial health burdens with high morbidity and mortality. Recently, efforts have been made to develop digital technologies to alleviate such conditions. Moreover, the COVID-19 pandemic has facilitated the application of telemedicine and telemonitoring for patients with chronic airway diseases. This article reviews current trends and studies in digital technology utilization for investigating and managing environmental exposure and chronic airway diseases. First, we discussed the recent progression of digital technologies in general environmental healthcare. Then, we summarized the capacity of digital technologies in predicting exacerbation and self-management of airway diseases. Concluding these reviews, we provided suggestions to improve digital health technologies' abilities to reduce the adverse effects of environmental exposure in chronic airway diseases, based on personal exposure-response modeling.ope

    Ablation of the deubiquitinase USP15 ameliorates nonalcoholic fatty liver disease and nonalcoholic steatohepatitis

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    Nonalcoholic fatty liver disease (NAFLD) occurs due to the accumulation of fat in the liver, leading to fatal liver diseases such as nonalcoholic steatohepatitis (NASH) and cirrhosis. Elucidation of the molecular mechanisms underlying NAFLD is critical for its prevention and therapy. Here, we observed that deubiquitinase USP15 expression was upregulated in the livers of mice fed a high-fat diet (HFD) and liver biopsies of patients with NAFLD or NASH. USP15 interacts with lipid-accumulating proteins such as FABPs and perilipins to reduce ubiquitination and increase their protein stability. Furthermore, the severity of NAFLD induced by an HFD and NASH induced by a fructose/palmitate/cholesterol/trans-fat (FPC) diet was significantly ameliorated in hepatocyte-specific USP15 knockout mice. Thus, our findings reveal an unrecognized function of USP15 in the lipid accumulation of livers, which exacerbates NAFLD to NASH by overriding nutrients and inducing inflammation. Therefore, targeting USP15 can be used in the prevention and treatment of NAFLD and NASH.ope

    Association of serum ferritin levels with smoking and lung function in the Korean adult population: analysis of the fourth and fifth Korean National Health and Nutrition Examination Survey

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    BACKGROUND: Iron-catalyzed oxidative stress contributes to lung injury after exposure to various toxins, including cigarette smoke. An oxidant/antioxidant imbalance is considered to play a critical role in the pathogenesis of COPD. Ferritin is a key protein in iron homeostasis, and its capacity to oxidize and sequester the metal preventing iron prooxidant activity implicates its possible role in the alteration of antioxidant imbalance. We investigated the relationship among cigarette smoking, lung function, and serum ferritin concentration in a large cohort representative of the Korean adult population. MATERIALS AND METHODS: Among 50,405 participants of the Korean National Health and Nutrition Examination Survey from 2010 to 2014, 15,239 adult subjects older than 40 years with serum ferritin levels and spirometric data were selected for this study. RESULTS: The mean age was 56.5 years for men (43%) and 56.9 years for women (57%). The prevalence of airway obstruction was 13.4%, which was significantly higher in men than in women, and increased in former or current smokers. The median levels of serum ferritin were highest in the airway obstruction group, followed by the restrictive pattern group, and lowest in the normal lung function group. The median ferritin levels were increased by smoking status and amounts in each spirometric subgroup. In multivariable regression analysis, serum ferritin was positively associated with forced expiratory volume in 1 second and forced expiratory volume in 1 second/forced vital capacity, whereas the smoking amount was negatively associated with the adjustment with age, sex, height, and weight. CONCLUSION: Serum ferritin levels were increased in former or current smokers and were increased with smoking amount in all subgroups of participants categorized according to spirometric results. The result was also evident in the subgroups divided by obstructive severity. While smoking amount was inversely related to lung function, higher levels of serum ferritin were associated with enhanced spirometric results in a representative sample of the general Korean adult population. Future prospective studies will be needed to clarify the causality between serum ferritin and lung functions and their role in COPD morbidity.ope

    Analysis of changes in income tax rate and corporate tax rate due to tax competition and the replacement of ruling party

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ํ–‰์ •๋Œ€ํ•™์› ํ–‰์ •ํ•™๊ณผ(์ •์ฑ…ํ•™์ „๊ณต), 2022. 8. ์ •๊ด‘ํ˜ธ.์กฐ์„ธ๋Š” ์ •๋ถ€ ํ™œ๋™์„ ์œ„ํ•œ ์ž์›์„ ๋ฏผ๊ฐ„๋ถ€๋ฌธ์—์„œ ์ •๋ถ€๋ถ€๋ฌธ์œผ๋กœ ์ด์ „ํ•˜๋Š” ์ˆ˜๋‹จ์œผ๋กœ, ๊ตญ๋ฏผ ๋˜๋Š” ์ฃผ๋ฏผ์— ๋Œ€ํ•ด ๋ฐ˜๋Œ€๊ธ‰๋ถ€ ์—†์ด ๋ถ€๊ณผยท์ง•์ˆ˜ํ•˜๋Š” ๊ธˆ์ „๊ธ‰๋ถ€์ด๋‹ค. ๊ตญ๋ฏผ์˜ ์žฌ์‚ฐ๊ถŒ์— ๋งค์šฐ ํฐ ์˜ํ–ฅ์„ ๋ฏธ์น˜๋ฏ€๋กœ ๋ฒ•๋ฅ ๋กœ ์ •ํ•˜๊ณ , ๋ฒ•๋ฅ ๋กœ ๊ฐœ์ •ํ•˜๋„๋ก ํ•˜๋Š” ๋“ฑ ์ค‘์š”ํ•œ ์‚ฌ์•ˆ์ž„์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ  ์กฐ์„ธ์ •์ฑ…์˜ ์˜์‚ฌ๊ฒฐ์ • ๊ณผ์ •์— ๋Œ€ํ•ด์„œ๋Š” ์—ฐ๊ตฌ๊ฐ€ ์ถฉ๋ถ„ํ•˜์ง€ ์•Š๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ์กฐ์„ธ์ •์ฑ…์—์„œ ๊ฐ€์žฅ ์ƒ์ง•์„ฑ์ด ์žˆ๋Š” ์„ธ์œจ์˜ ๊ฒฐ์ •๊ณผ์ •์— ๋Œ€ํ•ด ์‚ดํŽด๋ณด๊ณ ์ž ํ•œ๋‹ค. ํŠนํžˆ, ๋Œ€ํ‘œ ์„ธ๋ชฉ์ธ ์†Œ๋“์„ธ์™€ ๋ฒ•์ธ์„ธ์— ๋Œ€ํ•˜์—ฌ ์†Œ๋“์„ธ์œจ๊ณผ ๋ฒ•์ธ์„ธ์œจ์˜ ๊ฒฐ์ •์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ์š”์ธ์„ ์‚ดํŽด๋ดค๋‹ค. ๊ตฌ์ฒด์ ์œผ๋กœ ๊ตญ๊ฐ€ ๊ฐ„ ์กฐ์„ธ๊ฒฝ์Ÿ์ด ํ•œ๊ตญ์˜ ์„ธ์œจ๊ฒฐ์ •์— ์–ด๋–ค ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š”์ง€์™€ ์ง‘๊ถŒ์ •๋‹น์˜ ์ •์น˜์„ฑํ–ฅ์— ๋”ฐ๋ผ ์„ธ์œจ๊ฒฐ์ •์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ์ค‘์ ์ ์œผ๋กœ ์‚ดํŽด๋ณด๊ณ ์ž ํ–ˆ๋‹ค. ๊ทธ ๋ฐ–์— ๊ฒฝ์ œ์„ฑ์žฅ๋ฅ , ์žฌ์ •๊ฑด์ „์„ฑ๊ณผ ๊ด€๋ จ์ด ์žˆ๋Š” GDP ๋Œ€๋น„ ํ†ตํ•ฉ์žฌ์ •์ˆ˜์ง€๋น„์œจ ๋“ฑ๋„ ์„ธ์œจ๊ฒฐ์ •์— ์˜ํ–ฅ์„ ๋ฏธ์นœ๋‹ค๊ณ  ๋ณด๊ณ  ํ•จ๊ป˜ ๊ณ ๋ คํ–ˆ๋‹ค. ์ด๋Ÿฌํ•œ ๋ถ„์„์„ ์œ„ํ•ด ์†Œ๋“์„ธ์œจ๊ณผ ๋ฒ•์ธ์„ธ์œจ์— ๋Œ€ํ•ด ๊ฐ๊ฐ ๋‹ค์ค‘ํšŒ๊ท€๋ถ„์„์„ ์ง„ํ–‰ํ•˜๊ณ , ์ด์™€ ํ•จ๊ป˜ ์†Œ๋“์„ธ์œจ๊ณผ ๋ฒ•์ธ์„ธ์œจ์— ๋Œ€ํ•œ ๋™์‹œ์  ์ƒ๊ด€๊ด€๊ณ„๋ฅผ ๊ณ ๋ คํ•˜์—ฌ SUR ํšŒ๊ท€๋ถ„์„(Seemingly unrelated regression)์„ ์ง„ํ–‰ํ–ˆ๋‹ค. ๋˜ํ•œ ๋…๋ฆฝ๋ณ€์ˆ˜ ์ „๊ธฐ(t-1๊ธฐ)์˜ ๋ณ€ํ™”๊ฐ€ ์ข…์†๋ณ€์ˆ˜(t๊ธฐ)์— ์˜ํ–ฅ์„ ๋ฏธ์น  ์ˆ˜ ์žˆ๋Š” ์ƒํ™ฉ์„ ๊ณ ๋ คํ•˜์—ฌ ์ „๊ธฐ์— ๋Œ€ํ•ด์„œ๋„ ํšŒ๊ท€๋ถ„์„์„ ํ•จ๊ป˜ ์ง„ํ–‰ํ–ˆ๋‹ค. ๋‹ค๊ฐ์ ์œผ๋กœ ํšŒ๊ท€๋ถ„์„์„ ์ง„ํ–‰ํ•˜์—ฌ ๊ฒฐ๊ณผ๋ฅผ ๋„์ถœํ•˜์˜€์œผ๋‚˜, ๊ด€์ธก๊ฐ’์ด ์ถฉ๋ถ„ํ•˜์ง€ ์•Š๊ณ  ์ผ๋ถ€ ๋น„์ •์ƒ์ (non-stationary) ๋ณ€์ˆ˜๋ฅผ ํฌํ•จํ•˜๋Š” ํ•œ๊ณ„๊ฐ€ ์žˆ๋‹ค. ํšŒ๊ท€๋ถ„์„ ๊ฒฐ๊ณผ ๊ตญ๊ฐ€ ๊ฐ„ ์กฐ์„ธ๊ฒฝ์Ÿ, ์ฆ‰ ์ฃผ์š”๊ตญ ํ‰๊ท  ์†Œ๋“์„ธ์œจ ๋ฐ ๋ฒ•์ธ์„ธ์œจ์˜ ๋ณ€ํ™”๋Š” ๋™๊ธฐ(t๊ธฐ)์™€ ์ „๊ธฐ(t-1๊ธฐ) ๋ชจ๋‘ ํ•œ๊ตญ์˜ ์†Œ๋“์„ธ์œจ๊ณผ ๋ฒ•์ธ์„ธ์œจ ๋ณ€ํ™”์— ์œ ์˜๋ฏธํ•˜๊ฒŒ ์˜ํ–ฅ์„ ๋ฏธ์น˜๋ฉฐ, ์กฐ์„ธ์ธํ•˜๊ฒฝ์Ÿ์œผ๋กœ ์ธํ•ด ํ•œ๊ตญ์˜ ์†Œ๋“์„ธ์œจ๊ณผ ๋ฒ•์ธ์„ธ์œจ๋„ ์ธํ•˜ํ•˜๋Š” ๊ฒƒ์œผ๋กœ ํ™•์ธ๋๋‹ค. ์‹ค์ œ ์ด๋ช…๋ฐ• ์ •๋ถ€์—์„œ๋Š” ์ •๋ถ€ ์ธก ์„ธ์ œ๊ฐœํŽธ์•ˆ๊ณผ ์—ฌ๋‹น๊ณผ ์•ผ๋‹น์˜ ์ฃผ์š” ๋…ผ๋ฆฌ๋กœ ์กฐ์„ธ๊ฒฝ์Ÿ(tax competition) ๊ฐœ๋…์ด ๋“ฑ์žฅํ•˜๋ฉฐ, ์„ธ์œจ ๊ฒฐ์ •์— ์žˆ์–ด ์ฃผ์š”ํ•œ ์š”์ธ์ด ๋˜๊ณ  ์žˆ๋‹ค. ๋‹ค๋งŒ, ๊ธ€๋กœ๋ฒŒ ๊ธˆ์œต์œ„๊ธฐ ์ดํ›„ ๋Œ€๋‚ด์ ์œผ๋กœ ๊ฒฝ์ œ์ƒํ™ฉ์ด๋‚˜ ์žฌ์ •๊ฑด์ „์„ฑ ๋“ฑ์ด ๊ฐ•์กฐ๋œ ์ธก๋ฉด์ด ์žˆ๋‹ค. ๋ฌธ์žฌ์ธ ์ •๋ถ€์—์„œ๋Š” ์ฃผ์š”๊ตญ์˜ ๋ฒ•์ธ์„ธ์œจ ์ธํ•˜์ถ”์„ธ๋Š” ๊ณ„์†๋์œผ๋‚˜ ์ฃผ์š”๊ตญ ์†Œ๋“์„ธ์œจ์€ ์ฆ๊ฐ€ํ•˜๋Š” ์ถ”์„ธ๋กœ ์ „ํ™˜๋˜์–ด ๊ณต์•ฝ๋Œ€๋กœ ์„ธ์œจ ์ธ์ƒ ๋ฐ ์ฆ์„ธ๋ฅผ ์ถ”์ง„ํ•  ์—ฌ๋ ฅ์ด ์ƒ๊ฒผ๋‹ค๊ณ  ๋ณผ ์ˆ˜ ์žˆ๋‹ค. ๋˜ํ•œ, ์ง‘๊ถŒ์ •๋‹น ๋ณ€ํ™”์— ๋”ฐ๋ฅธ ์„ธ์œจ ๋ณ€ํ™”๋Š” ์†Œ๋“์„ธ์œจ์˜ ๊ฒฝ์šฐ ์œ ์˜๋ฏธํ•œ ๊ฒฐ๊ณผ๋Š” ์•„๋‹ˆ์ง€๋งŒ ๋ณด์ˆ˜์ •๋‹น์ด ๋ฏผ์ฃผ๋‹น๊ณ„์— ๋น„ํ•ด ๋†’๊ฒŒ ์œ ์ง€ํ•œ๋‹ค๊ณ  ๋‚˜์™€ ์ผ๋ฐ˜์ ์ธ ์ธ์‹๊ณผ๋Š” ๋‹ค๋ฅด๋‹ค. ๋ฒ•์ธ์„ธ์œจ์€ ๋ณด์ˆ˜์ •๋‹น์ด ๋ฏผ์ฃผ๋‹น๊ณ„์— ๋น„ํ•ด ๋‚ฎ๊ฒŒ ์œ ์ง€ํ•˜๋ฉฐ ์ด๋Š” ์œ ์˜๋ฏธํ•œ ๊ฒฐ๊ณผ๋กœ ๋„์ถœ๋๋‹ค. ์ด๋Ÿฌํ•œ ๊ฒฐ๊ณผ๋Š” ๋ฏผ์ฃผ๋‹น๊ณ„ ์ง‘๊ถŒ์‹œ๊ธฐ์ธ ๊น€๋Œ€์ค‘ยท๋…ธ๋ฌดํ˜„ ์ •๋ถ€์—์„œ๋„ ์„ธ์œจ์ธํ•˜๊ฐ€ ๊ณ„์†๋œ ๋ฐ˜๋ฉด, ๋ณด์ˆ˜์ •๋‹น ์ง‘๊ถŒ์‹œ๊ธฐ์ธ ์ด๋ช…๋ฐ• ์ •๋ถ€์—์„œ๋Š” ๊ฐ์„ธ๋ฅผ ์ ๊ทน ์ถ”๊ตฌํ–ˆ์Œ์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ  ์˜คํžˆ๋ ค ์†Œ๋“์„ธ์œจ ์ธ์ƒ์ด ๋‚˜ํƒ€๋‚˜๋Š” ๋“ฑ์˜ ์˜ํ–ฅ์œผ๋กœ ๋ณด์ธ๋‹ค. ๊ธ€๋กœ๋ฒŒ ๊ธˆ์œต์œ„๊ธฐ ์ดํ›„ ์ฆ์„ธ ํ•„์š”์„ฑ์ด ์ œ๊ธฐ๋˜๋Š” ์ƒํ™ฉ์—์„œ ๋ณด์ˆ˜์ •๋‹น์€ ์†Œ๋“์„ธ์œจ ์ธํ•˜๋Š” ์œ ๋ณดํ•˜๋˜ ๋ฒ•์ธ์„ธ์œจ์€ ๊ณ„์† ์ธํ•˜๋ฅผ ์ถ”์ง„ํ•˜์ž๊ณ  ์ฃผ์žฅํ•˜๋Š” ๋“ฑ ์†Œ๋“์„ธ์œจ ๋ณด๋‹ค๋Š” ๋ฒ•์ธ์„ธ์œจ ์ธํ•˜๋ฅผ ๋ณด๋‹ค ๊ณ ์ˆ˜ํ•˜๋Š” ๊ฒฝํ–ฅ์ด ์žˆ์—ˆ๋‹ค. ๊ทธ ๊ฒฐ๊ณผ ํšŒ๊ท€๋ถ„์„ ๊ฒฐ๊ณผ์™€ ๊ฐ™์ด ๋ณด์ˆ˜์ •๋‹น์ด ๋ฏผ์ฃผ๋‹น๊ณ„์— ๋น„ํ•ด ๋†’์€ ์†Œ๋“์„ธ์œจ์„ ์œ ์ง€ํ•œ๋‹ค๋Š” ๊ฒฐ๊ณผ๊ฐ€ ๋‚˜์˜ฌ ์ˆ˜ ์žˆ์—ˆ์œผ๋ฉฐ, ์ •์น˜์ง€ํ˜•๊ณผ ๋‹น์‹œ ๊ฒฝ์ œ์  ์ƒํ™ฉ ๋“ฑ์— ๋”ฐ๋ผ ์ •์น˜์  ํƒ€ํ˜‘์˜ ๊ฒฐ๊ณผ๊ฐ€ ๋‹ค์–‘ํ•œ ์–‘ํƒœ๋กœ ๋‚˜ํƒ€๋‚  ์ˆ˜ ์žˆ์Œ์„ ํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ์ตœ๊ทผ ๋ฏธ๊ตญ์˜ ๋ฐ”์ด๋“  ์ •๋ถ€๋Š” ์ •๋ถ€์˜ ์ ๊ทน์ ์ธ ์—ญํ• ์„ ๊ฐ•ํ™”ํ•˜๊ณ  ๋ฒ•์ธ์„ธ์œจ ์ธ์ƒ ๋“ฑ์„ ๋…ผ์˜ํ•˜๊ณ  ์žˆ๋Š” ์ƒํ™ฉ์ด๋ฉฐ, ์†Œ๋“์„ธ์œจ์€ ๊ธ€๋กœ๋ฒŒ ๊ธˆ์œต์œ„๊ธฐ ์ดํ›„ ์ธ์ƒ์ถ”์„ธ๊ฐ€ ๊ณ„์†๋˜๊ณ  ์žˆ์–ด ํ•œ๊ตญ๋„ ์„ธ์œจ์ธ์ƒ์˜ ์—ฌ๋ ฅ์ด ์ƒ๊ธธ ๊ฒƒ์œผ๋กœ ๋ณด์ธ๋‹ค. ๋‹ค๋งŒ, ๋ฌธ์žฌ์ธ ์ •๋ถ€์—์„œ ๊ตญ์ œ์ ์ธ ์ถ”์„ธ์— ์šฐ์„ ํ•˜์—ฌ ๋ฒ•์ธ์„ธ์œจ๊ณผ ์†Œ๋“์„ธ์œจ์„ ์ธ์ƒํ•œ ๊ฒฝํ–ฅ์ด ์žˆ์œผ๋ฏ€๋กœ ์กฐ์„ธ๊ฒฝ์Ÿ์˜ ์ธก๋ฉด์—์„œ ์ตœ๊ทผ ์„ธ์œจ ์ธ์ƒ ์†๋„์— ๋Œ€ํ•ด ๊ฐ๊ด€์ ์ธ ํ‰๊ฐ€๊ฐ€ ํ•„์š”ํ•˜๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ตœ๊ทผ ๋ณด์ˆ˜์ •๋‹น, ๋ฏผ์ฃผ๋‹น๊ณ„ ์ •๋‹น ๋ชจ๋‘ ๋‹ค์–‘ํ•œ ๊ฒฝ์ œยท๋ณต์ง€ ์ •์ฑ… ๋น„์ „์„ ์ œ์‹œํ•˜๊ณ  ์žˆ์œผ๋‚˜ ์กฐ์„ธ๋ถ€๋‹ด ๊ฐ•ํ™”์— ๋Œ€ํ•ด ๋…ผ์˜ํ•˜๋Š”๋ฐ ๋ถ€๋‹ด์„ ๋Š๋ผ๋ฉฐ ๊ณต๋ก ํ™”๊ฐ€ ์ถฉ๋ถ„ํžˆ ์ด๋ค„์ง€์ง€๋Š” ์•Š๋Š” ์ƒํ™ฉ์ด๋‹ค. ๊ตญ๋ฏผ์˜ ์‚ถ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์ด ํฐ ์กฐ์„ธ์ •์ฑ…์ด ๋ณด๋‹ค ์˜ฌ๋ฐ”๋ฅด๊ฒŒ ์ˆ˜๋ฆฝ๋˜๊ธฐ ์œ„ํ•ด์„œ๋Š” ๊ธฐํš์žฌ์ •๋ถ€ ๋ฐ ์ƒ์ž„์œ„์›ํšŒ์ธ ๊ธฐํš์žฌ์ •์œ„์›ํšŒ๋Š” ์„ธ์œจ๊ณผ ์„ธ๋ถ€๋‹ด์— ๋Œ€ํ•œ ๋…ผ์˜๊ฐ€ ์ถฉ๋ถ„ํžˆ ๋‹ค๋ค„์งˆ ์ˆ˜ ์žˆ๋„๋ก ์žฅ์„ ๋งŒ๋“ค ํ•„์š”๊ฐ€ ์žˆ๋‹ค. ์กฐ์„ธ์ •์ฑ…์˜ ์˜์‚ฌ๊ฒฐ์ •์— ๋Œ€ํ•ด ์—ฐ๊ตฌ๊ฐ€ ๋ถ€์กฑํ•œ ์ƒํ™ฉ์—์„œ ์˜์‚ฌ๊ฒฐ์ •์— ๋ฏธ์น˜๋Š” ๋Œ€๋‚ด์™ธ์  ์ฃผ์š” ์š”์ธ์„ ๋ถ„์„ํ•จ์œผ๋กœ์จ ํ–ฅํ›„ ์˜์‚ฌ๊ฒฐ์ • ๊ณผ์ • ๋“ฑ์— ๋„์›€์ด ๋  ๊ฒƒ์œผ๋กœ ๋ณธ๋‹ค. ์ถ”ํ›„ ์—ฐ๊ตฌ์—์„œ๋Š” OECD ๋“ฑ ์ฃผ์š” ๊ตญ๊ฐ€์˜ ์„ธ์œจ ๊ฒฐ์ •๊ณผ์ •์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ์š”์ธ๋„ ํ•จ๊ป˜ ๋ถ„์„ํ•œ๋‹ค๋ฉด ์—ฐ๊ตฌ ๊ฒฐ๊ณผ๊ฐ€ ๋ณด๋‹ค ๋’ท๋ฐ›์นจ๋  ๊ฒƒ์œผ๋กœ ๊ธฐ๋Œ€๋œ๋‹ค.์ œ1์žฅ ์„œ๋ก  1 ์ œ1์ ˆ ์—ฐ๊ตฌ์˜ ๋ชฉ์ ๊ณผ ํ•„์š”์„ฑ 1 ์ œ2์ ˆ ์—ฐ๊ตฌ ๋ฒ”์œ„ ๋ฐ ์—ฐ๊ตฌ ๋ฐฉ๋ฒ• 5 ์ œ2์žฅ ์ด๋ก ์  ๋ฐฐ๊ฒฝ๊ณผ ์„ ํ–‰์—ฐ๊ตฌ ๊ฒ€ํ†  7 ์ œ1์ ˆ ์กฐ์„ธ์ธํ•˜๊ฒฝ์Ÿ ๊ด€๋ จ ์ด๋ก ์  ๋ฐฐ๊ฒฝ๊ณผ ์„ ํ–‰์—ฐ๊ตฌ 7 ์ œ2์ ˆ ๊ฐ์„ธ๋ก ๊ณผ ์ฆ์„ธ๋ก  ๊ด€๋ จ ์ด๋ก ์  ๋ฐฐ๊ฒฝ๊ณผ ์„ ํ–‰์—ฐ๊ตฌ 12 ์ œ3์ ˆ ์„ ํ–‰์—ฐ๊ตฌ์˜ ์˜์˜์™€ ํ•œ๊ณ„ 18 ์ œ3์žฅ ๋ฒ•์ธ์„ธ์œจ๊ณผ ์†Œ๋“์„ธ์œจ ๋ณ€ํ™” 20 ์ œ1์ ˆ ์ฃผ์š”๊ตญ์˜ ๋ฒ•์ธ์„ธ์œจยท์†Œ๋“์„ธ์œจ ๋ณ€ํ™” ์ถ”์ด 20 ์ œ2์ ˆ ํ•œ๊ตญ์˜ ์ •๊ถŒ๋ณ„ ๋ฒ•์ธ์„ธ์œจยท์†Œ๋“์„ธ์œจ ๋ณ€ํ™” ์ถ”์ด 24 ์ œ3์ ˆ ์„ธ์œจ๊ฒฐ์ •์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ์š”์ธ 33 ์ œ4์žฅ ์—ฐ๊ตฌ์˜ ๋ถ„์„ํ‹€ ๋ฐ ์—ฐ๊ตฌ ์„ค๊ณ„ 39 ์ œ1์ ˆ ์—ฐ๊ตฌ ๋ณ€์ˆ˜ 39 ์ œ2์ ˆ ์—ฐ๊ตฌ์˜ ๋ถ„์„ํ‹€ 42 ์ œ3์ ˆ ์—ฐ๊ตฌ ๊ฐ€์„ค 43 ์ œ4์ ˆ ๋ถ„์„๋ชจํ˜• ๋ฐ ์ž๋ฃŒ์ˆ˜์ง‘ 45 ์ œ5์žฅ ํšŒ๊ท€๋ถ„์„ ๊ฒฐ๊ณผ 52 ์ œ1์ ˆ ์†Œ๋“์„ธ์œจ์— ๋Œ€ํ•œ ๋‹ค์ค‘ํšŒ๊ท€๋ถ„์„ 52 ์ œ2์ ˆ ๋ฒ•์ธ์„ธ์œจ์— ๋Œ€ํ•œ ๋‹ค์ค‘ํšŒ๊ท€๋ถ„์„ 54 ์ œ3์ ˆ ์†Œ๋“์„ธ์œจ๊ณผ ๋ฒ•์ธ์„ธ์œจ์— ๋Œ€ํ•œ SUR ํšŒ๊ท€๋ถ„์„ 56 ์ œ4์ ˆ ์†Œ๋“์„ธ์œจ๊ณผ ๋ฒ•์ธ์„ธ์œจ์— ๋Œ€ํ•œ SUR ํšŒ๊ท€๋ถ„์„(๋…๋ฆฝ๋ณ€์ˆ˜: t-1๊ธฐ) 61 ์ œ5์ ˆ ๋ถ„์„๊ฒฐ๊ณผ ๋ฐ ํ•œ๊ณ„ 66 ์ œ6์žฅ ๊ฒฐ๋ก  70 ์ฐธ๊ณ ๋ฌธํ—Œ 75 [์ฐธ๊ณ ] ํšŒ๊ท€๋ถ„์„ ๊ธฐ์ดˆ ๋ฐ์ดํ„ฐ 78 [์ฐธ๊ณ ] ํšŒ๊ท€๋ถ„์„ ๊ฒฐ๊ณผ 79์„

    ํšจ์œจ์  ํ† ์„๋ฅ˜ ๋ฐœ์ƒ ์˜ˆ์ธก์„ ์œ„ํ•œ DBMS๊ธฐ๋ฐ˜ ์ง€์งˆ์ •๋ณด์‹œ์Šคํ…œ ์„ค๊ณ„

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€ ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค์ „๊ณต, 2016. 8. ๋ฌธ๋ด‰๊ธฐ.DBMS๋Š” ๋‚ ์ด ๊ฐˆ์ˆ˜๋ก ๋ฐœ์ „ํ•ด ๋‚˜๊ฐ€๋ฉฐ ์ˆ˜๋งŽ์€ ๋ถ„์•ผ์— ํŠนํ™”๋œ ์—ฌ๋Ÿฌ ์ข…๋ฅ˜์˜ DBMS ๋“ค์ด ๋‚˜์˜ค๊ณ  ์žˆ์ง€๋งŒ ์ด ์ˆ˜๋งŽ์€ ๊ธฐ๋Šฅ์„ ์กฐํ•ฉํ•˜์—ฌ ์›ํ•˜๋Š” ๊ธฐ๋Šฅ์„ ๊ตฌ์„ฑํ•˜๋Š” ๋ฐ๋Š” ๋งŽ์€ ์–ด๋ ค์›€์ด ๋”ฐ๋ฅธ๋‹ค. ํ† ์„๋ฅ˜์˜ ๋ฐœ์ƒํ™•๋ฅ ์„ ์˜ˆ์ธกํ•˜๋Š” ๊ฒƒ๋„ ์ด๋Ÿฐ ๋ฌธ์ œ์— ํ•ด๋‹นํ•˜๋Š”๋ฐ, ํ† ์„๋ฅ˜์˜ ๋ฐœ์ƒํ™•๋ฅ ์„ ๋น ๋ฅด๊ณ  ์ •ํ™•ํ•˜๊ฒŒ ์˜ˆ์ธกํ•˜๊ธฐ ์œ„ํ•ด์„œ๋Š” ์‹ค์‹œ๊ฐ„์œผ๋กœ ๊ณ„์†ํ•ด์„œ ์—…๋ฐ์ดํŠธ ๋˜๋Š” ์ž๋ฃŒ์ธ ๊ฐ•์šฐ๋Ÿ‰์„ ์ฒ˜๋ฆฌํ•  ๋•Œ ๋ฐœ์ƒํ•˜๋Š” ์‹ค์‹œ๊ฐ„ ๋ฐ์ดํ„ฐ(stream data)์— ๋Œ€ํ•œ ์ฒ˜๋ฆฌ์™€ ์ œ์ผ ๊ฐ€๊นŒ์šด ๋‘ ์ง€์ ์„ ์ฐพ์„ ๋•Œ ๋ฐœ์ƒํ•˜๋Š” ๊ณต๊ฐ„์  ์งˆ์˜(spatial query)๋ฅผ ํ•œ ์‹œ์Šคํ…œ์„ ํ†ตํ•ด ํšจ์œจ์ ์œผ๋กœ ์ฒ˜๋ฆฌํ•ด์•ผ ํ•œ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์‹ค์‹œ๊ฐ„์œผ๋กœ ์—…๋ฐ์ดํŠธ ๋˜๋Š” ๋ฐ์ดํ„ฐ๋ฅผ ํšจ์œจ์ ์œผ๋กœ ์ฒ˜๋ฆฌํ•˜๊ธฐ ์œ„ํ•ด stream์„ ์ฒ˜๋ฆฌํ•˜๋Š” ์—ฌ๋Ÿฌ ์‹œ์Šคํ…œ์„ ๋น„๊ต, ํ‰๊ฐ€ ํ•œ ๋’ค ์ด์— ๋Œ€ํ•œ ๋‚ด์šฉ์„ ๋ฐ”ํƒ•์œผ๋กœ PostgreSQL์— stream์— ๋Œ€ํ•œ ์ฒ˜๋ฆฌ ๋Šฅ๋ ฅ์„ ๋”ํ•œ PipelineDB๋ฅผ ์ด์šฉํ•ด ์‹ค์‹œ๊ฐ„ ๋ฐ์ดํ„ฐ๋ฅผ ํšจ์œจ์ ์œผ๋กœ ๋‹ค๋ฃจ๊ณ , ์ œ์ผ ๊ฐ€๊นŒ์šด ๋‘ ์ง€์ ์„ ์ฐพ์„ ๋•Œ ๋ฐœ์ƒํ•˜๋Š” ๊ณต๊ฐ„์  ์งˆ์˜์— ๋Œ€ํ•ด์„œ๋Š” PostgreSQL์˜ extension์ธ PostGIS๋ฅผ ํ†ตํ•ด ํ•ด๊ฒฐํ•˜๊ณ  ์ด๋ ‡๊ฒŒ ๋งŒ๋“ค์–ด์ง„ ๋‚ด์šฉ์„ QGIS๋ฅผ ํ†ตํ•ด ์‚ฌ์šฉ์ž์—๊ฒŒ ์ „๋‹ฌํ•˜๊ณ  ์ž…๋ ฅ๋ฐ›๋Š” ์‹œ์Šคํ…œ์˜ ๊ตฌ์กฐ๋ฅผ ๊ตฌ์„ฑํ•˜์˜€๋‹ค. ๋˜ ์ด๋ ‡๊ฒŒ ๊ตฌ์„ฑํ•œ ์‹œ์Šคํ…œ์— ๋Œ€ํ•ด ์‹ค์ œ ํ† ์„๋ฅ˜์˜ ๋ฐœ์ƒํ™•๋ฅ ์„ ์˜ˆ์ธกํ•  ๋•Œ ์‚ฌ์šฉ๋˜๋Š” ์งˆ์˜๋ฌธ์„ ๋˜์ ธ ์„ฑ๋Šฅ์„ ์ธก์ •ํ•˜๊ณ  ์—ฌ๊ธฐ์„œ ์–ป์–ด์ง„ ๊ฒฐ๊ณผ ๊ฐ’์„ ํ† ๋Œ€๋กœ ์•„๋ฌด ๊ฒƒ๋„ ๋ณ€๊ฒฝํ•˜์ง€ ์•Š์€ ์ผ๋ฐ˜ DBMS๋ฅผ ์‚ฌ์šฉํ•œ ํ”„๋กœ๊ทธ๋žจ์— ๋น„ํ•ด ์–ผ๋งˆ๋‚˜ ์„ฑ๋Šฅ์ด ์ข‹์•„์กŒ๋Š”์ง€๋ฅผ ์‚ดํŽด๋ณด๋„๋ก ํ•˜๊ฒ ๋‹ค.์ œ1์žฅ ์„œ๋ก  1 1.1 ํ† ์„๋ฅ˜ ๋ฐœ์ƒ ์˜ˆ์ธก ์‹œ์Šคํ…œ ๊ตฌํ˜„์˜ ๋ฌธ์ œ ๋ฐ ๋‚œ๊ด€ 2 1.2 ๋ฐฐ๊ฒฝ์ง€์‹ 3 1.2.1 Spatial Data 3 1.2.2 Data Stream Management System(DSMS), Complex event processing(CEP) 3 1.3 ๊ด€๋ จ์—ฐ๊ตฌ 4 1.3.1 Geospatial Stream Query Processing using Microsoft SQL Server StreamInsight 4 1.3.2 Continuous Query Processing of Spatio-temporal Data Streams in PLACE 4 1.3.3 Spatio-Temporal Stream Processing in Microsoft StreamInsight 5 ์ œ2์žฅ ์‚ฌ์šฉํ•  Backend ํ”„๋กœ๊ทธ๋žจ์— ๋Œ€ํ•œ ๊ณ ์ฐฐ 6 2.1 DBMS ๊ธฐ๋ฐ˜, ํ˜น์€ ์ดˆ์ฐฝ๊ธฐ์˜ DSMS 6 2.1.1 TelegraphCQ 8 2.1.2 Aurora 9 2.1.3 STREAM 9 2.1.4 NiagaraCQ 10 2.1.5 PipelineDB 11 2.1.6 VoltDB 11 2.1.7 Microsoft StreamInsight 12 2.2 Recent CEP 13 2.2.1 Apache Storm 14 2.2.2 Apache Spark 15 2.2.3 IBM InfoSphere 16 2.2.4 Oracle CEP 17 2.2.5 TIBCO StreamBase 18 2.2.6 Esper 18 2.3 ์„ ํƒํ•œ ์‹œ์Šคํ…œ 19 ์ œ3์žฅ ๊ตฌ์กฐ 21 3.1 Frontend 21 3.2 Backend. 23 3.2.1 ์ตœ๊ทผ์ ‘ ๊ฐ•์šฐ๊ด€์ธก์†Œ ์„ค์ • 23 3.2.2 ์‹ค์‹œ๊ฐ„ ๊ฐ•์šฐ์ž๋ฃŒ์˜ ์ฒ˜๋ฆฌ 24 3.2.3 ์ฃผ์˜๋ณด, ๊ฒฝ๋ณด ๋ฐœ๋ น 24 ์ œ4์žฅ ์‹คํ—˜ ๊ฒฐ๊ณผ 26 4.1 ์‹œ์Šคํ…œ ์‚ฌ์–‘ ๋ฐ ์‹คํ—˜ ์„ค์ • 26 4.2 Stream Data์— ๋Œ€ํ•œ Query์™€ ๊ฒฐ๊ณผ 26 4.3 Spatial Data์— ๋Œ€ํ•ด Query์™€ ๊ฒฐ๊ณผ 31 ์ œ5์žฅ ๊ฒฐ๋ก  ๋ฐ ํ–ฅํ›„ ์—ฐ๊ตฌ 35 ์ฐธ๊ณ ๋ฌธํ—Œ 37 Abstract 39Maste
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